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Annotating Agent Trajectories

How to annotate AI agent trajectories step by step, error taxonomies, severity scoring, and trajectory-level success, using Potato's trajectory evaluation.

A trajectory is the full sequence of steps an agent took, its thoughts, tool calls, and observations. Annotating a trajectory means judging the run as a whole and marking where individual steps went wrong, with a category and a severity for each error. Potato annotates agent trajectories step by step with custom rubrics, free and self-hosted, producing the data behind reward models and targeted debugging.

For the feature reference, see Agentic Annotation.

What do you collect when annotating a trajectory?

  • Overall outcome: success, partial success, or failure.
  • Per-step judgments: for each step, was it correct, unnecessary, or wrong?
  • Error categories: why a step was wrong (wrong tool, bad arguments, hallucination, looping, unsafe action…).
  • Severity: how bad each error was, often weighted into a score.

How do I set up trajectory evaluation in Potato?

Potato's trajectory_eval type renders each step as a card and attaches a per-step error taxonomy with severity weights:

yaml
annotation_schemes:
  - annotation_type: trajectory_eval
    name: step_evaluation
    description: "Evaluate each step for correctness and mark any errors."
    steps_key: steps
    error_types:
      - {name: reasoning,  subtypes: [logical_error, factual_error, planning_error]}
      - {name: execution,  subtypes: [wrong_tool, wrong_args, api_error]}
      - {name: safety,     subtypes: [harmful_action, data_leak, scope_violation]}
    severities:
      - {name: minor,    weight: -1}
      - {name: major,    weight: -5}
      - {name: critical, weight: -10}
    show_score: true

The severity weights roll up into a trajectory score, so you can rank runs and track regressions across model versions.

How do I design an agent error taxonomy?

The taxonomy is the heart of the task. Keep it small, exhaustive, and mutually exclusive. A practical starting set:

  • Reasoning errors: wrong conclusion, ignored evidence, bad plan.
  • Execution errors: wrong tool, malformed call, mishandled result.
  • Safety errors: unsafe action, out-of-scope behavior, data exposure.

Add a free-text "other" so annotators aren't forced to misfile novel failures, then promote recurring "other" notes into named categories.

Quality considerations

  • Agreement on step correctness is usually high; agreement on error category is lower. Measure both, see Inter-Annotator Agreement.
  • Long trajectories are fatiguing; cap length or paginate.
  • The "first wrong step" is often what matters most for training, see Process Reward Models.

Further reading