Feature: Edge AI


Figure 1: Continuous planning and feedback loops enable agentic AI systems to operate reliably within the fixed constraints of embedded and edge platforms


[Image is adapted from Sudarshan Poudel’s ‘How AI Agents Think, Plan and Act – Let’s Build One with LangChain’]


energy-constrained platforms. Engineers must decide how much autonomy can be supported locally, what level of optimisation is required, and whether workloads should be distributed across heterogeneous processors or offloaded selectively. Latency and determinism introduce further challenges. In


many embedded applications, decision making must occur within bounded time windows. Planning and reasoning loops that vary in execution time can undermine predictability if not carefully managed. Agentic systems therefore must be designed so that autonomous behaviour coexists with real-time control requirements, oſten through architectural separation between high-level reasoning and low-latency execution paths. Safety and reliability impose additional constraints. As systems


gain autonomy, they must also demonstrate predictable behaviour under fault conditions. Embedded agentic systems require clear boundaries on what autonomy is permitted, how failures are detected and how systems transition into safe states. Tese considerations tie agentic AI closely to established safety engineering practices rather than treating it as an isolated soſtware concern.


Architectural considerations for agentic systems Successful implementations of agentic AI typically separate perception, reasoning, planning and execution into modular components, allowing complexity to be managed and constrained where necessary. Perception modules ingest and pre-process sensor data, whilst


state representations aggregate this information into a form suitable for reasoning. Planning components generate action sequences aligned with system objectives, and execution layers translate plans into concrete actions whilst enforcing timing and safety constraints. Tis modularity allows agentic behaviour to be


introduced incrementally and tested independently. Many systems also use hierarchical control structures, where


high-level goals guide mid-level planning, and low-level controllers enforce deterministic behaviour. Tis hierarchy makes it possible to introduce autonomy without compromising stability or responsiveness, particularly in systems where control loops must meet strict timing requirements. Feedback loops play a central role here; see Figure 1. In


embedded deployments, feedback mechanisms must operate reliably despite intermittent connectivity, noisy sensors or partial observability. Designing these loops is as much a systems engineering task as an AI one. Deploying agentic AI is typically incremental. Teams begin


by enhancing perception and contextual awareness before extending decision logic and planning under clearly-defined safety boundaries. Introducing autonomy in stages allows behaviour to be validated and refined without destabilising the wider system. Translating architectural intent into a working system then


requires alignment between soſtware design and hardware capability. Decisions about reasoning depth, memory allocation, timing behaviour and power consumption must reflect the realities of the target platform, particularly in embedded and edge environments where constraints are fixed. Troughout this process, a strong ecosystem can reduce implementation risk and technical uncertainty. As a leading semiconductor distributor, Avnet Silica works with


engineering teams to evaluate platform trade-offs, align workloads with appropriate hardware, and address lifecycle and availability considerations. Tis hardware-aware design guidance helps developers move from concept to deployable systems with greater confidence in long-term stability and scalability.


www.electronicsworld.co.uk July/August 2026 19


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44