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What is the agent designed to accomplish? Define the core task and success criteria...
Answer users questions in natural language. It needs to be able to provide answers that are correct and based on the documents it has access to. It must cite its sources.
What level of autonomy does the agent have? What are the boundaries and constraints it must operate within?
It needs to be able to fetch documents and reason about those documents without human intervention.
Define the tools and actions available to the agent. Specify tool interfaces, input/output schemas, and how the agent selects which tool to use...
It has access to some kind of search feature and a way to rank responses.
Design the overall agent architecture. Choose an orchestration pattern (single agent, multi-agent, hierarchical). Identify key components and how they interact. Use the diagramming tool to illustrate the architecture.
How does the agent manage memory and context? Define short-term (conversation) and long-term (persistent) memory strategies. How is context window managed? What retrieval mechanisms are used (RAG, vector stores, summarization)?
Deep dive into the agent's reasoning strategy. How does it plan, decide, and self-correct? Discuss prompt engineering, chain-of-thought, reflection loops, failure handling, and tradeoffs.
What guardrails and safety mechanisms does the agent have? Define permission boundaries, human-in-the-loop checkpoints, content filtering, rate limiting, and how the agent handles adversarial inputs or unexpected states.