Use AI to monitor your systems

You connect your logs, metrics, alerts and deploys to Tares as sources, and Tares merges them into one timeline per service. When an alert fires, a trigger wakes a built-in Tares agent, or your own agent service over a webhook, and the agent writes its diagnosis onto the service’s timeline before anyone opens a dashboard. Claude Code, Cursor and any other MCP client read the same timeline when you want to investigate yourself.

Prometheus or Alertmanager keeps owning alerting. Tares ingests what fired, correlates it with the evidence and handles the waking, so an incident starts with a diagnosis instead of a blank page.

What you connect

Each signal is a source. Give every source the same service label; that is what puts them on one timeline.

Most real root causes are a change, so the deploy source is the one worth adding first.

How an alert becomes a diagnosis

1. Source. An alert lands on the service’s timeline next to the metric that tripped it and the logs around it.
2. Trigger. A trigger watches the sources for that service and fires on a condition you choose. In the demo, it fires when an alert event arrives.
3. Agent. The trigger wakes a Tares agent, or posts the timeline to your own agent service over a webhook. The agent starts with the correlated timeline already in front of it, so it does not spend its first minutes querying each system.
4. Finding. The agent writes what broke, since when, the likely cause tied to evidence lines and a next action onto the timeline. The next investigation, yours or another agent’s, starts from that finding.

Try it with the demo

The demo runs on a laptop. It stands up a small api-server with Prometheus and a traffic generator, wires Tares to it, and lets you cause an incident on purpose:

curl -O https://raw.githubusercontent.com/glassflow/tares/main/demo/docker-compose.yml
docker compose up -d
uv tool install tares          # or: brew install glassflow/tap/tares
tares up

Then pick the AI SRE demo template under Projects in the console at http://127.0.0.1:8787, paste an Anthropic API key so the built-in agent can run, and click Start. To connect an MCP client such as Claude Code to the same timeline, start the MCP endpoint in a second shell:

tares mcp

Then point your client at it:

claude mcp add --transport http tares http://localhost:8788/mcp

Now cause an incident with the Cause an incident button on the project page, or from a shell:

curl -s -XPOST localhost:8080/demo/inject -H 'content-type: application/json' \
  -d '{"scenario": "error_spike"}'

Within about 30 seconds the alert fires and the trigger wakes the agent. The finding lands on the timeline when the agent’s run finishes.

Next: Build an AI SRE

Build an AI SRE walks through the demo step by step, connects an MCP client and then points the same setup at your real systems.