ai-agent-design

Designing production-grade AI agents requires a structured approach to architecture, tool use, memory, orchestration, and safety guardrails. You must define component boundaries, validate inputs/outputs, and implement predictable escalation to ensure reliable operation.

Structuring tool calling schemas involves specifying structured tool contracts and applying validation principles for tool result handling. This prevents incorrect or unsafe actions by ensuring the agent validates inputs and outputs before executing function calls.

Memory systems handle context persistence by categorizing data into short-term, long-term, episodic, and semantic memory. Retrieval strategies like RAG and hybrid approaches are used to persist relevant context and inform future agent actions.

Multi-agent orchestration should be used when a workflow requires coordinating distinct roles through supervisor or routing patterns. A single-agent pattern using ReAct or plan-and-execute is better for self-contained tasks with simpler orchestration needs.

AI agents handle error recovery through structured recovery flows like retry, fallback, and escalate mechanisms. By establishing input, output, and action guardrails, the agent can safely respond to uncertainty or tool failures with predictable behavior.

An autonomy progression model is needed to scope autonomy from reactive behaviors to an autonomous orchestrator. It helps you design the agent architecture accordingly by defining clear component boundaries and matching capabilities to workflow requirements.