How to Build an AI Agent: A Practical Architecture Guide
A step-by-step guide to designing planner-executor AI agents — tool use, memory, guardrails, evaluation, and what changes when you move from prototype to production.
Building an AI agent is not prompt engineering with extra steps. It is software engineering with a non-deterministic core. This guide walks through the architecture I use for planner-executor agents in production: a typed goal contract, a tool registry with clear schemas, a planning model that emits structured intents, an executor that calls tools and writes to short-term memory, and a reflection step that decides whether to continue, hand off, or escalate. We cover memory tiers (scratchpad, episodic, long-term retrieval), tool design conventions, and the guardrail stack — system prompt rules, a policy engine that vets every tool call, and human-in-the-loop checkpoints for irreversible actions. The second half is operations: tracing every step so failures are debuggable, evaluation harnesses that score task success and tool-call correctness, cost controls, and the rollout pattern (shadow, assisted, autonomous) that builds trust before turning the agent loose.
This is a working note — expect iteration as the field and our practice evolve. If you have feedback, counterexamples, or want to compare implementation notes, reach out.
Key takeaways
- Start with a goal contract: inputs, allowed tools, success criteria, and stop conditions.
- The agent loop is plan → act → observe → reflect — make each step inspectable.
- Tool design is the agent's API surface. Idempotent, typed, and small beats clever.
- Guardrails belong at three layers: prompt, policy engine, and human-in-the-loop.
- You can't ship what you can't evaluate — build the eval harness before the agent.