Why Agents Always Contradict Themselves
AI Engineer · youtube · 2026-07-20
Datadog's Diane Lin discusses why running the same agent twice can yield completely different answers, noting that such "failures" are usually not just randomness but occur in gray areas near decision boundaries.
Core takeaways:
- This inconsistency often doesn't mean the model is broken; rather, the task itself involves ambiguous policies and expert disagreements.
- Instead of treating instability as a bug, use "contradictions" as a signal to identify samples requiring human intervention.
- They recommend combining active learning, semantic memory (domain knowledge and business rules), and episodic memory (historical similar cases) to concentrate human review resources on the most valuable cases.
- The goal isn't simply fine-tuning the model, but establishing a continuous learning loop that aligns the agent more closely with the customer's actual decision-making processes.
She specifically noted that in high-stakes scenarios like cybersecurity, this mechanism can reduce the loss of trust caused by oscillating "malicious/benign" verdicts.
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