Building a Self-Improving Agent with Llama Stack
What if your AI agent could improve itself? Most agent tutorials show a single loop — user asks a question, the agent calls some tools, returns an answer. But what happens when you need to systematically improve your agent's behavior over time?
In this post, we build a ResearchAgent that answers questions from an internal engineering knowledge base — and gets better at it automatically. The agent uses the Responses API agentic loop with file_search and client-side tools to research questions, and it owns its own system prompt. Every N calls, it benchmarks itself by using a different model to judge the results, and rewrites its own prompt via the Prompts API.
This is literally self-referential: a Llama Stack agent evaluating and improving itself using the Responses API, Prompts API, and Vector Stores as its toolkit.