> ## Documentation Index
> Fetch the complete documentation index at: https://test-8ad8522e-feat-ai-sre.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Complete Your First AI SRE Investigation in 15 Minutes

> Start with a real incident and get your first AI SRE investigation — grounded in context, backed by evidence, and open to follow-up questions — then get every new incident analyzed automatically, build team knowledge, and deploy a BYOC Runner to reach real data sources.

<Info>
  **Public beta**: AI SRE is now open to all accounts — no application needed. Sign in to the console and start using it; it's free during the public beta. Features and the UI may continue to evolve.
</Info>

## Key Concepts (30 Seconds)

***

Four things to know before you start:

| Concept        | What it is                                                                                                                                 |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| Session        | Your working thread with the agent — start one from the console or from IM → [Chat](/en/ai-sre/sessions)                                   |
| @ reference    | How you bring an incident into the conversation — type `@` to search and insert it → [Chat](/en/ai-sre/sessions)                           |
| Knowledge base | The agent's long-term memory of your systems — the more complete it is, the sharper the diagnosis → [Knowledge](/en/ai-sre/knowledge)      |
| Environment    | Where the agent actually does the work — the cloud Sandbox, or a BYOC Runner on your own network → [Environments](/en/ai-sre/environments) |

## Three Entry Points

***

There's no single way into AI SRE — start from wherever you already are:

### Chat

For actively investigating an issue, digging deeper, or building up knowledge. Go to **AI SRE → Chat → New Chat**. Suggestion chips below the composer fill in a ready-made prompt; type `@` to reference an incident. → See [Chat](/en/ai-sre/sessions)

### Automations

For work that shouldn't need a human watching it — scheduled inspections, weekly insight reports, alert-noise cleanup. Go to **AI SRE → Automations** and create one from a built-in template (On-call Incident Analysis, Alert Governance, Weekly Insights, and more) or build one from scratch. Each run happens in a hidden session in the background, and the result lands in the run history where you can open the full trail any time. → See [Automations](/en/ai-sre/automations)

### IM (@ Mentions + Automatic War Room Diagnosis)

Where incidents actually happen. In any Slack, Feishu, DingTalk, or WeCom group with the bot connected, **@ AI SRE** to start or continue an investigation — it replies in-thread. Open a war room for an incident and AI SRE automatically runs an initial diagnosis and posts the conclusion back. → See [IM](/en/ai-sre/im)

## Your First Investigation (\~5 min)

***

Prerequisite: at least one incident in your account. AI SRE is open to everyone during the public beta — no application needed.

If you don't have an incident on hand right now, skip ahead to "Growing It Into Your SRE" below.

<Steps>
  <Step title="Start a new session">
    Go to **AI SRE → Chat** and click **New Chat** in the sidebar.
  </Step>

  <Step title="Bring in the incident context">
    Type `@` in the composer to open incident search, then pick the incident you want to investigate — it's inserted as a reference chip. You can also click the **Investigate an incident** chip below the composer: it fills the input with a ready-made prompt, and you can still append an `@` incident reference before sending.
  </Step>

  <Step title="Send your instruction">
    Send something like:

    ```
    Analyze this incident. List your investigation plan first, then give evidence for each conclusion. Do not make any production changes.
    ```
  </Step>

  <Step title="Watch the investigation unfold">
    The agent streams its plan, tool calls, and intermediate findings, then delivers a conclusion backed by evidence.
  </Step>
</Steps>

**You're done when**: you've received a reply with an investigation plan, a trail of tool calls, and an evidence-backed conclusion — and you can keep asking follow-up questions about it.

**If it doesn't work, check**:

* `@` doesn't find the incident — confirm it actually exists in this account and the name or ID is spelled correctly;
* No response for a while, or it's stuck on "environment initialization" — the cloud Sandbox or Runner is probably still starting up; give it a moment. For persistent unresponsiveness, see the troubleshooting section in [Environments](/en/ai-sre/environments);
* The reply only has a conclusion, no plan — just ask "give me an investigation plan first, then evidence for each point" and the agent will follow up;
* You can't find the **AI SRE** entry at all — the entry follows role permissions; ask your account admin to confirm your role has AI SRE access.

**Next**: for streaming, cancellation, forking a session, and context compaction, see [Chat](/en/ai-sre/sessions).

## Growing It Into Your SRE (As Needed)

***

Your first investigation is just the starting point. From here you can make analysis automatic, and keep sharpening its diagnoses — the latter depends on how much it knows about your systems and how much real data it can reach. All of this builds up over time, not all at once.

### Get every new incident analyzed automatically

Once manual investigations feel routine, make them the default: go to **AI SRE → Automations**, create a rule from the built-in **On-call Incident Analysis** template, enable the **On-call incident** trigger card, and pick the channels and severities to watch. From then on, every matching new incident gets an automatic investigation, and AI SRE writes one summary comment back to the incident — conclusion first, full session link at the end — delivered through the incident's existing notification channels, so responders see the first analysis right in IM. Configuration details: [Automations · On-call Incident Trigger](/en/ai-sre/automations#on-call-incident-trigger).

This is independent of war room auto-diagnosis: this path fires for every matching new incident and writes back to the incident itself; war room diagnosis runs when you open a war room for an incident and posts into the war room group (see [IM](/en/ai-sre/im)).

### Build a team knowledge base

If the agent has to guess "what service is this," "who owns it," and "how was this handled last time" on every investigation, it won't get much sharper. Type `/init` in a session and the agent interviews you to map out your services, runbooks, on-call routing, and more — confirming each write before it saves anything, and loading it automatically in future sessions. See the full interview flow in [Setup (/init)](/en/ai-sre/init) and the knowledge base structure and best practices in [Knowledge](/en/ai-sre/knowledge).

### Connect real data sources

By default the agent only sees Flashduty's own data. To let it query your actual systems — logs, metrics, code repositories, internal databases — connect an MCP server or deploy a BYOC Runner. Publicly reachable services (observability platforms, GitHub, and similar) install and authorize in one click from the MCP directory — see [MCP](/en/ai-sre/mcp); VPCs, internal databases, or local commands need a Runner deployed on a machine that can reach them — about three minutes, below.

### Deploy a BYOC Runner (\~3 minutes)

<Steps>
  <Step title="Create a self-hosted Environment">
    Go to **AI SRE → Environments** and click **Create** on the **Self-hosted** tab. The name is optional (the first heartbeat fills it in with the machine's hostname); pick Shared (account-level) or team scope.
  </Step>

  <Step title="Run the install command on the target machine">
    After creation, the **setup guide** opens with install commands that already include your Token — a Linux one-liner (needs sudo; registers a systemd service), plus Docker and macOS variants. Copy it onto a machine that can reach your target resources and run it.
  </Step>

  <Step title="Wait for the status to flip to Online">
    Back in the list, the status goes from **Pending** to **Online** once connected. Heartbeats are evaluated on a \~90-second cycle, so give it a moment after installing; if it stays Pending, the machine almost certainly can't reach the connect URL shown in the guide (outbound traffic blocked by a firewall).
  </Step>

  <Step title="Use it in a session">
    When starting a new chat, the environment picker at the bottom of the composer defaults to **Auto** — it prefers an online Runner when one is available; you can also select this self-hosted Environment explicitly. Note that the environment locks once the session starts and cannot be changed afterwards.
  </Step>
</Steps>

Permission rules (limiting what the agent may do on that machine), upgrades, and uninstallation: see [Environments](/en/ai-sre/environments).

## What's Next

***

<CardGroup cols={2}>
  <Card title="Product Overview" icon="robot" href="/en/ai-sre/overview">
    What AI SRE is, its full capability set, and console navigation.
  </Card>

  <Card title="Chat" icon="comments" href="/en/ai-sre/sessions">
    Creating and managing sessions, streaming, forking, and context compaction.
  </Card>

  <Card title="IM Platform" icon="comment-dots" href="/en/ai-sre/im">
    @ the agent in IM to troubleshoot, and learn about automatic war room diagnostics.
  </Card>

  <Card title="Environments" icon="server" href="/en/ai-sre/environments">
    Cloud Sandbox vs. BYOC Runner, deployment, and permission configuration.
  </Card>
</CardGroup>