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Should agent evaluation measure execution quality beyond task success?
A broader line of inquiry — a family of 72 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 72
Specific inquiring lines the field asks around this — ordered from the most general framing down to the most specific angle.
- Should agent evaluation include trajectory quality beyond final success?
- Should agent evaluation include trajectory quality and memory hygiene alongside task success?
- Which interaction artifacts matter most for reliable agent evaluation?
- Do agent behavior logs reveal hidden accuracy costs benchmarks miss?
- Can trajectory analysis replace one-shot task success as the primary evaluation metric?
- How do execution traces reveal error propagation in multi-step agent decisions?
- How do fixed external benchmarks anchor self-improving agent systems?
- How do trajectory quality and memory hygiene affect agent evaluation?
- Should feedback channels be excluded from the reward path in agent evaluations?
- Do trajectory quality metrics predict agent safety and user trust?
- What trajectory-level metrics matter beyond one-shot task success?
- Can we build reusable evidence that a run stayed within bounds?
- What dimensions beyond task success matter for evaluating long-horizon agent trajectories?
- Can measures of application actions reveal changes in coordination that output metrics miss?
- What dimensions should trajectory-level scoring capture beyond final correctness?
- Does shared experimental state alone explain progress or is an analyzer needed?
- Why does infrastructure-side evidence matter more than agent-reported traces?
- How do agent actions change state that reward procedures later read?
- What makes a trajectory score interpretable across different interactive benchmarks?
- What makes recorded transitions more trustworthy than agent reasoning trajectories?
- What hidden signals in agent logs reveal about frontier capability beyond pass-fail outcomes?
- Which reset, logging, and feedback channels do agents actually exploit in benchmarks?
- Can task success alone reveal whether memory routing is working?
- How do agent privacy compliance and task success differ in evaluation?
- Why do sparse outcome rewards fail to credit correct tool calls in failed trajectories?
- Why does persistence in the feedback loop predict agent success better than initial solution quality?
- Can trajectory consistency predict real-world deployment failures?
- Can an agent change reward-path state through actions during evaluation?
- How do execution trajectories become valid training examples after validation?
- How do we measure progress without confusing it with task completion?
- How do agent accuracy and error recovery affect delegation time?
- What does agent security look like when measured across interaction trajectories?
- How can decision quality be automatically extracted from agent trajectories?
- What makes an agent mechanism reusable versus benchmark-specific?
- What infrastructure and reporting standards would make interactive evaluation reproducible?
- Can trajectory structure alone reveal process quality without human annotation?
- Why do scalar evaluation scores collapse distinguishable agent behaviors?
- Can accuracy rankings be reordered by tracking agent behavior costs?
- What trajectory-level metrics replace one-shot task success measurement?
- Can tool-call advantage attribution distinguish between correct and incorrect calls in mixed trajectories?
- What makes exploration and reflection rewards verifiable in agentic environments?
- What within-run behavioral dimensions reveal where long-horizon agents succeed or fail?
- Why do current evaluation metrics fail to catch reasoning failures in persona agents?
- What governance and safety measurements matter for deployed agent environments?
- How can operators ground benchmark completion claims in infrastructure data?
- Can trajectory-level visibility separate refusals from real skill gaps?
- What properties of agent systems only become visible across multiple sessions?
- Does decision-making taste predict end-to-end task success independently?
- Why do checkpoints get evaluated more often than actual improvements are retained?
- Why does moving the reward target prevent saturation better than finding a better static proxy?
- How do trajectory quality and memory hygiene differ as evaluation metrics?
- What counts as research completeness versus correctness in agent evaluation?
- How do execution traces and tests represent agent environment state?
- What role does runtime feedback play in agent verification and progress confirmation?
- Why does held-out evaluation matter for detecting agent overfitting?
- How visible is the optional shortcut to the agent during task execution?
- What evidence should benchmark operators attach to completion claims?
- What would a benchmark for successful LLM agent retrofitting look like?
- Do execution trace reports change how developers review agent output?
- How does credit assignment across objectives differ from credit assignment across time?
- How do you verify agent code under incomplete feedback signals?
- What makes trajectory more actionable than absolute scores for human moderators?
- How do capability tracks and behavior tracks stay separable during skill deployment?
- What validates whether a rewritten agent is actually better?
- What information do next-state signals contain beyond what scalar rewards capture?
- How should response effectiveness be measured when common causes and agent-side changes are both possible?
- Can a world model cause coherence if no control run without it exists for comparison?
- Does replay fidelity hold when policies explore branches history never visited?
- Does endpoint-only scoring hide meaningful progress like the Judgment Bypass Rate found?
- What cognitive capabilities do agents need to internalize social feedback?
- How do agent capabilities change across 25 relay rounds of interaction?
- What does trajectory audit reveal about evolution cycle contributions and costs?