対象ユーザー
- On-call engineers and incident commanders
- SRE and platform teams
- Teams building runbooks and synthetic incidents
- Engineering managers reviewing postmortems
Tetrees AI Pack · TAIP/1 · v1.0.0
An evidence-led incident commander that correlates metrics, logs, traces, topology, and deploys into ranked hypotheses, safe mitigations, and a post-incident learning record.
Tetrees Agent ガイド
During an outage, responders jump between alerts, logs, metrics, traces, topology, and recent changes; weak timelines and untested hunches create noisy investigations and risky remediation.
以下のリクエストを出発点にして、詳細を用途に合わせて置き換えてください。
API latency and error rate rose after a deployment while database CPU and connection metrics disagree.
Use these alert, CloudWatch metrics, deploy events, and application logs to rank causes. Distinguish DB saturation, connection leakage, and retry amplification, then propose the safest mitigation.
期待される結果: A normalized timeline, correlated evidence, ranked hypotheses, disconfirming checks, a reversible mitigation, rollback criteria, and no unapproved production action.
Several infrastructure alerts fire together and the team risks treating the loudest one as the root cause.
Analyze this incident bundle. Identify which alerts are causal, consequential, coincidental, or unsupported, and give the next three evidence-gathering steps.
期待される結果: A topology- and time-aware correlation report, evidence gaps, confidence, safe next checks, and a bounded incident status update.
Agent Studio で OpenAI、Claude、Z.AI を選び、Tetrees ポイントまたは BYOK で実行します。
ホスト実行では署名済みのツールだけを使用します。ローカル MCP では、別途設定して承認したツールを追加できます。
Agent AVCP
テスト済み TAIP 成果物は本レポートと署名に結合されています。スコアは試験証拠であり将来の全出力を保証しません。
総合スコア
9.5
10点満点
必須ゲート
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