# Getting Started

This guide walks platform teams and administrators through setting up Coder
Agents, preparing your deployment, and running your first Coder Agent.

## Prerequisites

Before you begin, confirm the following:

- **Coder deployment** running the latest release.
- **LLM provider credentials** — an API key for at least one
  [supported provider](https://coder.com/docs/ai-coder/agents/models.md) (Anthropic, OpenAI, Google, Azure OpenAI,
  AWS Bedrock, OpenAI Compatible, OpenRouter, or Vercel AI Gateway).
- **Network access** from the control plane to your LLM provider. Workspaces
  do not need LLM access — only the control plane does.
- **At least one template** with a
  [descriptive name and description](https://coder.com/docs/ai-coder/agents/platform-controls/template-optimization.md)
  for the agent to select when provisioning workspaces.
- **Admin access** to the Coder deployment for configuring providers.
- **Access to configure models** in each organization where you configure models.
  The **Organization Admin** role and the **Owner** role include this access.
  A custom role with model configuration access also works.
- **Organization membership** for each user who uses Coder Agents.
  Users also need read access to at least one model in the organization.
  New models are shared with the whole organization by default; to restrict who can use Coder Agents, narrow the model access lists.
  See [Manage model permissions](https://coder.com/docs/ai-coder/agents/models.md#manage-model-permissions).

## Step 1: Configure an LLM provider and model

> [!IMPORTANT]
> Deployment administrators configure providers and deployment settings.
> Users with model configuration access in an organization configure that organization's models.
> Coder enables the edit controls for the organization that you select.
> Coder shows the deployment settings only to deployment administrators.
> Users with model access can view the relevant Models and Coder Agents pages.
> Users with MCP server access can open the MCP servers page.

To configure Coder Agents:

1. Navigate to **Admin settings** > **AI** and select **Providers**.
1. Add or update a provider with its credentials and upstream endpoint, then
   save it.
1. Navigate to **Admin settings** > **AI** > **Models**.
1. Select the correct organization.
   Coder shows the organization picker when you can access more than 1 organization.
1. Select **Add model** and configure at least one model with its identifier, display name, and context limit.

Coder makes the first model of an organization the default model.
To change the default later, open a model and select **Set as Coder Agents default model**.

Each organization has its own model list and its own default model.
Repeat the model steps in every organization that uses Coder Agents.
Refer to [Organization scope](https://coder.com/docs/ai-coder/agents/platform-controls/organizations.md) for the settings that stay deployment-wide.

Detailed instructions for each provider and model option are in the
[Models](https://coder.com/docs/ai-coder/agents/models.md) documentation.

> [!TIP]
> Start with a single frontier model to validate your setup before adding
> additional providers.

## Step 2: Start your first Coder Agent

1. Go to the **Agents** page in the Coder dashboard.
1. Select a model from the dropdown (your default will be pre-selected).
1. Type a prompt and send it.

The agent processes the prompt in the control plane.
When the conversation and available tools are sufficient, it works without provisioning a workspace.
If missing tools, skills, MCP integrations, or context block progress, the agent selects a template and provisions a workspace to continue.
It also provisions a workspace for tasks that need file access, command execution, or code changes.

## Optimize your templates

The agent selects templates based on their **name, description, and README**.
It does not read Terraform. Clear, specific descriptions are the most important
factor in whether the agent picks the right template.

Update your template descriptions to include:

- The language, framework, or stack the template targets.
- Which repository or service it is for, if applicable.
- What type of work it supports (backend, frontend, data pipeline, etc.).

When 128 characters is not enough, put the most important routing context near
the top of the template's
[`README.md`](https://coder.com/docs/ai-coder/agents/platform-controls/template-optimization.md#put-routing-context-near-the-top-of-the-readme).
The chat agent's template listing includes a bounded README excerpt (roughly the
first 1,000 characters), and template detail includes the README (up to roughly
8,000 characters). Both are reduced to plain text: frontmatter is stripped,
link text is kept while link URLs are dropped, images and badges are dropped
entirely, and code blocks and tables are preserved as text.

**Good examples:**

| Description                                                                                 | Why it works                                 |
|---------------------------------------------------------------------------------------------|----------------------------------------------|
| Python backend services for the payments repo. Includes Poetry, Python 3.12, and PostgreSQL | Specific language, repo, and toolchain       |
| React frontend development for the customer portal. Node 20, pnpm, Storybook pre-installed  | Clear stack, named project, key tools listed |
| General-purpose Go development environment with Go 1.23, Docker, and common CLI tools       | Broad but descriptive                        |

**Descriptions to avoid:**

| Description        | Problem                                         |
|--------------------|-------------------------------------------------|
| Team A template v2 | No information about what the template is for   |
| Dev environment    | Too generic to distinguish from other templates |
| Default            | Tells the agent nothing                         |

See [Template Optimization](https://coder.com/docs/ai-coder/agents/platform-controls/template-optimization.md) for
the full guide, including dedicated agent templates, network boundaries,
credential scoping, and pre-installing dependencies.

## Things to know before you start

### Plan for change between releases

Coder Agents is generally available.
However, APIs, behavior, and configuration may change between releases.
As always, you should review [release notes](https://github.com/coder/coder/releases) and the [changelog](https://coder.com/changelog) before upgrading, so changes do not affect production.

### Use HTTPS for push notifications

Coder Agents use browser push notifications to alert you when a task
completes or needs attention. Most browsers require a secure (HTTPS)
origin for the [Push API](https://developer.mozilla.org/en-US/docs/Web/API/Push_API)
to work. If your access URL uses plain HTTP,
push notifications may not function.

This does not affect agents themselves — only the browser notification
delivery. If you terminate TLS at a reverse proxy, ensure the
[access URL](https://coder.com/docs/admin/setup.md) is configured with an `https://` scheme.

### Set a deployment-wide system prompt

Administrators can set a system prompt that applies to all Coder Agents across the
deployment. Use this to encode organizational conventions:

- Coding standards and style guidelines.
- Commit message formats.
- Branch naming conventions.
- Required review processes before merging.
- Any guardrails specific to your environment.

Configure the system prompt from **Admin settings** > **AI** > **Coder Agents** > **Instructions**
or via the API at `PUT /api/v2/chats/config/system-prompt`.
See [Platform Controls](https://coder.com/docs/ai-coder/agents/platform-controls.md) for details.

### Understand the security model

The agent runs in the control plane, not inside workspaces. This means:

- **No LLM API keys in workspaces.** Credentials stay in the control plane.
- **No agent software in workspaces.** No supply chain risk from
  third-party agent tools.
- **User identity is always attached.** Every action is tied to the user
  who submitted the prompt — no shared bot accounts.
- **No privilege escalation.** The agent has exactly the same permissions
  as the prompting user.

Agent workspaces inherit the same network access as any manually created
workspace. If your templates don't restrict egress, the agent has full
internet access from the workspace. Consider
[creating dedicated agent templates](https://coder.com/docs/ai-coder/agents/platform-controls/template-optimization.md#create-dedicated-agent-templates)
with tighter network policies.

### Plan for LLM costs

Every conversation turn sends tokens to your LLM provider. Long-running tasks,
sub-agent delegation, and complex multi-step work can consume significant
token volume. Consider:

- Starting with a single model to establish a cost baseline.
- Capping spend with [AI Gateway budgets](https://coder.com/docs/ai-coder/agents/platform-controls/spend-management.md).
- Monitoring provider dashboards for usage trends during the evaluation.

### Plan for concurrency limits

Community licenses run up to 5 agents at once.
Additional agents queue and start automatically when capacity frees.
Premium licenses with Agent Hours do not impose a concurrency limit unless the Agent Hours hard limit is reached.
If the Agent Hours allocation is exhausted without a configured hard limit, Coder warns about usage but does not impose a concurrency limit.
When the Agent Hours hard limit is reached, additional agents queue under the concurrency limit.
Refer to [Concurrent agents](https://coder.com/docs/ai-coder/agents/platform-controls.md#concurrent-agents) for details.

### Pilot with a small group

Identify 3–5 developers and a few concrete use cases for the initial rollout.
Good starting points:

- **Low-risk, high-visibility tasks** — generating unit tests, writing inline
  documentation, small refactors.
- **Investigation and triage** — exploring unfamiliar code, triaging bugs,
  understanding legacy systems.
- **Prototyping** — building proof-of-concept implementations, simple
  dashboards, internal tools.

Set expectations that this is an evaluation period. Developers should still
review all agent-produced code before merging. The agent is a force
multiplier, not a replacement for developer judgment.

### Use the API for programmatic automation

The [Chats API](https://coder.com/docs/reference/api/chats.md) enables programmatic access to Coder Agents.
This is useful for building automations such as:

- Triggering Coder Agents from CI/CD pipelines when builds fail.
- Creating Coder Agents from GitHub webhooks on new issues or PRs.
- Building internal tools or dashboards on top of the API.
- Scripting batch operations across repositories.

**Quick example — create a Coder Agent via the API:**

```sh
curl -X POST https://coder.example.com/api/v2/chats \
  -H "Coder-Session-Token: $CODER_SESSION_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "content": [
      {"type": "text", "text": "Fix the failing tests in the auth service"}
    ]
  }'
```

Stream updates in real time by connecting to the WebSocket endpoint:

```txt
GET /api/v2/chats/{chat}/stream
```

For service-to-service automation, use
[API keys](https://coder.com/docs/admin/users/sessions-tokens.md)
rather than developer session tokens. Keep automation credentials
narrowly scoped.

### Add workspace context with AGENTS.md

Create an `AGENTS.md` file in the home directory (`~/.coder/AGENTS.md`) or
the workspace agent's working directory to provide persistent context to the
agent. This file is automatically read and included in the system prompt
for every conversation with a Coder Agent that uses that workspace.

Use it for:

- Repository-specific build and test instructions.
- Important architectural decisions or constraints.
- Links to relevant documentation or runbooks.
- Any context that helps the agent work effectively in that codebase.

### Consider prebuilt workspaces for faster startup

Workspace provisioning is the main source of latency when the agent starts a
task. If your templates take more than a minute to provision, consider
configuring
[prebuilt workspaces](https://coder.com/docs/admin/templates/extending-templates/prebuilt-workspaces.md)
to maintain a pool of ready-to-use workspaces. The agent gets assigned an
already-running workspace instead of provisioning from scratch.

## Providing feedback

Coder Agents is a collaborative evaluation between your team and Coder.
Share feedback — workflow observations, feature requests, bugs, performance
issues, or operational challenges — through your **customer-specific Slack
channel** with the Coder team.

Good feedback includes:

- **What you tried** — the prompt, the template, and the model.
- **What happened** — the agent's behavior, any errors, unexpected results.
- **What you expected** — the outcome you were looking for.
- **Context** — screenshots, `chat_id` values, or links to the Agents page help
  the team investigate quickly.

Your input directly influences product direction.

## Next steps

- [Architecture](https://coder.com/docs/ai-coder/agents/architecture.md) — how the control plane, LLM providers,
  and workspaces interact.
- [Models](https://coder.com/docs/ai-coder/agents/models.md) — configure additional providers and models.
- [Platform Controls](https://coder.com/docs/ai-coder/agents/platform-controls.md) — system prompts,
  template routing, and admin-level configuration.
- [Template Optimization](https://coder.com/docs/ai-coder/agents/platform-controls/template-optimization.md) —
  create agent-friendly templates with network boundaries and scoped
  credentials.
- [Chats API](https://coder.com/docs/reference/api/chats.md): build programmatic integrations.