| name | agent-interaction-insights |
| description | Analyze agent transcripts and traces to recommend collaboration improvements and generate decision-oriented HTML reports. |
| when_to_use | Use when the user asks to analyze agent sessions, reduce corrections, improve trust, stop retry loops, compare agents, or generate insights from Claude/Codex/Gemini logs, OTel spans, or LangSmith/Langfuse exports. |
Agent Interaction Insights
Goal
Turn agent conversation and trace evidence into concrete next-run improvements: prompts, AGENTS.md/CLAUDE.md, workflow, validation rules, tool policy, or task routing. Lead with what should change; use evidence to justify the change. Default reports should read like decision material for an agent owner.
Workflow
-
Privacy mode: Default to team-share. Read references/privacy-modes.md before extracting prompt/response/path/command/header/secret-adjacent data. For HTML reports and examples, use reader-safe summaries: short task/claim summaries, field categories, time ranges, counts, statuses, and analysis boundaries. Exact local identifiers belong only in private-debug work requested by the user.
-
Classify the question:
improve-collaboration: reduce corrections, clarify framing, improve AGENTS.md/CLAUDE.md.improve-trust: make summaries and validation claims reliable.reduce-waste: stop loops, retry churn, token/time waste.improve-workflow: decide which instructions, checks, evals, policies, or workflow gates should change.compare-fit: compare agents, models, prompts, or task classes.
-
Route evidence to reference docs:
- Sources:
references/data-source-routing.md
- Improvement classes:
references/improvement-classes.md
- Evidence model:
references/common-evidence-model.md
- Friction taxonomy:
references/friction-taxonomy.md
- System summary input:
references/handoff-contract.md
- Output shapes:
references/report-shapes.md
- Examples:
references/example-patterns.md
If the user provides both interaction logs and AgentSight/system data, analyze only interaction evidence here. Consume already summarized system findings as compact context; route raw AgentSight data to agentsight-system-friction.
-
Build facts:
- Map records into sessions, messages, LLM calls, tool attempts, validation claims, and user signals.
- Extract minimal fields; distinguish observed from inferred.
- Note when transcripts cannot prove process/file/network side effects.
-
Recommend improvements:
- Lead with 3-7 changes ranked by expected leverage.
- Each: target, change, evidence, expected benefit, confidence, next action.
- Include findings as supporting evidence. Mark causal explanations as inference.
-
Shape output:
- Quick questions: what to change next + compact evidence.
- Full analysis or shareable output: self-contained HTML report. Name the reader and their decision before writing. Put decision, top changes, and strongest evidence in the first screen. Put source/privacy/capture details in a short appendix using plain language.
- PR handoff: PR comment or checklist.
- System side effects: recommend
agentsight-system-friction when AgentSight data is available.
Output Contract
Always include:
- evidence source and time range when available
- the user's likely decision question
- observed facts vs inferences
- evidence gaps
- privacy mode used, phrased for the reader rather than as schema labels
- whether raw logs were read and what field categories were extracted
Use redacted summaries by default.
For HTML reports, translate internal terms before writing: "system summary" for cross-boundary evidence, "single-page report" for the output, "analysis boundary" for scope, and category labels for local identifiers.
Example Requests
Analyze my last 20 Claude and Codex sessions. Where did the agents waste time and where should I improve AGENTS.md?
Use this Langfuse export to tell me whether the agent really validated the PR.
Generate a self-contained HTML report from these agent interaction findings, without raw prompts.
