Building Reliable AI Agents

AI agents become useful when their architecture makes failure visible and recoverable.

Start with the task contract

Define inputs, allowed tools, success criteria, latency, budget and escalation paths.

Separate planning from execution

A planner can propose steps while an executor operates under tighter permissions. This makes evaluation and recovery easier.

Treat memory as data infrastructure

Memory needs relevance, expiry, privacy boundaries and evaluation—not just a vector database.

Build evaluation before autonomy

Use deterministic test cases, tool-call checks, groundedness checks and human review for high-impact actions.

Production checklist

• Trace every tool call
• Rate limit expensive actions
• Require approval for irreversible steps
• Measure task success, not token volume
• Keep fallbacks simple