Continue from this implementation example into live AI market coverage.

Developers are increasingly managing autonomous AI agents that perform tasks such as code generation, security scanning, and operations monitoring. These agents autonomously execute high-level goals by breaking them down into sequential phases, selecting appropriate tools, and adapting dynamically to real-time feedback. Developers shift from writing detailed code to orchestrating and supervising these agents, ensuring reliability, security, and compliance through human-in-the-loop oversight. This approach is actively unfolding in engineering teams, requiring new skills in context engineering, retrieval-augmented generation, sandboxed execution, and continuous evaluation frameworks.