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The loop-and-verify pattern can run plan → act → verify → commit, with verification and policy gates defined and tested for each workflow before actions are allowed to commit.
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.
The agent decomposes the user request into concrete sub-tasks, picks tools, and sketches the expected outcome so success is measurable.
The agent executes each sub-task, calling APIs and writing state. Every tool call is captured with its inputs, outputs, and timing.
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.
When verification fails, the agent refines its plan and retries — without pulling in a human for routine misses.
Every loop produces a reproducible trace you can replay offline, making regression testing practical.
Policy checks can run at verify time; destructive actions require tested gates and appropriate human approval before production.
Failed verifications are free compared to failed deployments. Cost compounds in the right direction.