# Put eyes on the deploy

Picks the metrics that would show trouble, then watches them in Datadog and Sentry against baseline once the deploy lands.

## The prompt

```
I want to do this: We're deploying checkout-api at 5pm. Watch the metrics after and flag anything that moves.

## Setup (skip if Polylane is already set up)

Read and follow https://polylane.com/auth.md for non-interactive signup and setup. Start by checking whether I am already signed in; reuse my account and workspace.

If the CLI is missing, bootstrap it without starting the interactive wizard:

curl -fsSL 'https://polylane.com/setup?ref=prompts' | bash -s -- --install-only

Then follow the guide through email verification, workspace selection, source connections, and MCP authentication. Ask me for an email code or OAuth consent only when needed. Verify each step; report pending setup instead of claiming success from installation alone.

## How to work

Over MCP: searchTools lists what this workspace exposes, with each tool's schema; call it first. runTool runs one tool, runCode chains several in one call and returns just the answer. search and execute cover the full Polylane REST API: threads, issues, investigations, autofixes, memories.
From the terminal: the polylane CLI wraps the same API, with structured output and non-interactive flags everywhere.
Reads always work. Write tools appear only if I have opted in, and every write is screened.

## Task: Put eyes on the deploy

Steps:
1. Read the deploy's diff and work out which services and metrics it can affect
2. Record each metric's pre-deploy baseline
3. Compare the post-deploy window against baseline, rhythm-aware
4. Check error rates and logs for new shapes, not just volume
5. Deliver the verdict: clean, or exactly what moved with the series behind it

Ground every claim in data you actually pulled: the query, the log line, the change record. If the data is inconclusive, say so. Ask me before anything that writes.
```

## What it replaces

**"Keep an eye on it" means nobody does.** The deploy lands, everyone watches the graph for four minutes, then dinner happens. The regression that takes twenty minutes to show up reports itself via customers.

- Four minutes of vigilance for a twenty-minute regression
- Nobody agreed which metrics even matter for this change
- The evening deploy that ruins the evening

## What the agent does

1. Read the deploy's diff and work out which services and metrics it can affect
2. Record each metric's pre-deploy baseline
3. Compare the post-deploy window against baseline, rhythm-aware
4. Check error rates and logs for new shapes, not just volume
5. Deliver the verdict: clean, or exactly what moved with the series behind it

## What you get

**Vigilance that doesn't get bored** The right metrics identified from the diff and watched properly after the deploy. You hear about the regression from the watcher, not from customers.

Every prompt: https://polylane.com/prompts/

Get started with one command: `curl -fsSL https://polylane.com/setup | bash` installs the CLI, connects your coding agents, and creates the account.