ELE Times

New unmanned aircraft expand the Indian company’s portfolio across long-range surveillance and tactical unmanned combat aerial vehicle applications.

Indian autonomous aerial vehicle supplier ideaForge Technology has announced the launch of two variants of its ZOLT tactical UAV system- the ZOLT Hybrid and the ZOLT Unmanned Combat Air Vehicle (UCAV). The move signals ideaForge’s entry into the long-range (ISR) as well as tactical unmanned aerial system (UAS) markets. The existing ZOLT system is already thought to be in service with the Indian Army. The ZOLT Hybrid is reported to have endurance of more than six hours and a range of 100km, and is intended for long-range surveillance and persistent overwatch missions. Its hybrid architecture, combining vertical take off and landing and longer endurance, offers an advantage in areas lacking suitable runways or where they are too hard to create.

The ZOLT UCAV is intended for tactical operations and precision effects. Endurance is said to be 2-3 hours, with a launch and recovery range of 30- 50 km. Its use case is clearly different from a surveillance aircraft, with more focus on the tactical deployment and mission-specific payloads. While this introduction mirrors a broader transition in defence aviation to unmanned aircraft that operate in difficult terrain and minimise personnel exposure to immediate risk, operational performance of these aircraft will always be defined by payload mix, comms resilience, environmental conditions and mission parameters.

The development also underscores the increasing involvement of indigenous manufacturers providing specialised UAV system services to India’s armed forces. Procurement, as well as the operational evaluation and fielding of the systems, will influence the pace at which the new variants are further integrated.

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Indigenous lighter-than-air system reaches 21 km altitude during flight trial, supporting India’s high altitude surveillance ambitions.

India’s Defence Research and Development Organisation (DRDO) has successfully flight-tested a domestically developed High Altitude Platform (HAP) for long endurance surveillance and Communication capabilities. The Press Information Bureau of the Ministry of Defence issued a statement: “The experimental platform attained an altitude of 21 kilometres above mean sea level (AMSL) and hovered at 20 kilometres (AMSL) for over 30 minutes before being commanded to descend. The flight test system was recovered successfully, and post-flight performance data was analysed. The lighter-than-air platform, a potential long endurance, stratosp​heric surveillance system, is being developed by DRDO’s Aerial Delivery Research and Development Establishment (ADRDE), Agra.”

High-altitude platforms would enhance the satellite, aircraft, and existing UAS fleet by enabling constant monitoring of an area. These platforms might be used for border monitoring, intelligence collection, weather monitoring, communications support, etc. Its utility will be based on how long the platforms can stay in flight, how much equipment they can carry, and how well they can withstand the weather and stay where they are supposed to, which is called station-keeping. The trials required coordination with the Indian Air Force, CEMILAC, the Directorate General of Civil Aviation, the Airport Authority of India, and other organizations. “During the flight test, engineers at the ground control station collected real-time video as well as vehicle parameter data. The high-altitude platform payload carried an inertial measurement unit, a Global Positioning System (GPS) receiver, onboard cameras, an altitude-control system, and other electronic subsystems,” the statement mentioned.

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Global automation leader Emerson today announced new radio frequency (RF) test capabilities that help engineers characterize increasingly complex devices and scale automated measurement workflows. The new NI PXIe-5633 four-port vector network analyzer (VNA), together with synchronization-focused software enhancements for the NI PXIe-5860 vector signal transceiver (VST), expands RF validation capability to new multiple-input multiple-output (MIMO) and multiport S-parameter applications within the scalable, software-driven NI PXI platform.

New capabilities will enable engineers to:

• Expand RF validation capacity with four-port vector network analysis capabilities that address more complex device architectures and emerging multiport measurement requirements.
• Address next-generation wireless test challenges through enhanced NI PXIe-5860 synchronization capabilities that support advanced MIMO, multichannel and timing-sensitive RF applications.
• Consolidate measurements within a single system by combining network analysis and modulated RF measurements in one PXI architecture, reducing system complexity and simplifying test workflows.
• Build adaptable test systems for growth with a scalable, software-connected platform that extends automated measurement workflows from design validation through production.

“RF test is no longer only about validating individual components in isolation. As wireless systems become more distributed, software-defined and antenna-intensive, engineers need test architectures that evolve as quickly as the technologies they are validating,” said Kevin Schultz, NI chief technology officer with Emerson’s test and measurement business. “These capabilities reflect our commitment to helping engineers build adaptable test infrastructure that supports system-level validation and future wireless innovation.”

The NI PXIe-5633 extends NI’s existing vector network analysis capabilities to four ports, helping engineers characterize more complex RF devices. As wireless systems increasingly rely on multiple RF paths, synchronized radios and distributed architectures, engineers must characterize interactions across entire systems rather than individual components in isolation. NI PXIe-5860 software enhancements support synchronization-focused and MIMO workflows for increasingly complex wireless test requirements.

Engineers can also perform S-parameter characterization alongside modulated RF measurements through a single RF connection. Combining these measurements in one software-driven PXI system can reduce the need for separate instruments and test setups while maintaining a consistent path for automation.

The new capabilities are being featured in booth A40B at European Microwave Week in a 4×4 MIMO demonstration using the NI PXIe-5860 and the four-port VNA. Applications span semiconductor validation, advanced wireless research, aerospace and defense systems and other RF-intensive technologies increasingly built around multi-antenna, multichannel and software defined architectures.

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​AI is no longer just creating chatbots; now it can plan, use external tools, scrape data, and perform multi-step processes. Frameworks for creating AI agents are making this possible. AI agent frameworks are software suites for designing, prototyping, orchestrating, debugging, and deploying AI agents.

These frameworks mean that in 2026, you will be able to rapidly prototype new AI agents or create operational AI agents for complex workflows, multi-agent workflows, and enterprise-scale applications. Here are 10 popular frameworks and why to use them.

1. LangGraph

Created by LangChain, LangGraph is a framework for building stateful AI agents with granular control over execution. It incorporates a graph-based structure that manages new execution flows with conditional logic, persistence, human approval, and the ability to recover from interruption. It’s ideal for enterprise use and long-term agents.

2. CrewAI

CrewAI is a collaborative, role-based platform for managing multi-agent artificial intelligence. For multi-AI applications, coders can assign each agent its own role and then group them into teams to conduct research, analysis, content creation, or run a business. It has a very simple product design and user interface for fast prototyping.

3. OpenAI Agents SDK

The OpenAI Agents SDK allows you to construct agents that invoke functions, hand off tasks between agents, and utilize guardrails. It includes tracing, which allows you to observe agent behaviour and debug your application’s flow. A good choice for applications with tool use and multiple agents in collaboration.

4. Google Agent Development Kit (ADK)

The Google Agent Development Kit (ADK) can be used to develop, test, and deploy your AI agents- or multi-agent systems. The kit has a modular architecture, supports integrations with popular tools and coding languages (Python, TypeScript, Go, and Java), and may be useful for people building Gemini apps or using Google Cloud.

5. Microsoft Agent Framework

Microsoft Agent Framework offers the combined features of AutoGen and Semantic Kernel. It can implement single agents, multi-agent workflows, memory, tool integration, and human-in-the-loop execution. It supports Python and .NET​, the latter being useful for organisations building AI in the Microsoft ecosystem and on Azure.

6. LlamaIndex

LlamaIndex is a great fit for document-focused or enterprise knowledge agents. Its indexing and retrieval features, as well as data connectors, allow developers to create search functions over internal data or information, as well as generate context sensitive responses. It’s a good choice for RAG and document analysis tools or research assistants.

7. Mastra

Mastra is a TypeScript framework for creating AI agents and applications. It supplies tools to developers for creating agents, workflows, and integrations in the JavaScript/TypeScript ecosystem. This is mainly for teams creating AI based mostly web apps without a separate Python agent layer.

8. Pydantic AI

Type-safe application development for AI with structured data validation. Pydantic AI empowers developers to author and verify expected output, compose tools, and develop applications where the interface to the language model is more explicit and predictable than standard software. It is particularly relevant for Python teams looking for predictable data validation and maintainability.

9. Haystack

Haystack, a deep-set built tool, is a modular framework designed for developing AI applications with pipelines, retrieval, and agent components. Document search and processing, search, and RAG are all features of the tool. Developers can leverage search with tool use to develop knowledge-intensive chatbots and business information systems.

10. Claude Agent SDK

The Claude Agent SDK from Anthropic offers a set of primitives to build agents based on Anthropic’s Claude models. It provides support for tool-based workflows and integrations that might be useful for coding, file workflows, and research-based use cases. Review the model provider requirements and tool permissions before selecting this option.

How Should Developers Choose an AI Agent Framework?

The best framework for you will depend on your use case, programming language, provider, and operational needs: complex, stateful workflows might use LangGraph; CrewAI is a good choice for role-oriented, collaborative workflows; OpenAI Agents SDK provides an easy-to-implement path to tool-using agents; Google ADK is best if you’re in the Google Cloud world; for document-centric workflows, check out Llama Index and Haystack.

For TypeScript teams, Mastra; for anyone who loves typed Python, Pydantic AI; and for enterprise solutions, Microsoft Agent Framework. You’ll want to consider documentation, observability, security controls, deployment options, licensing, and ongoing costs. You don’t necessarily need an agent framework- a single model invocation or a traditional ETL-style workflow may be simpler to run.

Conclusion

Frameworks for AI agents are emerging as essential components of applications that require more than text generation. They help developers leverage AI capabilities into functional software by reducing the complexity of tool integration, orchestrating workflows, managing context across multiple agents, and orchestrating multi-agent systems. There’s no single best option. The best framework depends on the scale, complexity, technical stack, reliability requirements, and costs of your project.

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Rapid technological acceleration and shifting regulatory landscapes warrant robust governance, risk management, and workforce readiness, skill development, essential to build national cyber resilience. Anwesh Koley of ELE Times interacted with key leadership from ISACA, who shared strategic perspectives on India’s evolving cybersecurity posture, the impact of legislative initiatives and the pressing need to bridge the domain’s skills gap.

ELE Times: At the onset, could you brief us on what is currently happening at ISACA regarding cybersecurity, governance, risk, compliance, and related fields?

Team ISACA: This is a pivotal moment for cybersecurity in India. Over the last five years, the government has made multiple moves indicating a clear focus on increasing national cyber posture and resilience.

Crucial legislation like the Digital Personal Data Protection (DPDP) Act has been pivotal. Furthermore, traditional regulators such as the Reserve Bank of India (RBI) and the Securities and Exchange Board of India (SEBI)—particularly within financial services—have consistently remained at the forefront of cybersecurity enforcement. Across sectors, there is expanding interest in skill development, awareness, and building a deeper appreciation of what constitutes a robust cybersecurity program.

However, the ecosystem remains fragmented. A wide spectrum of policymakers and regulators operate with varying levels of understanding regarding how cyber risks impact their specific sectors. While entities like CERT-In and the National Cyber Security Coordinator serve as positive signals, the lack of a single, unifying national body to tie these initiatives together leads to disparate, localised pockets of progress rather than a fully unified national strategy.

On artificial intelligence, the implementation across sectors is similarly experiencing fragmented approaches. While there is currently no plan to introduce national AI legislation in India—keeping the focus intentionally on fostering innovation—the rapid pace of AI adoption makes AI security and enterprise governance frameworks critical.

Finally, addressing the skills gap remains essential. Building a well-qualified workforce requires continuous, innovative development initiatives to establish standard capabilities across all domains.

ELE Times: You raised the important issue of skill development. What specific avenues is ISACA focusing on, and where does India feature in your strategic plans regarding quality skill development?

Team ISACA: India is a critical jurisdiction for ISACA, and our organisation is making significant strategic investments here. India represents our largest concentration of staff outside North America. Beyond building local cybersecurity postures and AI capabilities, India aims to leverage cybersecurity and AI as economic growth levers to position itself as a regional technology leader. We actively support this vision alongside our local network of 10,000 members organised across 12 chapters.

There is an important opportunity for India to make the cybersecurity sector a national economic success story, mirroring its historical success in the software and IT services industry.

Skill development extends beyond pure-play cybersecurity specialists; there is surging demand for cybersecurity expertise in adjacent corporate roles—including procurement, legal, project management, and executive board governance. Beyond specialised operational roles, building a robust national talent pool enables the creation and export of domestic cybersecurity products and services.

To support these goals, ISACA delivers a multi-pronged approach:

• Extensive Credential Portfolio: Offering structured pathways from foundational competencies through to specialist credentials, including new certifications designed as entry points into cybersecurity careers.
• Enterprise Governance & Benchmarking Frameworks: Providing frameworks (such as COBIT) that allow enterprises to review, assess, and benchmark their maturity in adopting AI, IT controls, IT audit assurance, and governance.
• Continuous Learning & Local Framework Alignment: Moving away from static, one-time certifications toward mandatory, continuous professional education (CPE). Additionally, our credentials and workforce frameworks adapt to local regulatory requirements to ensure seamless regional implementation.

ELE Times: How do you assess the current Indian workforce and its preparedness toward accepting cybersecurity norms and building long-term careers in this domain across various industry verticals?

Team ISACA: Workforce preparedness varies across sectors. Industries like financial services have made substantial investments, and critical infrastructure sectors are actively advancing their capabilities. However, operational challenges remain, particularly within public sector capacity and municipal-level infrastructure where personnel manage multiple operational responsibilities.

A central challenge is the sheer speed at which the field evolves. Rather than replacing cybersecurity roles, technologies like AI are reshaping job requirements rapidly. Cybersecurity professionals must continually adapt to keep pace with emerging technology stacks.

Additionally, growth is accelerating in intersecting domains like risk management and regulatory compliance. Navigating this environment demands an agile mindset and a commitment to continuous learning to stay ahead of evolving threats and technological shifts.

ELE Times: Expanding beyond India, how do you see the broader South Asian and regional markets performing in terms of cybersecurity readiness and willingness to adopt scaling frameworks?

Team ISACA: India acts as a primary technology and policy leader across South Asia. While smaller regional jurisdictions can sometimes move faster due to scale, there is a widespread recognition across South Asia that robust cybersecurity adoption is mandatory to participate effectively in the global digital economy.

Beyond South Asia, regions like the Middle East—Saudi Arabia in particular—are moving rapidly to establish strong national cybersecurity profiles. India holds a strong position to offer thought leadership and drive regional dialogue across these markets.

Regarding the willingness to adopt cyber norms and frameworks across the region, industry resistance is rarely the barrier. Instead, organisations face challenges around regulatory proliferation and a lack of harmonisation.

When enterprise teams must navigate overlapping, unaligned compliance requirements from multiple authorities, compliance becomes burdensome. Diverting technical staff and financial resources purely toward administrative compliance exercises can take focus away from vital operational security tasks—such as active risk management, system patching, and continuous monitoring.

The industry is receptive to regulation, standards, and guidance. However, the priority must be regulatory coherence and harmonisation so that compliance directly drives meaningful improvements in an organisation’s overall security posture.

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Artificial intelligence workloads are redefining the power requirements of modern data centers. Increasing GPU performance and rising rack densities are driving a shift toward 800 VDC or ±400 VDC power supply architectures. To address these  requirements, Infineon Technologies AG is launching a 27kW three-phase power supply unit (PSU) solution for server ODMs and OEMs. The reference design meets the requirements of the OCP Open Rack V3 (ORV3) specification and AI server rack architectures, further expanding Infineon’s comprehensive portfolio for powering AI servers. The solution helps developers shorten time-to-market while improving rack power density, efficiency, and thermal performance.

The design combines Infineon components including 650V CoolSiC and CoolMOS MOSFETs, EiceDRIVER gate drivers, XENSIV current sensors, and PSOC microcontrollers. It achieves a peak efficiency of more than 98 percent at an input voltage of 480 VAC and 50 percent load. The reference design uses a five-level ANPC PFC topology and a three-level LLC converter, reaching a power density of 116 W/in³, compared with the ORV3 minimum requirement of 94 W/in³. An integrated energy buffer provides a hold-up time of 20 milliseconds during short power grid disturbances. It also helps smooth the line voltage during rapid GPU load changes, eliminating the need for a separate capacitor bank unit (CBU). An integrated planar magnetic structure enables a compact, modular, and scalable high-frequency transformer design.

The PFC and LLC stages are digitally controlled to respond to demanding AI load transients. The programmable power control accelerator (PPCA), integrated into the PSOC Control C3 Performance Line microcontroller, supports precise current and voltage regulation with fast dynamic response. The reference design supports a three-phase input voltage range of 311 to 528 VAC and is therefore suitable for a range of global grid standards. It achieves low input-current total harmonic distortion (iTHD) and a power factor above 0.99 across most of the load operating range. The design supports operation at ambient temperatures from -5°C to 45°C.

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DigiKey, the global distribution leader of electronic components and automation products, announces its agreement with TDK SensEI to serve as the first distributor for edgeRX Starter, a predictive maintenance solution designed to support improved industrial equipment performance.

TDK SensEI’s edgeRX Starter combines AI-driven multi-modal sensing with advanced analytics to enable predictive maintenance, transforming vibration and temperature data into early warning signals of developing issues. By continuously capturing changes in machine behavior, the system uses AI to identify patterns and anomalies that may signal developing equipment issues and delivers predictive insights with prescriptive guidance. This helps teams act early to prevent downtime, extend asset life, optimize performance and keep production lines moving.

“DigiKey is proud to be the first online electronic components distributor of the edgeRX Starter, especially since our team has been using it in our product distribution center ourselves,” said Jason Simoneau, vice president, passives and multi-market semiconductors, for DigiKey. “This cohesive solution’s predictive maintenance capabilities have benefited our team, and we look forward to offering our customers access to the same innovative system.”

TDK SensEI is addressing a critical gap in the market by delivering a solution for industrial businesses that often lack the resources or in-house expertise to implement advanced predictive maintenance (PdM) systems with AI.

“We’ve created edgeRX Starter to make predictive maintenance easier to adopt for industrial teams of all sizes,” said Sandeep Pandya, CEO of TDK SensEI. “With edgeRX Starter, we’re making predictive maintenance more accessible, helping teams transform uncertainty into confidence and reactive fixes into proactive decisions.”

DigiKey has more new product introductions (NPIs) on its shelves than any other electronics distributor, allowing customers to build the machines and devices that accelerate progress in industries such as healthcare, AI, energy, industrial automation, sustainability and IoT.

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India’s premier indigenous video security brand, PRAMA displayed its innovative transportation solutions at the 14 th Edition of Traffic Infra Tech Expo. Prama India had participated in the Traffic Infratech Expo held between 07-08 October at Bharat Mandapam in New Delhi. The PRAMA booth displayed the latest indigenous Transport Security Solutions. These products and solutions including Traffic Control Solution, Toll Plaza Surveillance, Facial Recognition Terminal, Advanced Traffic Management solution, FRT Solutions, Storage Products, UPS, Network & Transmission, ADAS, and PRAMA VMS solutions. PRAMA booth at the Traffic Infratech Expo displayed its indigenously manufactured transportation security products and solutions.

The two-day ITS India Congress 2026 was concurrently organized along with the Traffic Infratech Expo event, it covered eclectic mix of contemporary themes on transportation, traffic management, logistics, Mobility, Smart Cities Parking Management. In the ITS India Congress, Conference theme was ‘Connected, Integrated & Intelligent Mobility for Viksit Bharat 2047’. Prama India representative Prashant Hegde, Vertical Head- PPOG, Heavy Industries & Transportation, participated in a panel discussion on the theme ‘Citizen Centric Intelligent Transportation Systems for Viksit Bharat’. He said “PRAMA offers advanced solution for Indian Traffic Scenarios. PRAMA’s ATMS integrates traffic monitoring, AI analytics, incident detection, ANPR, speed enforcement, vehicle classification, driver behavior analytics, through a centralized management platform.”

Traffic Infratech Expo- 2026 provided a networking forum for government representatives, experts, project heads, system integrators and service providers to collectively find solutions to urban transport challenges. The exquisitely designed PRAMA booth got good response from the security professional community, transportation industry leaders, government representatives and delegates. The event got concluded with relevant insights on the latest developments in the India’s transport sector.

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Automotive safety has grown up in layers. Passive safety, things like seatbelts and airbags, reduces harm once a crash is already happening. Active safety, such as anti-lock braking and electronic stability control, steps in while you drive to help prevent the crash in the first place. The newest layer, predictive safety, uses driver assistance and automated driving to remove the mistake before it occurs. Electrification adds one more dimension, because the battery itself now needs surveillance.

Behind every one of these layers sits sensing: a small device measuring pressure, acceleration or angular rate and passing it to a control unit that decides what to do. This article looks at where that sensing is advancing across the vehicle, from the tire and crash detection to vehicle stability and the battery in an electric car.

Safety Starts at The Tire

Tires are easy to overlook, but an under-inflated tire handles worse, takes longer to stop and is more likely to fail at speed. On an electric car it also cuts range. That is why tire-pressure monitoring is mandated in many markets and has become one of the most widespread passive-safety features on the road.

The job is harder than it sounds. A sensor sits inside each tire, measuring pressure and temperature and sending the reading to the vehicle by radio. It has to run for years on a small battery, so it spends most of its life asleep, and an accelerometer wakes it once the wheel starts turning and helps the system work out which wheel it is fitted to.

ST’s NTM88 family covers this across vehicle types, from passenger cars and two-wheelers to light trucks, buses, heavy trucks and trailers, and even agricultural equipment, with pressure ranges up to around 1500 kPa. Each device packs the pressure sensor, an 8-bit microcontroller with a dedicated TPMS firmware library, a dual-axis accelerometer for wake-up and localization, and the radio interfaces into a 4 by 4 mm package. The sensors are AEC-Q100 qualified, rated to +125 °C, draw as little as 180 nA in sleep, and are in mass production.

Passive Safety: Airbags And Restraints

Passive safety is about limiting the harm once a crash can no longer be avoided, and that depends on recognizing the impact fast enough to act on it. Small inertial sensors placed around the vehicle sense the sharp deceleration of a collision and signal the airbag control unit to fire the airbags and tension the seatbelts at the right instant. The same kind of sensing supports pedestrian protection, where the system responds to an impact at the front of the car, and rollover detection, where it senses the vehicle beginning to tip. ST’s crash-detection inertial sensors, including the NXLS95322AES and NXLS95422AES, are built for these roles.

Active Safety Keeps the Car Stable

Active safety works while the car is moving. Anti-lock braking came first; electronic stability control (ESC) built on it and is now standard in many regions. ESC and traction control track how the vehicle is behaving, its rotation and acceleration, its wheel speeds and steering input, and if they sense the car starting to slide or a wheel losing grip, they brake individual wheels or trim engine torque to bring it back into line. Rollover prevention works the same way.

All of this depends on inertial sensing that stays accurate in a demanding chassis environment, close to heat and vibration, and that is dependable enough to sit inside a safety function. ST’s combo sensors family targets these vehicle-stability and traction-control roles, the kind of sensing that lets a stability system read the vehicle’s motion precisely enough to step in at the right moment.

A New Frontier: Protecting The EV Battery

Electrification brings a safety concern that combustion cars never had: thermal runaway, where a failing lithium-ion cell overheats and can set off nearby cells. One of the earliest physical warning signs is a change in pressure inside the battery pack, which can appear before the temperature climbs far. Detecting it early buys time to warn the driver or take protective action.

A battery pressure monitoring sensor watches for those abnormal changes. ST’s NBP8 family does the work at the edge. Each integrates an 8-bit microcontroller with firmware and user-selectable detection algorithms, a fixed threshold, a change in pressure, or a rate of change over time, so the sensor itself can flag an anomaly and let the rest of the system stay powered down until something happens. The parts cover an absolute pressure range of 40 to 250 kPa with accuracy within about 1.2%, are AEC-Q100 qualified and rated to +125 °C.

Toward Predictive Safety

Starting with the safety of the tire, the newest layer, predictive safety aims to prevent the mistake altogether, through driver assistance and automated driving. Part of that is active prevention: monitoring the driver, and combining that with data from the navigation, active-safety and tire systems so the car can respond to a developing risk early. It also relies on fusing several sensing types, cameras, radar, lidar, satellite positioning and inertial units, into a single picture of the car and its surroundings.

The same forward-looking approach reaches the powertrain, where a vibration sensor on an electric vehicle’s traction inverter can catch the early signs of wear and trigger predictive maintenance before a fault develops. It is a large field, but the underlying direction is the same as everywhere else: sensing that is more accurate, and closer to the source, makes the whole system safer.

A Common Thread

Across all these layers the pattern repeats. Safety keeps moving toward smaller, smarter sensors that measure a physical signal, whether pressure, acceleration or angular rate, and act on it early. Tire pressure, crash detection, vehicle stability and battery health are all current examples, and more will follow as vehicles electrify and automate.

Explore ST’s automotive MEMS and sensors portfolio to see the full range across these safety functions.

The post The Evolution of Automotive Safety and the Sensors Driving It appeared first on ELE Times.

As appliance and equipment manufacturers look for ways to reduce system costs, many are replacing displays with Human Machine Interfaces (HMIs) using high-end decoration and illuminated capacitive-touch buttons, sliders and wheels. These interfaces often require a large number of touch sensors which can increase acquisition time and reduce the signal-to-noise ratio (SNR) when using conventional controllers. To address these challenges, Microchip Technology has expanded its turnkey capacitive-touch family with the launch of MTCH3380P and MTCH3240P controllers, helping manufacturers create visually appealing, display-free HMIs at lower system cost and with less design complexity. The first in Microchip’s touch turnkey controller portfolio to incorporate parallel sensing for buttons, sliders and wheels, the devices support IEC/UL 60730 Class B functional safety certification and operation up to 105°C, making them well suited for home appliance, outdoor and industrial applications.

By acquiring data from multiple sensors simultaneously, the MTCH3380P and MTCH3240P can support a large number of buttons, sliders and wheels while maintaining fast response times, a high signal-to-noise ratio (SNR) and reliable touch detection in noisy operating environments. This performance is enabled by 12 on-chip analog-to-digital converters (ADCs) operating in parallel. The MTCH3380P supports up to 38 sensors in self-capacitance mode and 64 sensors in mutual capacitance mode, while the MTCH3240P supports up to 24 and 32 sensors, respectively, without the acquisition-time penalties often associated with high sensor counts.

Enhanced touch sensitivity and noise immunity enable reliable touch detection through thick cover materials and when users are wearing gloves. These capabilities are especially valuable in-home appliances such as ovens, cooktops, refrigerators and washing machines, where moisture and gloved operation are common, as well as in outdoor applications including EV chargers, parking meters and kiosks that require durable protective covers and dependable operation in cold-weather conditions.

“Manufacturers are increasingly looking for ways to reduce system cost while still delivering intuitive and visually appealing user interfaces. As display-free HMIs incorporate more touch controls, maintaining responsive and reliable touch performance becomes increasingly challenging,” said Giovanni Fontana, senior director of Microchip’s human machine interface division. “Our MTCH3380P and MTCH3240P controllers address the touch-sensing challenges associated with display-free interfaces by delivering fast touch acquisition and high signal-to-noise ratio, helping enable responsive, reliable operation even in demanding applications.”

With support for customer driven IEC 60730 Class B functional safety certification, the MTCH3380P and MTCH3240P devices help manufacturers work towards meeting regulatory requirements. Microchip’s touch-sensing ecosystem and turnkey controllers are designed to provide the hardware, software and development resources needed to accelerate design, evaluation and testing. Support includes the mTouch Studio development environment and evaluation hardware, including host drivers for Linux and Zephyr.

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AI is transitioning from answer-providing computer programs to the software we refer to as AI agents, which can choose a goal, reason, come up with a plan, access external resources, and act semi-autonomously. Whereas a chatbot is only activated when a user submits a question, an AI agent can autonomously choose a next step, collect data, communicate with software, and update its plan.

Google Cloud says that these AI agents use reasoning, planning, and memory to accomplish the specific goal on behalf of the user. Companies that leverage AI agents in various fields such as software development, cybersecurity, customer support, research, finance, IT operations, enterprise automation, and many others will be more efficient and innovative.

What Is an AI Agent?

An AI agent is an AI-powered system capable of perceiving, reasoning about the goal, planning, and executing the task that needs to be done by sensing the environment. Usually, an agent would use a large language model (LLM) as the reasoning brain. The LLM, however, isn’t enough on its own, and a real-world agent needs a model, plan orchestration, knowledge sources, tools, permissions, and monitoring.

For instance, rather than merely responding to a customer inquiry about an order, an agent can extract the order number, access the latest information from an enterprise system, verify the shipment, and relay the information back to the customer. If there’s additional work to do, such as generating a support ticket, the agent can employ the right business tool. Agentic AI has the capability to go from “answering” to “acting.”

How AI Agents Work

Most AI agents work in a loop of reasoning, planning, acting, and observing. The agent starts off with a goal and some context and reasons about the current state and what actions may be taken. The planning layer can decompose the goal into subgoals and plan a sequence of actions to achieve those subgoals.

It thinks about what tool/step/action to take, takes the action, watches the outcomes, interprets the data, then takes the next step. This cycle continues until either the goal is reached, the agent hits a terminal state, or makes the last step before human input. This paradigm is similar to the ReAct (reason + act) pattern, which interleaves reasoning and acting to allow external information to inform later inference. Google Cloud and AWS both detail agent architectures based on this iterative process.

Planning: Breaking Complex Goals into Steps

This enables an AI agent to pursue a multi-step goal rather than addressing every prompt as a singular question. Imagine a business requests an agent to research a network issue. The agent might want to review system logs, recognize abnormal activity, analyse recent configuration updates, review security alerts, and draft a recommendation.

Rather than doing it all at once, a planning layer can break the goal into sub-goals, or re-plan if a tool reveals something unforeseen. The latest generation of agent platforms are starting to include support for these kinds of workflows. For instance, Microsoft’s modern Agent Framework has notions of tools, sessions, persistent memory, multi-step workflows, and agents that plan, execute, and follow up on tasks, so planning becomes the task management layer of the agent.

Memory: Giving Agents Context and Continuity

Memory is the other significant aspect in which a simple AI app differs from more advanced agents. An agent usually requires some short-term working memory, which keeps the overall context of the current task, the conversational context, intermediate results, the tools’ results, and the current state of the workflow.

Long-term memory has another function. It can store relevant information for several sessions, like preferences, previous conversations, history of tasks performed, or important lessons. Google Cloud’s existing agent architecture also distinguishes between short-term memory and long-term knowledge and memory. Its Agent Platform includes a Memory Bank that can remember information on a personal level for several sessions.

OpenAI’s agent tooling already enables persistent memory between runs, so agents can hold on to reusable data from their previous work rather than just re-running an entire conversation. That said, memory introduced new engineering trade-offs. Companies have to think through issues of data correctness, privacy, storage, access, and the risk of using stale data.

Tools: How AI Agents Act

An AI model can produce text, but an agent requires tools to act in the real world. The set of tools an agent can use might consist of APIs, databases, search, enterprise applications, code execution environments, browsers, file systems, and business apps. With tool calling, an agent can access current information or perform actions that are beyond the capabilities of a model. For instance, a software-development agent could examine a code repository, run tests, analyse an error, and generate a recommended code update.

An accounts agent could access approved financial data and generate a report. Forthcoming enterprise IT architectures are also converging on standards for interoperability, including the Model Context Protocol (MCP), which AWS says enables discovery and interoperability with other tools. A similar architecture exists for accessing tools through the MAC. But since the range of tools that agents could use would be vast, access to tools must be carefully regulated; AWS suggests, for example, authorisation, input/output validation, access to a trusted tool registry, monitoring and human review for high-risk tasks.

Reasoning: The Decision-Making Engine

Reasoning relates to what an agent will do, given the information to hand. By knowing how reasoning-enabled models make decisions, we can compare alternatives, evaluate potential courses of action, consider constraints, and understand how tools may be of value.

But reasoning alone does not make an agent right. An agent may not be aligned to a goal, rely on false information, adopt the wrong tool, get caught in an inefficient loop, and much more. That’s why production-safe agent systems are combining model reasoning with grounding and evaluation, observability, policy controls, human oversight, and more. 2026 Agentic AI guidance at AWS is for dependable, secure, observable, cost-conscious, bounded autonomy in production.

AI Agents vs Traditional Automation

The most basic type of automation is the X then Y type. This is the beauty of AI. It can go further than that. It can understand what it’s targeting, determine how it needs to behave, and make alterations to the sequence in light of incoming data.

For instance: A traditional automated can order system would send an email informing of the delayed can order. A smarter AI agent would explore the cause of the delayed can order, investigate the system, find out that it is a customer who needs to be informed, write the message, and escalate the matter if it is outside the domain approved by the system. But this level of sophistication introduced even greater complexity and risks. Greater autonomy demands greater ownership over permission, diagnostics, and failover.

Where AI Agents Are Being Used

AI agents are being applied in nearly every enterprise application. In the customer-service domain, they can research queries, fetch account details, and carry out sanctioned actions. In the software engineering space, coding agents can explore repositories, author or update code, test, and debug programs. Cybersecurity agents can research alarms, relate data, and trigger incident-response workflows.

IT-operations agents can research fault domains and suggest or carry out resolution actions. Other areas include research, supply-chain management, financial analysis, healthcare management, and business-process automation. As an example, Google Cloud currently has an agent platform that offers agents tailored to enterprise workflows, discovery of information, and content creation.

The Security Challenge

These same properties that make AI agents helpful could also make them risky. An agent that has access to databases, APIs, or company software can perform harmful things if it’s not tightly controlled. Today’s agent security design therefore involves least-privilege access, identity and role management, use authorisation, input sanity checks, audit trails, speed restrictions, and human approval for high-impact tasks. AWS’s newest guidance advocates limited freedom and clear controls of agents’ activities, while its guidance on secure use of tools advocates policy-based authorisation and verification of tools before they’re used.

The Future of AI Agents

AI agents aren’t simply going to be standalone assistance, but multi-agent systems that can perform more and more complex workflows. A multi-agent architecture is naturally distributed across specialised agents, with some having separate roles (for research, analysis, and execution, for example). The architecture therefore isn’t simply LLM + chatbot. It’s a wider stack of models, reasoning, planning, memory, tools, orchestration, identity, security, and observability.

The real opportunity isn’t to give AI the ultimate in autonomy, but rather the right amount of autonomy for the task at hand. As enterprises consider how to bring agents from experiment to production, reliability, governance, and measurable business impact will be as critical as model intelligence.

Conclusion

AI agents are a new type of software agent that uses one or more types of artificial intelligence. An AI agent is made up of a mixture of reasoning, planning, memory, and tools, which makes the agent capable of reaching goals, responding to change, and completing long-term tasks.

The technology is still evolving, but the trend is clear: Future AI-beyond the ones and zeros of a cloud model will manifest as digital workers that will be able to understand objectives, coordinate activities, and operate within applications. Their success will rely on more than the intelligence of the model behind them; it will depend on the design of systems for safety and reliability.

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DigiKey, the global distribution leader of electronic components and automation products, is hosting a webinar on how to use the company’s time-saving My Lists parts management tool entitled “Empower your everyday work with My Lists,” scheduled for Tuesday, Oct. 13, 2026, at 10 a.m. CDT. The free 60-minute virtual event will cover My Lists’ comprehensive list management and quoting features that allow users to make informed purchasing decisions, collaborate with coworkers and lock in pricing.

Managing parts across multiple sources can create complexity and inefficiency. With DigiKey’s My Lists tool, users have access to a single, centralized hub for part information, pricing, supplier details and real-time inventory status. “As a high-service distributor, DigiKey is continually improving the customer experience, and My Lists is an important tool for enhancing the sourcing process,” said Jillian Switts, senior director for e-commerce at DigiKey. “Whether you are managing a bill of materials, evaluating alternatives, checking price and availability, or coordinating with suppliers, My Lists brings everything together in one organized platform to make the product procurement process smarter and easier.”

Beyond organization, My Lists offers users measurable business benefits. The online tool generates quotes directly from product lists and locks in pricing for up to 30 days, giving users budget certainty. With immediate access to critical data that eliminates manual searching, reduces errors and accelerates decision-making, users and sourcing teams can improve pricing visibility, increase procurement cycles and deliver strategic purchasing outcomes.

During the webinar, attendees will learn to:

• Select and save substitute and alternate parts
• Use the tool to collaborate with team members or share information publicly
• Generate quotes and lock in pricing quickly
• Identify cost-saving opportunities
• Streamline bill of material (BOM) management and procurement workflows

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DigiKey, the global distribution leader of electronic components and automation products, proudly announces the opening of its new Bengaluru office, home to DigiKey India’s Global Capability Center (GCC), supporting innovation, technology enablement and business operations for teams worldwide. The company held an inauguration ceremony on Oct. 5, 2026, at its new office at Sumadhura Capitol Towers, Whitefield, Bengaluru.

“Our new Bengaluru office strengthens DigiKey’s long-term growth strategy by providing capacity to attract top talent, support business expansion and foster greater collaboration and innovation among our global team,” said Shane Zutz, vice president of human resources for DigiKey. “It also reinforces our commitment to delivering value to our customers, suppliers and team members. We are thrilled to continue this commitment in India and invest in the exceptional engineering talent and innovation ecosystem this region brings to the world.”

The office move marks an important milestone in DigiKey India’s growth journey, providing a modern workspace that supports collaboration, innovation and future expansion. India plays an increasingly important role in DigiKey’s global operations, providing access to highly skilled talent and supporting the company’s long-term innovation and digital transformation initiatives.

Since launching as a five-person prototype team in 2022, DigiKey India has grown to nearly 300 employees, reflecting the company’s continued investment in the region. Today, the high-performing Global Capability Center (GCC) serves as a strategic hub for innovation, collaboration and technology enablement, supporting DigiKey’s global operations while driving impactful solutions for the future of electronics and automation distribution.

India continues to be an important growth market and innovation center for DigiKey, and the new Bengaluru office reflects the company’s long-term commitment to the region. The investment expands access to exceptional talent, supports business growth and enhances organizational efficiency while providing a strong foundation for scaling operations and strengthening collaboration and alignment across global teams.

Bengaluru is widely recognized as one of India’s leading technology and innovation hubs, making it a strategic location for DigiKey’s continued growth. The Bengaluru GCC continues to create opportunities for talented professionals who want to contribute to a global technology organization.

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Keysight Technologies, is bringing agentic AI to its software, enabling engineers to direct AI agents to automate complex design tasks. Customers can use their large language models (LLMs) to generate and optimize designs, with Keysight simulation validating the agents’ progress. This shifts how engineering is done, as teams can evaluate significantly more options in the same development time.

More than 60% of organizations expect to deploy AI agents by 2028, yet radio frequency (RF) engineering has been slower to follow. The discipline relies on specialist expertise and uses schematics and layouts that LLMs cannot read in a consistent, deterministic way, so the process remains largely manual. Keysight Advanced Design System (ADS) 2027 addresses this by letting teams record workflows as macros their agents can learn from and convert graphical designs into code they can read. Model Context Protocol (MCP) servers connect agents to the software, guiding LLMs and agents in their interaction with ADS.

When an engineer makes a request in natural language, their agent completes the task in ADS. The MCP servers provide the agent with documented skills and tools built by Keysight that execute specific RF work consistently, reducing variability caused by AI inference models generating answers statistically. The agent then runs Keysight simulation to validate its output.

Key benefits include:

• Creates an open ecosystem for agentic workflows: The MCP servers work with AI assistants and LLMs that organizations are already using, and with tools from multiple vendors in the same workflow.
• Speeds and expands design cycles: Agents handle repetitive setup and simulation steps in ADS, so engineers can cover more scenarios and find more optimal designs while reducing time to market.
• Shares engineering knowledge: Macros recorded in ADS capture an experienced engineer’s methods for colleagues and agents to reuse across teams.

Niels Faché, Senior Vice President, Keysight Design Engineering Software, said: “Agentic engineering is reshaping every stage of design from concept through verification. Our decades of simulation and domain expertise validate AI results before they reach hardware. Over time, agents will turn prior projects into organizational intelligence, so a customer’s best work is the foundation for each new design.”

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Murata Manufacturing Co., Ltd. is pleased to announce it will exhibit at CEATEC 2026, taking place from October 13th to October 16th, 2026, at Makuhari Messe, booth No. 7H340.

Advances in AI, robotics, and IoT are changing what is required from sensing technologies, creating new opportunities to detect, interpret, and act on physical conditions, material states, and biological signals with greater accuracy and efficiency.

Addressing these evolving requirements, under CEATEC 2026’s theme ‘Transformation’, Murata will showcase its latest technologies alongside innovative solutions developed in collaboration with partners. Together, the exhibits will highlight new approaches to sensing, monitoring, and detection that can help support a more connected, sustainable, and prosperous society of the future.

Visitors are invited to explore these technologies firsthand, with key exhibits at the Murata booth including:

Ultrasound Transmission Metamaterial

Murata’s ultrasound transmission material enables ultrasonic detection through materials such as metal and resin, which are normally difficult for ultrasound to penetrate. Applied to these obstructing materials, the metamaterial increases ultrasound transmission, extending ultrasonic sensing to applications where internal conditions have traditionally been difficult to assess, including nondestructive testing, flow meters and biomedical applications.

Wearable Device for Capturing Trace Biomarkers

A wearable technology patch under development that captures substances such as glucose and alcohol. Designed for advanced wellness-related applications, its proprietary enzyme-based technology captures target substances　and is being developed with the aim of improving detection in low-concentration ranges.

RFID Sensor Tag

A battery-free, wireless tag capable of sensing temperature, humidity, and strain. The technology enables real-time monitoring of conditions that have traditionally been difficult to assess, including bolt loosening and changes in concrete condition, and is designed to improve inspection and maintenance efficiency.

Neuromorphic Sensing System

Inspired by the information-processing mechanisms of biological nervous systems and sensory organs, this technology enables large-area, high-density sensing with ultra-low power consumption and low latency. Supporting applications like electronic skin for humanoid robots and equipment temperature monitoring, the system provides real-time detection of temperature distributions across a large surface area.

Obstacle detection for AMRs equipped with thermophones

This solution equips an autonomous mobile robot (AMR) with a thermophone, a device that generates ultrasound using heat, to detect nearby obstacles. In current development testing, the technology can measure distances from 1 cm to 2 m and can detect transparent objects such as glass, which can be difficult for optical sensors such as cameras to identify. It can also operate in high-glare environments, supporting obstacle detection for AMR applications.

 

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Renesas Electronics Corporation, a premier supplier of advanced semiconductor solutions, has expanded its GaN portfolio into low-voltage applications with its first family of 100V enhancement mode (E-mode) GaN-based discrete power transistors. The RTP100E005G1FL, RTP100E2P6G1FL, RTP100E1P8G1FL-DSC and RTP100E1P2G1FL-DSC low-voltage GaN FETs deliver ultra-fast switching speeds and enhanced thermal performance in efficiency-critical, high power density applications, including AI data centers, humanoid robotics, factory automation and industrial motor drives, power tools and solar microinverters.

The new GaN FETs achieve industry-leading hard- and soft-switching figure-of-merit (FOM) performance, delivering up to 35% lower hard-switching FOM and up to 63% lower soft-switching FOM than comparable GaN devices. The low-voltage GaN devices also maintain a silicon-compatible footprint that allows for easy adoption into existing designs.

Power converters in today’s data centers generally operate at switching frequencies of a few hundred kilohertz. But as AI servers and industrial infrastructure transition to 800 VDC power distribution and 48V bus architectures, many converter stages are moving toward megahertz-class switching to shrink magnetics, increase power density and improve overall efficiency.

Using low-voltage GaN in 800V high-voltage direct current (HVDC) power architectures simplifies power conversion design, significantly reducing passive component size, switching losses and cooling requirements. At the system level, this translates to better efficiency, fewer thermal management requirements and lower energy and BOM costs.

Broad GaN Portfolio Yields Greater Efficiency, Power Density and Design Flexibility

Built on Renesas’ recently expanded low-voltage E-mode GaN technology platform, this new transistor portfolio features ultra fast GaN switching with low total gate charge (Qg) and output charge (Qoss) to minimize the overlap between voltage and current. It reduces energy lost in each conversion cycle across AI power supply units, motor drives and DC-DC power stages. The devices also offer zero reverse recovery (Qrr = 0), which eliminates energy losses for immediate switching efficiency gains.

Fast switching increases power density and yields higher frequency operation by shrinking PCB footprint and reducing the size of magnetic and passive components, while low RDS(on) improves operating efficiency by curtailing conduction losses. Available bottom- and dual-side cooling configurations provide added heat dissipation and design flexibility. Depending on application requirements and power-conversion architecture, the low-voltage GaN devices can achieve up to 40–70% lower switching losses and up to twice the power density at the system level.

The new GaN transistors are offered in multiple standard MOSFET-compatible packages, allowing customers to quickly migrate from silicon MOSFET layouts for faster design implementation without significant PCB rework. The package options, combined with a wide RDS(on) range (5 mΩ-1.2 mΩ), enable designers to scale across power levels and applications, including synchronous rectification, multiphase buck conversion and motor drives.

“Customers adopting next-generation GaN technology for AI servers, robotics, industrial motor drives and renewable energy systems are looking for ways to deliver more efficient power conversion performance from increasingly compact systems,” said Akhil Nair, Senior Director, Low-Voltage GaN at Renesas. “Our low-voltage GaN family delivers the efficiency, switching performance and power density designers expect from GaN while making it significantly easier to transition from existing silicon MOSFET designs.”

High GaN Performance in Silicon-Compatible Footprints

The new GaN family is built on E-mode (normally off) GaN technology, which offers the performance advantages of GaN in a convenient silicon-compatible footprint. This helps designers capture the efficiency and power benefits of GaN and simplifies migration from existing silicon-based designs.

Key features of the new low-voltage GaN family

• 1 to 3% higher efficiency over silicon-based designs, eliminating Qrr loss
• Drastic 40 to 70% reduction in switching losses
• Doubled power density by reducing switching energy per cycle and supporting high frequency operation
• Smaller system footprint with higher switching frequency reducing magnetics size and lowering overall system losses
• Optimized thermal management reduces fan and active cooling requirements, cutting system complexity and BOM cost

Additionally, with its lower total energy consumption per power stage, the new GaN family helps designers meet sustainability objectives. This is achieved by reducing the size of fans required for thermal and airflow management, enabling the use of smaller magnetics and shrinking overall PCB footprint, which contributes to AI data center and industrial energy efficiency targets.

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How leading enterprise AI platforms are helping organisations build, deploy, govern, and scale autonomous AI agents

Moving from chatbot and content generation to AI agents that think and plan, use tools, may incorporate enterprise data, and can take multiple actions. Generating several actions, generating enterprise data, and performing multiple actions. By 2026, enterprises will increasingly consider agent platforms for model quality, security, governance, integration, observability, and the ability to deploy AI endeavours at scale from proof of concept to production.

Here are 10 leading AI agent platforms enterprises should consider.

1. Microsoft Copilot Studio

Microsoft Copilot Studio enables customers to build their own generative AI agents with knowledge of your business, workflows, connectors, REST APIs, and MCP (Model Context Protocol) servers. These can be deployed to Microsoft Teams, websites, and enterprise apps, as well as communicate with each other. As a close Microsoft 365 and Power Platform partner, it targets organisations committed to the Microsoft ecosystem.

2. Google Gemini Enterprise Agent Platform

Google Cloud’s Gemini Enterprise Agent Platform, previously part of the agent capabilities of Vertex AI, is a complete platform for developing, deploying, governing, and refining enterprise AI agents. It includes the Gemini models as well as models from open source via Model Garden, Agent Development Kit (ADK), evaluation tools, and enterprise data grounding.

3. Amazon Bedrock AgentCore

AWS is growing the agent ecosystem by launching Amazon Bedrock Agent Core, a runtime for creating and managing agents with runtime, identity, access control, policy, memory, tool interface, evaluation, and observability. AWS has expanded Agent Core in the Asia Pacific (Hyderabad) region in August 2026. AWS has also announced Bedrock Managed Agents-powered by OpenAI in preview in September 2026.

4. Salesforce Agentforce

Salesforce Agentforce is for enterprise organisations looking to inject AI agent capabilities directly into their customer and business workflows. Agentforce 360, which provides the foundation for the Agentforce product, integrates agents with Salesforce data, metadata, applications and business logic. It fuses adaptive AI with deterministic controls and has security and observability functionality for enterprise deployments.

5. ServiceNow AI Platform

ServiceNow defines both an enterprise AI platform and the bundling of its advanced machine learning (AML) features called Otto as autonomous enterprise work. Its cloud platform automates integrations of AI agents with workflows, systems of record, business rules, and enterprise processes in IT, HR, security, finance, procurement, customer service, and others. ServiceNow’s managed execution strategy may attract enterprises that have made an investment in ServiceNow workflow automation.

6. IBM watsonx Orchestrate

IBM watsonx Orchestrate is about orchestrating enterprise AI agents across frameworks and environments. The 2026 Agentic Control Plane includes a centralised management, governance, and visibility system, agent management, agent cache, and scheduling. The system embraces the new open environment where agent builders from different teams and technologies can be combined.

7. Oracle AI Agent Studio

Oracle AI Agent Studio enables your organisation to design, assemble, test, and deploy AI agents and multi-agent orchestration workflows across your Oracle Fusion Cloud Applications. 2026 release of Oracle AI Agent Studio provides a new no-code and pro-code experience so you can author specialised agent teams to collaborate on the Fusion business objects, workflows, approvals, policies, and audit controls.

8. OpenAI Agents Platform

The 2026 agent stack comprised the Agent API, Agents SDK, Responses API, and Chat Kit. The agent’s API was a managed runtime for long-lived tasks; the agent’s SDK was an environment that enabled developers to manage tools, orchestration, handoffs, state, and guardrails within their applications. It’s why enterprise organisations focused on building customised agentic apps on top of OpenAI models: it was the perfect platform.

9. Databricks Agent Bricks

Agent Bricks and Mosaic AI Agent Framework from Databricks can help organizations build agents using the company’s enterprise data. It offers a single control plane for model, provider, and framework agents along with data governance, lineage, access controls, and cost management. This offering might be especially useful for companies with an AI strategy directly linked to their data platform.

10. Workato Agent Studio

Create enterprise Agents (Genies) with Workato Agent Studio. Workato Agent Studio is a no-code/low-code AI Agent (Genie) creation and management platform. It unifies Enterprise Applications, APIs, Workflows, MCPE Servers, Identify, approvals, observability, and governance into the Agents. The integration-first architecture enables the automation of sophisticated cross application enterprise workflows.

Choosing the Right Enterprise AI Agent Platform

The best AI agent platform for your organisation depends on the specific requirements of your organisation. For those already using their ecosystem extensively, Microsoft and Salesforce are good options. For others, AWS, Google Cloud and Databricks deliver a lot of the required infrastructure with lots of developer flexibility. For workflows focused on automation, ServiceNow, Oracle, and Workato are hard to beat, while IBM and OpenAI give a flexible platform for agent orchestration and app creation.

As AI agents grow more autonomous, companies will be able to evaluate the security, IAM, data management, human approval, observability, and auditability, model agility, integrations, and TCO of all aspects against model performance. Those that have platforms that unify autonomous execution and enterprise controls will be positioned to accelerate the automation revolution.

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Infineon Technologies AG has introduced the TRAVEOTM CYT4EN, a new microcontroller (MCU) for cost-effective high-performance instrument clusters and display applications in the automotive industry, including two-wheelers. The automotive cluster MCU is designed to leverage external LPDDR4 memory, resulting in capabilities that traditionally required more complex SoC (System-on-chip)-based platforms: The device supports advanced 2.5D graphics and 3D scene rendering, drives high-resolution displays up to Full HD, and can power two displays simultaneously. At the same time, it reduces system complexity and the bill of materials compared to SoC-based platforms.

“Cutting-edge cluster and display solutions are playing a central role for the user experience in connected and software-defined vehicles,” said Thomas Boehm, Senior Vice President Automotive Microcontrollers at Infineon. “With TRAVEO T2G CYT4EN, we are helping our customers bring them to market more quickly, efficiently and cost-effectively. By combining the simplicity, safety, and fast responsiveness of an MCU architecture with the high-bandwidth memory required for modern cockpit graphics, we are enabling a new generation of vehicle displays.”

While developing CYT4EN, Infineon collaborated closely with Micron to optimize the memory subsystem for the use of LPDDR4 memory to support advanced graphic capabilities while maintaining overall system efficiency.

“Micron’s automotive LPDDR4 memory provides a proven combination of bandwidth, reliability, and functional safety features for cost-optimized cockpit platforms,” said Amanda Henderson, Director of Ecosystem Enablement at Micron. “For vehicle programs where affordability, longevity, and functional safety are key requirements, LPDDR4 remains an effective solution for delivering modern display experiences.”

Built on the proven TRAVEO T2G architecture, the CYT4EN combines high-performance processing, advanced graphics, and automotive-grade safety and security in a highly integrated device. It includes features such as on-the-fly rendering and hardware-accelerated decompression to enable a rich graphical user experience. The device delivers deterministic boot times below 150 milliseconds that are significantly shorter than the multi-second initialization common to complex SoC platforms. It supports functional safety up to ASIL-B according to ISO 26262 and was developed in accordance with ISO 21434 for automotive cybersecurity. In addition, an independent voltage domain keeps central system functions such as communication and basic processing active in low-power modes, improving system efficiency and robustness.

The high level of integration enables cost-effective smart cockpit designs without complex system implementations. For example, CYT4EN allows designs without additional microcontrollers for system management and in-vehicle network communication, enables simplified PCB layouts with as few as six layers, and avoids the need for active cooling thanks to optimized power efficiency.

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Keysight Technologies has collaborated with the University of Glasgow, the National Physical Laboratory (NPL), and MPI Corporation to develop a new approach to broadband characterization of next-generation sub-terahertz (sub-THz) semiconductor devices. Together, the organizations demonstrated continuous on-wafer characterization of indium phosphide high-electron-mobility transistors (InP HEMTs) from near DC to 250 GHz in a single sweep, simplifying broadband measurements for advanced semiconductor research and design.

As semiconductor technologies move into millimeter-wave and sub-terahertz frequencies for next-generation applications, engineers need to accurately characterize transistor performance across increasingly broad frequency ranges. Conventional approaches can require multiple measurement setups and calibrations, adding complexity and making it more difficult to obtain consistent data for broadband device modeling.

The four organizations combined Keysight instrumentation, University of Glasgow semiconductor devices, MPI Corporation on wafer probing technology, and NPL metrology expertise to create the broadband measurement solution. The setup brought together:

• Keysight’s PNA-X Vector Network Analyzer
• Keysight’s Single-Sweep 250 GHz Frequency Extender
• Keysight’s Precision Source/Measure Unit (SMU)
• University of Glasgow’s InP HEMT devices and on-wafer calibration standards
• MPI Corporation’s broadband on-wafer probing technology and calibration software
• NPL’s high-frequency metrology and calibration methodologies

Together, these technologies enabled continuous measurement from near DC to 250 GHz with a single probe touchdown, eliminating the need for multiple setup changes and providing consistent broadband S-parameter measurements. Built-in source filtering and broadband source power calibration also helped ensure signal purity of the applied millimeter-wave signal at the probe-tip.

Thierry Locquette, VP of Sales, Europe, Middle East & Africa at Keysight said: “Characterizing devices continuously from near DC to 250 GHz in a single sweep can significantly simplify the measurement process for researchers developing next generation semiconductor technologies. By bringing together expertise in instrumentation, devices, probing, and metrology, this collaboration demonstrates a practical approach to generating the consistent broadband data engineers need to accelerate device development.”

Dr Xiaobang Shang, Principal Scientist and On-wafer Measurement Lead at NPL, said: “Accurate on-wafer measurement is essential for developing reliable semiconductor devices, particularly as technologies move further into the millimetre-wave and sub-terahertz frequency ranges. We were pleased to contribute NPL’s high-frequency on-wafer metrology and calibration expertise to this collaboration, helping demonstrate a practical route to consistent broadband device measurements using the state-of-the-art single sweep system.”

Matthew White, Director of Business Development at MPI Corporation, said: “Extending on-wafer characterization to 250 GHz requires the probing, calibration, and measurement platform operating as one seamlessly integrated system. This collaboration demonstrates a practical approach to achieving consistent broadband measurements from near DC to 250 GHz with a single probe touchdown, helping researchers simplify characterization and accelerate device development with great confidence.”

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Infineon Technologies today announced the completion of its acquisition of C2i Semiconductors, a Bengaluru-based technology company specialising in software-defined multiphase controllers and smart power stages for AI data centre applications. C2i Semiconductors’ technology complements Infineon’s leading portfolio of power semiconductors and power systems, enabling intelligent and scalable power delivery architectures from grid to core for AI servers and high-performance computing platforms. The acquisition further strengthens Infineon’s leadership in power solutions for AI data centres, expands its engineering capabilities in India and reinforces the country’s role as a strategic innovation hub. With the completion of the transaction, the C2i Semiconductors team becomes part of Infineon’s Power Systems division.

“Completing this acquisition marks an important milestone for our AI power business and our innovation activities in India,” said Adam White, President of Infineon’s Power Systems division. “C2i Semiconductors brings exceptional expertise in software-defined power management and system-level power architectures, backed by a highly experienced engineering team. By combining these capabilities with Infineon’s semiconductor technologies, application know-how, manufacturing capabilities and global customer reach, we will accelerate innovation in power delivery solutions for AI data centres and create significant value for our customers. I am delighted to welcome the C2i team to Infineon.”

Software-defined power solutions combine advanced power semiconductors with intelligent digital control and software algorithms to optimise power conversion, regulation and system performance in real time. As increasingly powerful AI processors create highly dynamic and rapidly changing power demands, power delivery systems must respond faster and more precisely to sudden load fluctuations while maintaining efficiency and system stability. By adding intelligence to the power delivery architecture, software-defined solutions can help improve efficiency, reduce power losses and support the increasing power density requirements of next-generation AI processors.

The acquisition combines C2i Semiconductors’ digital power expertise with Infineon’s global scale, application know-how and broad portfolio of power semiconductors, including silicon, silicon carbide (SiC) and gallium nitride (GaN) technologies, as well as vertical power delivery solutions. Together, the teams will accelerate the development of more intelligent, efficient and scalable power delivery solutions for AI infrastructure, including future Substrate Integrated Voltage Regulators (SIVR).

C2i Semiconductors adds expertise in software-defined power management, multiphase controllers, smart power stages, digital control technologies and system-level power architectures. The combination strengthens Infineon’s capabilities across the entire power path from grid to core and supports the development of next-generation power solutions for AI servers and high-performance computing platforms.

The acquisition also adds highly specialised engineering expertise to Infineon’s global R&D network and further strengthens its innovation capabilities in India. The combined team will contribute to reinforcing Bengaluru’s role as an important innovation location for digital power technologies and to accelerating the development of next-generation power solutions for AI infrastructure. Infineon currently employs approximately 2,800 people across multiple locations in India.

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