{ "schema_version": 1, "builder_plan": { "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.", "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.", "baseline": "Your current evaluator and a manually reviewed reference set.", "steps": [ "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.", "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.", "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.", "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test." ], "measure": "Agreement with human review, missed failures, false alarms, and evaluation cost per task.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2610.03315", "title": "Lightweight, Rubric-Guided Trajectory Evaluation for Production AI Agents", "summary": "LiteTrajEval replaces full-trace LLM evaluation pipelines such as AgentRx with offline-derived rule profiles, heuristic failure marking, budget-bounded trace serialization, and a single rubric-guided judge call. It costs an offline profiling step and an online preprocessing layer; the claims are measured against public Magentic-One-style and tau-bench-style datasets, so gains on the reader's own agents are unverified. Heuristic failure marking can drop diagnostically relevant context, and the single-judge design inherits LLM-judge bias and rubric sensitivity, so localization quality may degrade on failure modes absent from the rule profiles.", "rationale": "It runs through a single API-level judge call, fits a small team and limited budget, and directly addresses a production need the reader likely already has, so adoption is warranted with validation on the reader's own trajectory logs.", "technique": "Rubric-guided LLM judge over compressed agent trajectories", "family": "avaliacao_de_agente", "recommendation": "adotar", "published": "2026-10-02", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2610.03315", "independent_implementations": 1, "total_implementations": 2, "stars": 131, "citations": null, "claims": [ { "claim_id": "claim-01", "statement": "LiteTrajEval replaces full-trace LLM evaluation pipelines such as AgentRx with offline-derived rule profiles, heuristic failure marking, budget-bounded trace serialization, and a single rubric-guided judge call. It costs an offline profiling step and an online preprocessing layer; the claims are measured against public Magentic-One-style and tau-bench-style datasets, so gains on the reader's own agents are unverified. Heuristic failure marking can drop diagnostically relevant context, and the single-judge design inherits LLM-judge bias and rubric sensitivity, so localization quality may degrade on failure modes absent from the rule profiles.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.03315", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "Reduces evaluation cost by about 6x and evaluation time by more than 8x versus AgentRx, while improving failure-localization alignment with human annotations by roughly 20-35 percentage points on Magentic-One-style and up to 23 percentage points on tau-retail trajectories.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.03315", "source_page": null, "source_excerpt": "" } ], "exposure_map": [ { "dimension": "quality", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compute", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "latency", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "operations", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compatibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "security", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "data_and_training", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "reproducibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" } ], "risks": [], "minimum_test": [], "open_questions": [], "independent_tests": [], "lab_reproductions": [], "report_available": false, "validation_tier": "unknown", "evidence_tier": "unknown", "infrastructure_basis": "unknown", "equations": [], "equations_status": "not_fetched", "core_kind": "", "equations_fetched_at": "", "equations_source_url": "", "equations_source_sha256": "", "exposures_reviewed_at": "", "exposures_source_sha256": "" }