Agent Trajectory Evaluation

As automations hand control to LLM agents, “did it still work?” becomes “did the agent take an acceptable path?”. evaluate_trajectory() scores a recorded run against a declarative rubric, giving a deterministic, dependency-free signal for agent regression testing.

A trajectory is the ordered list of steps a run took — each a dict with at least an "action" name and optionally "args" / "observation". The rubric is plain data (so it lives happily in a JSON action file or arrives over MCP):

Rubric key	Meaning
required_actions	Actions that must all appear.
ordered	With the above, also require that relative order.
forbidden_actions	Actions that must never appear.
max_steps	Upper bound on trajectory length.
success_contains	Substring that must appear in some observation.

Headless API

from je_auto_control import evaluate_trajectory

trajectory = [
    {"action": "AC_focus_window", "observation": "focused"},
    {"action": "AC_type_text", "observation": "typed"},
    {"action": "AC_click_mouse", "observation": "Saved successfully"},
]
result = evaluate_trajectory(trajectory, {
    "required_actions": ["AC_type_text", "AC_click_mouse"],
    "forbidden_actions": ["AC_kill_process"],
    "max_steps": 10,
    "success_contains": "Saved",
})
assert result["passed"]            # every applicable check passed
print(result["score"], result["checks"])

score is the fraction of applicable checks that passed; passed is true only when all pass; an empty rubric trivially passes. Each entry in checks is {name, passed, detail} so a failure pinpoints the violated expectation.

Executor command

AC_evaluate_trajectory takes trajectory and rubric (each a JSON string from the visual builder, or already-decoded data from a JSON action file / MCP) and returns {passed, score, steps, checks}. The same operation is exposed as the MCP tool ac_evaluate_trajectory and as a Script Builder command under Agent.