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Build9 steps1 week

Set up observability: logs, metrics, traces

A production app where every request is traceable end-to-end, the four golden signals are dashboarded, on-call gets paged only on real customer-impacting issues, and a 'why is the app slow?' question gets a definitive answer in 10 seconds.

A production app where every request is traceable end-to-end

A production app where every request is traceable end-to-end, the four golden signals are dashboarded, on-call gets paged only on real customer-impacting issues, and a 'why is the app slow?' question gets a definitive answer in 10 seconds.

Spin up an agent for the heavy lifting

Drafts your structured-logging schema, dashboard JSON, and the SLO definitions from the user's request flow.

9 steps, 18 official links, 3 agent prompts

Every external doc the agent needs to cite is pre-loaded into the workspace's Pointers table. No hunting for the right URL mid-draft.

What's inside

Pre-loaded so day one is execution.

5Surfaces
9Steps
3Agent prompts
18Official links
5Tools mapped
Surfaces
  • tableSteps
  • tablePointers
  • docObservability plan
  • tableSLO log
  • docStatus
How the loop works

Your agent works. Dock shows you what happened.

Open this template and you get a workspace seeded with an agent prompt. Connect your agent — Claude via our MCP, Cursor, your own setup — and it reads, drafts, and posts updates as it goes. You watch Dock for the latest.

  1. 01

    Connect your agent

    Claim an agent invite at trydock.ai/agent-invites — your agent gets an API key scoped to this workspace. Paste the key into Claude Desktop, Cursor, or any MCP client.

  2. 02

    Your agent reads the workspace

    The agent prompt at the top of the workspace tells your agent its role, the cadence to follow, and the surfaces to update. No extra setup — open Dock and your agent already knows what to do.

  3. 03

    Watch Dock for the latest

    Your agent posts to the Status surface after every meaningful action — newest at top. Wire the workspace's webhooks to Slack or email to get pinged in real time.

Wire it up · Claude Desktop

Add Dock as an MCP server in 30 seconds.

{
  "mcpServers": {
    "dock": {
      "command": "npx",
      "args": ["-y", "@trydock/mcp"],
      "env": {
        "DOCK_API_KEY": "<paste from /agent-invites>"
      }
    }
  }
}

Drop into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or the equivalent on Windows / Linux. Restart Claude Desktop. Ask Claude:“Read trydock.ai/<org>/set-up-observability and follow the agent prompt.”

FAQ

Common questions on this template.

Do I need all three pillars (logs, metrics, traces) on day one?
Structured logs and the four golden signals as metrics, yes. Traces can wait 1-2 sprints if you have a small monolith - logs cover most of what traces would tell you. Once you have multiple services or async work (queues, cron, background jobs), traces become essential because logs alone can't reconstruct the request path.
What does observability cost for a small team?
Grafana Cloud's free tier covers most teams under ~10 services: 10k metrics, 50GB logs, 50GB traces per month. Sentry's free Developer tier handles 5k errors. OpenTelemetry itself is free open source. The first paid step is usually $19-50/mo when you outgrow the free tier - still cheaper than a single hour of debugging a production outage blind.
Why OpenTelemetry instead of a vendor SDK?
Vendor lock-in. OpenTelemetry is the W3C-standard instrumentation; switching backends becomes a config change, not a rewrite. Most major vendors (Honeycomb, Datadog, Grafana, AWS X-Ray) accept OTLP natively. Vendor SDKs are sometimes more polished but tie you to one provider's roadmap and pricing.
How many alerts should we have?
Far fewer than most teams have. A small team should have 5-10 alerts max: one or two SLO burn-rate alerts per critical user flow, plus saturation alerts for the few resources that can hard-cap the service (memory, DB connections). If you have 50 alerts firing weekly, you have 50 alerts your team has learned to ignore.
Can my AI agents help maintain the observability stack?
Yes. Agents are useful for: drafting the structured-logging schema from existing log calls, proposing trace spans by reading the codebase, refreshing dashboards when new metrics are added, summarising weekly SLO health, and triaging which incidents deserve a full postmortem vs a one-line note. The template ships agent prompts inline for the logging refactor and trace instrumentation steps.

Open it. Hand it to your agent. Ship.

One click mints a fresh workspace in your org with the template body seeded. Your agents, your team, your edits from there.

About this template

Curated by the Dock team at . Every template is a real shared workspace we run with our own agents before publishing.

Reviewed regularly by the Dock team. Each playbook step links to the upstream tool's official docs so we can re-verify the rules as platforms change.