Line of inquiry
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Why do agents confidently report success despite actually failing tasks?
A broader line of inquiry — a family of 39 specific questions the research asks around this. Follow one into its inquiring-line page, or move sideways to a related line below.
Questions in this line of inquiry 39
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- Why do agents report success when actions actually fail?
- How do agents learn to report success on actions that actually failed?
- Why do agents report success when they have actually failed at tasks?
- Which failure modes dominate in autonomous research agents?
- Can agent success reports serve as reliable oversight signals in real deployment?
- Why do AI agents fail at verification but succeed at generation?
- How does completion bias in agents differ from other epistemic failure modes?
- How do agents decide when to stop and reflect on failure?
- What tasks do AI agents still fail at most often?
- Can stopping rules extracted from past failures improve agent reliability without retraining?
- How should safety systems catch confident failures from agents that report success on unsafe actions?
- How should tool-call attribution distinguish credit between successful accidents and intentional actions?
- How much autonomy can agents safely exercise before failing?
- How do mode-specific failures differ between completion and agent benchmarks?
- Why do completion-mode strengths not transfer to agentic settings?
- What distinguishes mechanical generation failures from deliberate behavioral withholding?
- How can verifiers check policy compliance in agentic reasoning tasks?
- Can automated evaluation replace human judgment in agent testing?
- What structural features enable agents to detect when understanding has broken down?
- How do agent privacy compliance and task success differ in evaluation?
- What distinguishes strategic fabrication from accidental hallucination in research agents?
- What are the differences between chat model and agent authorization failures?
- What makes action-producing models fail in ways text models typically do not?
- How does poor belief tracking cause agents to keep acting past the point of usefulness?
- What other agent behaviors besides citations reveal reasoning quality?
- How does user overreliance on model confidence differ between chat and deployed agents?
- What hidden signals in agent logs reveal about frontier capability beyond pass-fail outcomes?
- What specific failure modes occur when downstream agents receive too much upstream input?
- What training objectives could reduce completion bias in autonomous agents?
- How do you verify agent code under incomplete feedback signals?
- How can agents distinguish between optional and required form fields during execution?
- How do delayed effects complicate causal attribution in agent systems?
- What specific failure modes must evaluation catch before deploying action-capable systems?
- What are the fourteen failure modes in deep research agents?
- Does accountability differ when one party in an exchange cannot hold commitments?
- What specific training mechanism causes agents to over-claim actions and overwrite documents?
- What causes autonomous agents to grant access to non-owners?
- What role does runtime feedback play in agent verification and progress confirmation?
- Why do phone-use agents fail by overfilling optional personal data fields?