Operate the fleet — performance, policy, and the pipes.

Your seat answers: are agents responsive, governed, and instrumented — and what's the one thing to fix next?

Bottom line

Instrumentation coverage 88% (7/8 reporting; 1 shadow). Fix next: Codex CLI — worst tail at 6,800 ms p95, $78.00, ungoverned — one prioritized target. 3 agents are ungoverned. Latency measures response speed, not task success — agent outcome/quality is not yet measured.

Do nextFix Codex CLI (worst p95) →Govern 3 ungoverned →Instrument 1 shadow →

88%

instrumentation coverage

7 of 8 reporting · 1 shadow

6,800 ms

worst p95 latency

Codex CLI

ungoverned agents

no policy binding

MCP servers

tool surfaces in use

Fix next — one prioritized target

worst p95 × cost × governance

Codex CLI is your slowest agent at 6,800 ms p95, costs $78.00, and is ungoverned.

Highest tail latency in the fleet — wiring or budget here moves the worst number first. Open Performance →

Also high-latency × high-cost: Codex CLI, Claude Code — slow and expensive, watch together.

Your surfaces

Performancep95 6,800 ms

LLM responsiveness — tail latency.

Agent Inventory8 agents

Every agent + runtime detail.

Policy & Enforcement3 ungoverned

What an enforcing policy would block.

Integrations1 shadow

Stream telemetry to your stack.

Derived: instrumentation coverage · fix-next (worst p95 × cost × governed) — crossing performance.agents with agents in app/data/c16-fleet.ts.