the-producing-agent-should-not-be-sole-judge-of-its-output CLM-0166mechanismmechanism reasoningReviewed by AI
The agent that produced an artifact is a biased judge of it — it holds the context and the incentives that skew its assessment — so verification belongs with a deterministic sensor or a separate verifier that reports failures back rather than rewriting the output, and in a multi-agent system the verifier is the one component no agent may override.
Evidence for: No one should be judge in their own cause (holds), Two heads are better than one (narrows)
Capability: Fixing its own mistakes · Agentic
Applies when
- Effect on the capability:
- helps
- Needs:
- a second model with its own context
- Fails when:
- The verifier shares the producer's context and incentives, or any agent in the system can override it.
Sources
- Section XVII.C, stated as a principle with the reasoning but no measurement.
- The measured version of the same point on a single model: self-review without an external signal does not improve reasoning. Producer bias is the mechanism this catalog already holds for why.
- Frames sensors as the checks that catch what the agent's own judgment will not, and argues for self-correction loops that run before human eyes precisely because the agent cannot be trusted to grade itself.
Either way, bring evidence: a link, or what you saw.
Disagreeing is the most useful thing you can do here. Nothing on this page waits for a reviewer: the counts above move when evidence lands, and every source stays attached with who brought it.
Related claims
- In an agent's verify-and-fix loop, deterministic checks — tests, linters, schema validators — should be exhausted before any model-based judge is added, because they are free, fast and give the same verdict every time, while an LLM-as-judge costs tokens per run, returns non-deterministic verdicts, and so belongs only on properties no rule can express, and even then as an advisory signal until measured against human review.Fixing its own mistakes · reviewed by AI
- In LLM multi-agent systems that reflect after failure, restricting reflection to the single agent responsible for the first incorrect step — located by a trained step-level correctness scorer and paired with a counterfactually generated corrected step — raises task success more than having every agent reflect on the trajectory, because reflections written by agents that behaved correctly enter their memory as misleading guidance for later tasks.Getting more from several models than from one · reviewed by AI
- For LLM multi-agent systems failing on long-horizon web tasks, re-running the whole task from scratch — plain, with self-reflection, or with a critic agent — repairs few failures (at most 6.90% within three attempts, with feedback variants below plain rerun), while reconstructing the recorded execution prefix and intervening once at a symptom-flagged trace node repairs 20.15% with a single attempt.Getting more from several models than from one · reviewed by AI
- In multi-agent LLM systems where one agent starts with an erroneous shared belief, broadcasting every agent's full context at every step raises the rate of false assertions above doing no synchronization at all, because the error propagates to agents that were previously correct — and the harm appears only in tasks where one wrong fact cascades across semantically linked dimensions (destination to airport to weather), not where agent contexts are largely orthogonal.Stating false facts confidently · reviewed by AI
- When automatically rewriting the prompts of a multi-agent LLM system from its failures, locating the responsible agent by injecting a hint into one agent at a time and using that agent's hint-corrected intermediate output as the supervision signal produces prompt updates that generalize better than critique derived only from the system-level output.Improving its own scaffolding from failures · reviewed by AI
Notes
Supports external-feedback-repair-works-only-with-real-grounding from the multi-agent side. Filed separately because "who verifies" is a design decision in its own right, distinct from "does reflection help".