The short answer

A multi-step task can fail after several locally correct actions. Reliability depends on preserving state, recognising mistakes and recovering, not simply producing a convincing final response.

The question and scope

This article is an AI-assisted research-design draft for editorial review, not a report of a completed experiment. It asks how to evaluate ai agent reliability in a documented setting. No measurements, participants or client outcomes are claimed here.

An illustrative test case

The first tool writes a valid intermediate record, the second reads a stale copy and the third completes using it. A final success flag can hide the point where workflow state diverged.

This scenario defines a case to examine; it is not an observed result.

A proposed investigation

Construct workflows of increasing length with verified intermediate states. Add recoverable tool errors and compare a baseline agent with explicit checkpoints. Review the first divergence rather than only the final success flag.

Record setup versions, inputs and outcomes in a run ledger. Use repeated trials and matched comparison cases; preserve failed runs as well as successful ones.

What to measure

  • Completion rate by length.
  • first-error step.
  • recovery rate.
  • unnecessary actions.

Retain raw observations and explain exclusions. Human grading needs a documented rubric; acceptance thresholds need a stated rationale.

Interpretation and limitations

Different task lengths may differ in difficulty. Build matched variants and repeat runs before inferring a length effect.

Distinguish a plausible mechanism from a demonstrated effect. Preserve uncertainty when the design cannot eliminate alternative explanations.

A practical review checklist

  • Is the question narrow enough for the available system or dataset?
  • Are the comparison conditions genuinely comparable?
  • Can a reader trace each conclusion to an observation?
  • Are the boundaries and unresolved assumptions visible?
  • Does the decision change if the result is negative or inconclusive?

The publication artifact

The useful deliverable is a reproducible protocol, a measurement table and a decision note explaining what the evidence would change. Add actual results only after the investigation has been conducted and reviewed. This blueprint does not establish a novel finding or a provider-wide claim.

Sources and further reading

These primary resources are starting points for the concepts and controls involved. They do not validate the proposed experiment or its unmeasured outcomes. Check version-specific documentation when implementing the protocol.

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