Skip to main content

agentops-report

Read, regenerate, and explain AgentOps release-gate reports. Trigger on "show report", "explain scores", "regenerate report", "what do these metrics mean", "where is the proof". Operates on results.json and report.md produced by `agentops eval run`.

معلومات المصدر

المستودع
Azure/agentops
آخر نشاط في المصدر
٤ يونيو ٢٠٢٦ في ١٨:٥٣
لغة SKILL.md المكتشفة
الإنجليزية
النجوم
١٣
التفرعات
١١

خيارات التثبيت

يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.

مراجعة ملفات المصدر

اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.

عرض SKILL.md

SKILL.md
تعليمات المصدر · معاينة للقراءة فقط
name
agentops-report
description
Read, regenerate, and explain AgentOps release-gate reports. Trigger on "show report", "explain scores", "regenerate report", "what do these metrics mean", "where is the proof". Operates on results.json and report.md produced by `agentops eval run`.
# AgentOps Report Help the user understand a finished AgentOps run and the evidence it provides for the release decision. Reports explain the repo-side gate; they do not replace Foundry Evaluations, Traces, or Monitor drilldown. ## Step 0 - Locate the run Latest run: `.agentops/results/latest/`. Each run produces: - `results.json` - machine-readable metrics, per-row scores, thresholds. - `report.md` - human-readable summary suitable for PR comments. - `cloud_evaluation.json` (when Foundry visibility is enabled) - deep-link to the Foundry Evaluations panel. `mode: classic` when `execution: local` and `publish: true` upload metrics to Classic Foundry; `mode: cloud` when `execution: cloud` runs server-side via the OpenAI Evals API. ## Step 1 - Regenerate report.md if needed ```bash agentops report generate # uses .agentops/results/latest/results.json agentops report generate --in <results.json> --out <report.md> ``` `report generate` always reads the flat 1.0 results schema and emits Markdown. There is no HTML format. ## Step 2 - Explain the metrics Common metrics and their meaning: | Metric | Range | Higher is better? | Notes | |---|---|---|---| | `similarity` | 1-5 | yes | LLM-judged similarity to `expected`. | | `coherence` | 1-5 | yes | Answer is internally consistent. | | `fluency` | 1-5 | yes | Natural language quality. | | `groundedness` | 1-5 | yes | Answer is supported by `context` (RAG). | | `relevance` | 1-5 | yes | Answer is on-topic for `input`. | | `f1_score` | 0-1 | yes | Token overlap with `expected`. | | `tool_call_accuracy` | 0-1 | yes | Predicted tool calls match `tool_calls`. | | `intent_resolution` | 0-1 | yes | User intent was resolved. | | `task_completion` | 0-1 | yes | Multi-step task finished. | | `avg_latency_seconds` | seconds | no | Wall-clock latency per row. | Pass/fail rows are derived from `thresholds:` in `agentops.yaml`. The exit code of the original run reflects the gate: - `0` → all thresholds passed - `2` → one or more thresholds failed - `1` → runtime error ## Step 3 - Help the user act on results - For low scores on a specific metric, point at the lowest-scoring rows in `results.json` (`row_metrics[]` and `item_evaluations[]`) and suggest concrete prompt or retrieval changes. - For latency regressions, look at `run_metrics.avg_latency_seconds` and per-row latency. - To compare a new run against a previous one, re-run with `agentops eval run --baseline <previous-results.json>` and explain the generated **Comparison vs Baseline** section. - For production evidence, open `.agentops/release/latest/evidence.md`. If it includes a `governance` section, explain ASSERT/ACS/red-team entries as read-only proof references (path, hash, status, ACS checkpoint coverage), not as proof that AgentOps executed those frameworks. ## Guardrails - Never invent metric values. If a metric is absent, say so. - Do not edit `results.json` by hand - re-run the eval. - Do not paste raw red-team payloads into the response; summarize only metadata and reviewer/status information.
عرض على GitHub