Architecting Reliable AI Agent Systems
Architect reliable agents with bounded goals, governed context, constrained tools, identity, evaluation, observability, and recovery.
Bound the agent
Define its goal, allowed tools, data scope, spending or action limits, prohibited actions, termination conditions, and escalation route. A broad natural-language objective is not an adequate control boundary.
Separate reasoning from authority
An agent may recommend a consequential action without being authorized to execute it. Use identity, policy enforcement, approvals, and least-privilege credentials at the tool layer rather than trusting instructions alone.
Evaluate the complete system
Test representative tasks, adversarial inputs, tool failures, stale context, and multi-step drift. Monitor decisions, actions, costs, exceptions, and outcomes, with replayable traces for investigation.
Design graceful failure
Provide timeouts, retries with limits, idempotent actions, checkpoints, rollback where possible, and a clear handoff to people. Reliability includes recognizing when the system should stop.
Common Mistakes
- Relying on prompts for authorization
- Testing only successful paths
- Deploying without an accountable operator
Market Signals
- Agent permissions exceed task requirements
- Only final text outputs are logged
- Failures leave partial actions without recovery