Observability: real-time lifecycle dashboard + telemetry instrumentation for analysis runs

Summary

The multi-agent orchestration workflow (discovery → library analysis → application analysis → synthesis) currently has no structured telemetry or real-time visibility into its execution. There is a nascent observability/ directory with a static session_profiler.html and a live_monitor.sh script, but these are post-hoc tools that require manual wiring. This issue tracks adding first-class, always-on observability so any run can be profiled and monitored in a dashboard without extra setup.

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Problem

When a full analysis run executes (e.g., 24 libraries + 10 applications across 3 language stacks), there is no way to:

• See which subagents are currently active vs. queued vs. complete in real time
• Measure wall-clock time per phase (depth-0 libraries, depth-N libraries, application analysis, synthesis)
• Measure per-subagent token consumption, tool-call counts, and latency
• Detect stalled or failed subagents without tailing raw log files
• Compare performance across runs (e.g., a re-analysis after a diff vs. a full cold run)

The only current signal is logs/latest/tool_calls.jsonl and transcript.txt, which require manual parsing.

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Proposed Work

1. Structured Telemetry Emission

Instrument the lead agent and subagent lifecycle with structured span events written to a telemetry.jsonl file alongside the existing tool_calls.jsonl:

// Span open
{ "event": "span_start", "span_id": "lib-encoding", "parent": "phase-depth2", "component": "encoding", "kind": "library-analysis", "ts": 1712534400.123 }

// Span close
{ "event": "span_end", "span_id": "lib-encoding", "status": "ok", "duration_ms": 14820, "tokens": 42100, "tool_uses": 31, "ts": 1712534415.001 }

// Phase boundary
{ "event": "phase", "name": "library-depth-0-complete", "libraries": 9, "wall_ms": 68400, "ts": 1712534400.999 }

Spans to instrument:

• Full run (root span)
• Each phase (depth-0 libs, depth-N libs, application analysis, synthesis)
• Each subagent invocation (library / application / external-service / architecture-documenter)
• Discovery engine execution
• Manifest write

2. Live Dashboard (upgrade session_profiler.html)

Upgrade observability/session_profiler.html into a proper live dashboard that:

• Auto-refreshes by polling telemetry.jsonl (or a small SSE/WebSocket endpoint from serve_logs.py) every ~2s
• Gantt / swimlane view — one row per subagent, colored by phase, with wall-clock time on the x-axis; shows in-progress spans with an animated fill
• Phase summary bar — at the top: total elapsed, current phase, % complete, active agent count
• Per-agent cards — name, kind, status (queued / running / done / error), elapsed, tokens, tool calls
• Token burn rate chart — rolling 30s tokens/minute across all active subagents
• Error/warning panel — surfaces any "status": "error" spans immediately

Tech: keep it as a single-file HTML + vanilla JS + D3 (already imported); serve via the existing serve_logs.py.

3. serve_logs.py SSE endpoint

Add a /events Server-Sent Events endpoint to serve_logs.py that tails telemetry.jsonl and pushes new lines to connected browsers. This removes the need for the dashboard to poll a file and enables sub-second latency updates.

4. Run Summary Report

After synthesis completes, write logs/latest/run_summary.json with:

{
  "run_id": "eigenda-20260408-abc123",
  "source_repo": "https://github.com/Layr-Labs/eigenda",
  "source_commit": "61019b4",
  "total_wall_ms": 312500,
  "phases": {
    "discovery": { "wall_ms": 1200 },
    "library_depth_0": { "wall_ms": 68400, "agents": 9 },
    "library_depth_n": { "wall_ms": 112000, "agents": 15 },
    "application_analysis": { "wall_ms": 98000, "agents": 10 },
    "synthesis": { "wall_ms": 32900, "agents": 1 }
  },
  "totals": {
    "agents_spawned": 35,
    "total_tokens": 1842000,
    "total_tool_uses": 847,
    "analyses_written": 45
  }
}

This enables cross-run benchmarking and regression detection.

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Acceptance Criteria

• telemetry.jsonl is written automatically on every run with span_start / span_end / phase events
• observability/session_profiler.html shows a live Gantt view that updates without page refresh during an active run
• serve_logs.py exposes a /events SSE endpoint
• logs/latest/run_summary.json is written at the end of every run
• Dashboard correctly reflects the EigenDA run profile (9 parallel depth-0 agents, 15 sequential depth-N agents, 10 parallel app agents, 1 synthesizer)

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Context

The observability/ directory already has scaffolding (session_profiler.html, live_monitor.sh, serve_logs.py) — this issue is about making that scaffolding production-quality and always-on rather than opt-in.

Start by reading observability/session_profiler.html, live_monitor.sh, and serve_logs.py, then inspect how the existing logs/latest/tool_calls.jsonl and transcript.txt are produced. Run the current observability tooling to establish its baseline; done means automatic telemetry and run_summary.json output, a live dashboard, and the /events SSE endpoint meeting the listed acceptance criteria.