Deploying an AI model on edge hardware involves much more than generating application code. The model needs to be compressed or quantized to fit available memory, compiled for a specific processor, integrated with a runtime, and tested for latency, power consumption, and accuracy. Developers must also make the software work with the surrounding embedded components and validate its behavior on physical hardware. These stages are handled through separate development tools and manual steps. Agentic AI connects much of that workflow. Rather than only generating the code, a development agent works with compilers, test frameworks, profilers, debuggers, documentation, and other engineering tools to determine the next action. This shortens the development cycle that previously required repeated manual work. But embedded systems create a difficult boundary for this level of automation. The deeper an agent moves into deployment, optimization, and hardware integration, the more it has to understand the target system to verify and trust results. Agentic AI automates the edge AI development stack with intelligent workflows that speed up building, deploying, and managing models at the edge. (Source: Adobe Stock) Agentic AI connects fragmented workflows Typical embedded software development is divided into many different tools. For instance, an engineer starts with understanding the requirements, moves into a model or source code, configures a build system, compiles the software, creates test cases, searches documentation, inspects logs, and returns to the code for diagnosis. Edge AI adds additional steps to this model, including quantization, graph optimization, runtime selection, and deployment to the target process. Agentic AI reduces manual switching between different tasks. The agent can work toward an engineering objective by choosing a sequence of actions, invoking available toolchains, checking the output, and deciding what to do next. Vinesh Sukumar , who leads the AI and machine-learning product management team at Qualcomm Technologies Inc. , spoke with Embedded.com about the current edge AI development workflow. He said that much of this development process is handled step by step, from quantization and compilation to runtime selection. Qualcomm AI Hub provides developers with tools for moving generative AI models onto Qualcomm platforms. Developers can use it to prepare models from open-source repositories for supported devices and evaluate performance on the target hardware. Qualcomm also offers AI Runtime, which uses optimized libraries and operators to execute models across its compute engines. Sukumar said the company is working toward an agentic layer that can automate more of the quantization, compilation, runtime selection, and deployment process. Qualcomm AI Hub helps developers prepare, optimize, and evaluate AI models on supported hardware. (Source: Qualcomm Technologies Inc.) An agentic workflow could take instructions such as precompiling a model for a target device, optimizing it for performance, and then coordinating the software tools required to search for that result. Rohini Ramegowda , a firmware engineer at Torus Inc. who works on safety-critical embedded and real-time systems, told Embedded.com that she's already using a narrower version of this workflow in firmware development. Ramegowda uses an AI agent that reads the requirements she has prepared, employs a MathWorks MATLAB MCP server to build a MATLAB and Simulink model, generates unit tests from an existing template, and runs the simulation. She manually reviews the resulting model against the requirements. Previously, she had to identify suitable Simulink blocks herself, search MathWorks documentation when necessary, and add them manually. "That used to take weeks," she said. "Now it generates it in hours. But the review part remains the same." The ability to work across artifacts also helps with diagnosis. In one case, Ramegowda gave an agent a configur