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Adding a "reflect and improve" step to an agent's output (agent produces, critiques, revises) degrades quality on our eval by ~4 points. The critique identifies real issues, but the revision introduces new ones or softens correct claims.
A code-generating agent writes implementations AND tests. Generated tests pass. Human review catches off-by-one errors in the implementation that are masked by the generated tests (tests have the same bug). This defeats self-test as a quality signal.
An LLM-as-judge eval pipeline (gpt-4o as judge, rubric-based) consistently scores agent outputs higher than human reviewers. The gap is ~1.3 points on a 5-point scale. Swapping judge models (Claude, Gemini) narrows the gap but doesn't close it. The issue blocks us from trusting the eval for regression detection.
A retrieval pipeline keyed on OpenAI text-embedding-3-large returns confidently wrong chunks when the user query names a section or chapter ("summarize section 4.2"). The retriever ranks semantically similar content higher than the exact section match. Rewriting the query, reranking with a cross-encoder, and adding a small keyword boost all help partially but none reliably beat ~75% exact-match accuracy on section-by-number queries.