Agents that check their own work before they ship
The loop-and-verify pattern runs plan → act → verify → commit on every task. Mistakes are caught inside the loop, not downstream by a human.
Most AI systems respond and move on. Autonomous agents have to work like senior engineers: plan the change, make it, test the result, and only then commit. The loop-and-verify architecture is how that discipline is enforced at every step.
Inside the loop
- 01
Plan
The agent decomposes the user request into concrete sub-tasks, picks tools, and sketches the expected outcome so success is measurable.
- 02
Act
The agent executes each sub-task, calling APIs and writing state. Every tool call is captured with its inputs, outputs, and timing.
- 03
Verify & commit
Before the change is made permanent, the agent replays its plan against the new state, checks guardrails, and either commits or rolls back with a reason.
Why the loop matters
Self-correction
When verification fails, the agent refines its plan and retries — without pulling in a human for routine misses.
Deterministic evaluations
Every loop produces a reproducible trace you can replay offline, making regression testing practical.
Guardrail enforcement
Policy checks run at verify time, so destructive actions never reach production just because the plan looked confident.
Cheap retries
Failed verifications are free compared to failed deployments. Cost compounds in the right direction.