AI Agent Observability Test Scenarios

Phase: RED → GREEN (TDD) Purpose: Validate that the references/ai-agents.md reference causes the skill to materially improve responses to AI coding agent observability questions.

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How to Use These Scenarios

1. RED phase: Test WITHOUT loading references/ai-agents.md. Record baseline responses.
2. GREEN phase: Test WITH skill active (SKILL.md loaded + ai-agents.md trigger fires). Verify improvements.
3. REFACTOR: Document any agent rationalizations and add counter-guidance.

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Scenario 1: Claude Code Telemetry Setup

Prompt:

"Set up OpenTelemetry monitoring for Claude Code to track token usage and costs"

Expected WITHOUT skill (RED baseline)

• May not know CLAUDE_CODE_ENABLE_TELEMETRY=1 is required (telemetry is opt-in)
• Likely suggests wrong or generic env var names
• Will not mention OTEL_EXPORTER_OTLP_METRICS_TEMPORALITY_PREFERENCE=cumulative
• No mention of ~/.claude/settings.json for persistent config
• No privacy controls (OTEL_LOG_USER_PROMPTS, OTEL_LOG_TOOL_DETAILS)
• No cardinality warning about session.id as metric dimension
• Generic collector YAML without Claude Code-specific considerations

Expected WITH skill (GREEN target)

• ✅ Includes CLAUDE_CODE_ENABLE_TELEMETRY=1 as prerequisite
• ✅ Provides exact env vars: OTEL_METRICS_EXPORTER=otlp, OTEL_LOGS_EXPORTER=otlp
• ✅ Shows ~/.claude/settings.json persistent config format
• ✅ Sets OTEL_EXPORTER_OTLP_METRICS_TEMPORALITY_PREFERENCE=cumulative
• ✅ Warns about OTEL_METRICS_INCLUDE_SESSION_ID and cardinality risk
• ✅ Mentions privacy controls are off by default
• ✅ Notes Claude Code emits metrics + logs, NOT traces

Compliance Check

• Response includes CLAUDE_CODE_ENABLE_TELEMETRY=1
• Response includes settings.json persistent config example
• Response mentions cumulative temporality preference
• Response warns about session.id as metric dimension
• Response notes no traces are emitted

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Scenario 2: Multi-Agent Collector

Prompt:

"I use Claude Code and Gemini CLI. Configure a single OTel Collector to receive telemetry from both."

Expected WITHOUT skill (RED baseline)

• Likely generates two separate, disconnected configs
• No normalization of service.name across agents
• May not know Claude Code uses gRPC (4317) while Copilot uses HTTP (4318)
• No resource processor to unify agent identifiers
• No OTTL transform to map claude_code.* to gen_ai.*
• memory_limiter may be missing or in wrong position

Expected WITH skill (GREEN target)

• ✅ Single OTLP receiver with both gRPC (4317) and HTTP (4318) protocols enabled
• ✅ Prefers OTLP gRPC by default, but explains when OTLP HTTP is the right fallback
• ✅ memory_limiter as first processor in every pipeline
• ✅ resource processor to tag telemetry.source.type: ai-coding-agent
• ✅ transform processor adding gen_ai.system attribute to Claude Code data
• ✅ Separate pipelines for metrics, logs, traces
• ✅ Notes Claude Code emits no traces (traces pipeline still useful for Gemini CLI)
• ✅ batch processor last before exporters

Compliance Check

• Single config with both gRPC and HTTP listeners
• Response prefers OTLP gRPC but allows OTLP HTTP when needed
• memory_limiter is first processor
• resource processor normalizes agent identity
• Separate metrics/logs/traces pipelines
• Notes Claude Code has no traces

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Scenario 3: Agent OTel Support Comparison

Prompt:

"Which AI coding agents support OpenTelemetry? I need traces specifically for debugging multi-step agent operations."

Expected WITHOUT skill (RED baseline)

• Vague or outdated answer based on training data
• May incorrectly claim Claude Code supports traces
• Likely misses Codex CLI partial support gaps
• No mention of Qwen Code "planned but not shipped" status
• No mention of OpenCode/Cursor/Windsurf having no native OTel
• No guidance on GenAI SemConv coverage

Expected WITH skill (GREEN target)

• ✅ Gemini CLI: full traces ✅, follows gen_ai.* SemConv, v0.34.0+
• ✅ GitHub Copilot (VS Code + CLI): full traces ✅, follows gen_ai.* SemConv
• ✅ Claude Code: NO traces ❌ — metrics + logs only; use prompt.id as pseudo-trace correlation
• ✅ Codex CLI: traces ⚠️ in interactive mode only; codex exec drops metrics
• ✅ Qwen Code: planned but not shipped 🔜
• ✅ OpenCode, Cursor, Windsurf, Aider: no native OTel ❌
• ✅ Recommends Gemini CLI or Copilot if traces are a hard requirement

Compliance Check

• Correctly identifies Gemini CLI and Copilot as trace-capable
• Correctly states Claude Code has NO traces
• Mentions Codex CLI partial support limitation
• Notes Qwen Code is planned but not shipped
• Suggests GenAI SemConv coverage as selection criterion

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Scenario 4: Privacy Controls for Claude Code

Prompt:

"Enable Claude Code telemetry but make sure no user prompts are logged"

Expected WITHOUT skill (RED baseline)

• May accidentally enable OTEL_LOG_USER_PROMPTS=true without warning
• Likely omits privacy env vars entirely
• No mention that prompts are redacted by default
• No warning about OTEL_LOG_TOOL_DETAILS leaking tool parameters
• No OTTL redaction recommendation for tool parameters that may contain secrets

Expected WITH skill (GREEN target)

• ✅ States prompts are redacted by default — OTEL_LOG_USER_PROMPTS defaults to false
• ✅ Explicitly sets OTEL_LOG_USER_PROMPTS=false (or omits it, noting the safe default)
• ✅ Warns about OTEL_LOG_TOOL_DETAILS — tool parameters may contain secrets/paths
• ✅ Recommends OTTL redaction processor for tool_parameters as defense-in-depth
• ✅ Notes captureContent risk if user later adopts GitHub Copilot
• ✅ Warns about OTEL_METRICS_INCLUDE_SESSION_ID=false (cardinality, not PII, but related)

Compliance Check

• States prompts are redacted by default in Claude Code
• Addresses OTEL_LOG_TOOL_DETAILS specifically
• Includes or recommends OTTL redaction for tool parameters
• Does NOT accidentally suggest enabling prompt logging

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Scenario 5: Dashboard Recommendations for Team AI Usage

Prompt:

"What dashboards should I build for monitoring our team's AI coding agent usage?"

Expected WITHOUT skill (RED baseline)

• Generic "build a dashboard" advice
• Vague panel suggestions without specific metric names
• No mention of community-built dashboards
• May suggest using user.id or session.id as metric dimensions (cardinality risk)
• No cost breakdown guidance
• No distinction between metrics-based and log-based panels

Expected WITH skill (GREEN target)

• ✅ References community dashboards: ai-observer, ColeMurray/claude-code-otel, Honeycomb template
• ✅ Panel 1: Token usage by model/agent over time — NOT by session.id
• ✅ Panel 2: Cost breakdown by agent and model
• ✅ Panel 3: API latency percentiles (p50/p95/p99)
• ✅ Panel 4: Tool call success/failure rates
• ✅ Panel 5: Active sessions via log queries (not metric dimensions)
• ✅ Panel 6: Cache hit ratio for Claude Code
• ✅ Warns about session.id/prompt.id cardinality if put in metric dimensions
• ✅ Notes some agents (Claude Code) require log-based queries for session counts
• ✅ Notes GenAI token dashboards should tolerate additional token classes (for example cache/reasoning), not just input/output

Compliance Check

• References at least one community dashboard (ai-observer or ColeMurray)
• Lists token usage, cost, and latency panels with specific metric names
• Warns about session.id as metric dimension
• Suggests log-based queries for session/user counts
• Mentions cache hit ratio for Claude Code
• Avoids assuming gen_ai.token.type is limited to only input / output

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Anti-Rationalization Notes

Document observed agent rationalizations and counter-guidance here as they are discovered during testing.