coder/docs/ai-coder/agents/getting-started.md

# Getting Started

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

> [!NOTE]
> Coder Agents is in Beta. APIs, behavior, and configuration may change
> between releases without notice; pin a release before broad rollout.
> Use **Coder version 2.33.1 or greater**.

## 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](./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](./platform-controls/template-optimization.md)
  for the agent to select when provisioning workspaces.
- **Admin access** to the Coder deployment for configuring providers.
- **Coder Agents User role** assigned to each user who needs to interact with Coder Agents.
  This role is granted **per organization**. Owners and organization admins can
  assign it from **Admin settings** > **Organizations** > _[your organization]_ >
  **Members**. See [Grant Coder Agents User](#step-2-grant-coder-agents-user)
  below.

## Step 1: Configure an LLM provider and model

> [!IMPORTANT]
> Configuring providers, models, and system prompts requires the
> **Owner** role (Coder administrator). Non-admin users cannot access the
> admin Settings panel or modify deployment-level Agents configuration.

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 the **Agents** page, open **Settings** > **Manage Agents**, and
   select **Models**.
1. Click **Add** and configure at least one model with its identifier, display
   name, and context limit.
1. Click the **star icon** next to a model to set it as the default.

Detailed instructions for each provider and model option are in the
[Models](./models.md) documentation.

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

## Step 2: Grant Coder Agents User

The **Coder Agents User** role controls which users can interact with Coder
Agents. The role is assigned **per organization**, so a user must be granted
it in each organization where they need access. Members do not have it by
default.

Owners always have full access and do not need the role. Repeat the following
steps for each user who needs access in each organization.

**Dashboard (individual):**

1. Open **Admin settings** > **Organizations** in the Coder dashboard, then
   select the organization where you want to grant access.
1. The **Members** tab opens by default. Find the user in the table.
1. Click the **Roles** cell for that user to open the role editor.
1. Toggle on **Coder Agents User** and save.

> [!TIP]
> If your deployment has multiple organizations, repeat this for each
> organization where the user needs access.

**CLI (bulk, per organization):**

Granting the role via CLI is org-scoped. The `edit-roles` command **replaces**
the member's full set of org roles, so include every role you want them to
keep. To grant `agents-access` to a single user while preserving their
existing org roles:

```sh
ORG="my-org"
USER="alice"
ROLES=$(coder organizations members list -O "$ORG" -o json \
  | jq -r --arg user "$USER" \
      '.[] | select(.username == $user) | [.roles[].name, "agents-access"]
      | unique | join(" ")')
# shellcheck disable=SC2086
coder organizations members edit-roles "$USER" -O "$ORG" $ROLES
```

To grant the role to every member of an organization while preserving their
existing roles:

```sh
ORG="my-org"
coder organizations members list -O "$ORG" -o json \
  | jq -c '.[] | {user_id, roles: [.roles[].name]}' \
  | while read -r row; do
      user_id=$(echo "$row" | jq -r '.user_id')
      roles=$(echo "$row" | jq -r '(.roles + ["agents-access"]) | unique | join(" ")')
      # shellcheck disable=SC2086
      coder organizations members edit-roles "$user_id" -O "$ORG" $roles
    done
```

You can also set the organization with the `CODER_ORGANIZATION` environment
variable instead of `-O`.

## Step 3: 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. If the task requires
a workspace — reading files, running commands, editing code — the agent
selects a template and provisions one automatically. Conversations that
don't require compute (planning, Q&A, architecture discussions) start
immediately with no provisioning delay.

## Optimize your templates

The agent selects templates based on their **name and description** — 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.).

**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](./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 under active development. APIs, behavior, and
configuration may change between releases without notice. Pin a
specific release before broad rollout and review the release notes
before upgrading so changes do not surprise developers in 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](../../admin/setup/index.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 **Agents** > **Settings** >
**Manage Agents** > **Instructions**
or via the API at `PUT /api/experimental/chats/config/system-prompt`.
See [Platform Controls](./platform-controls/index.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](./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.
- Setting per-model token pricing under **Agents** > **Settings** >
  **Manage Agents** > **Models** (Input Price, Output Price) to track spend.
- Monitoring provider dashboards for usage trends during the evaluation.

### 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](../../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/experimental/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:

```text
GET /api/experimental/chats/{chat}/stream
```

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

> [!NOTE]
> The Chats API is in beta and may change without notice.
> See [Chats API](../../reference/api/chats.md) for the full endpoint reference.

### 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](../../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 during Beta.

## Next steps

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