What is an AI agent loop?
An agent loop repeatedly observes the current state, chooses an action, uses a tool or model, validates the result, and continues until it reaches an explicit success or stop condition.
Instrument 03 / agent reliability
Your agent may be fine. Is its loop controlled?
Ten checks expose the missing guardrail between a clever demo and a production agent that can stop, recover, and explain itself.
Inspect one production path
Check what contains the loop.
Run this against a concrete workflow, not the entire agent platform. Different tools and risk levels need different controls.
Control model
0–3: blind.
The loop can act without a reliable stop or recovery path.
4–7: bounded.
Some controls exist, but incident behavior is still ambiguous.
8–10: controlled.
The loop is observable, testable, and operationally contained.
Production guide
What controls a production AI agent loop?
A useful loop observes state, acts, validates the result, and stops for a known reason. Production readiness comes from deterministic limits and evidence operators can inspect—not from the agent declaring itself finished.
Read the full four-test AI evaluation guide →How do you stop an AI agent loop?
Pair machine-checkable success criteria with hard caps on attempts, wall time, tokens, or spend. Add cancellation, classified failure paths, and a human escalation contract for work the loop cannot finish safely.
What should be logged in an AI agent loop?
Retain inputs, state changes, model decisions, tool calls and outputs, validation results, retry reasons, costs, and the final stop reason. The record should let an operator reconstruct why the loop continued or ended.
Production evidence
Get the failure mode behind the agent demo.
ResearchAudio traces the primary sources, constraints, and missing operational evidence in AI launches.