Getting Started

This guide walks platform teams and administrators through enabling Coder Agents, preparing your deployment, and running your first chat.

Prerequisites

Before you begin, confirm the following:

• Coder deployment running the latest release with the agents experiment flag available.
• LLM provider credentials — an API key for at least one supported provider (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 for the agent to select when provisioning workspaces.
• Admin access to the Coder deployment for enabling the experiment and configuring providers.

Step 1: Enable the experiment

Coder Agents is gated behind the agents experiment flag. Pass it when starting the Coder server:

If you already use other experiments, add agents to the comma-separated list:

See Enable Coder Agents for full details.

Step 2: Configure an LLM provider and model

Once the server restarts with the experiment enabled:

1. Navigate to the Agents page in the Coder dashboard.
2. Click Admin to open the configuration dialog.
3. Under the Providers tab, select a provider, enter your API key, and save.
4. Switch to the Models tab, click Add, and configure at least one model with its identifier, display name, and context limit.
5. 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 documentation.

Step 3: Start your first chat

1. Go to the Agents page in the Coder dashboard.
2. Select a model from the dropdown (your default will be pre-selected).
3. 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:

Descriptions to avoid:

See Template Optimization for the full guide, including dedicated agent templates, network boundaries, credential scoping, and pre-installing dependencies.

Things to know before you start

Deploy to a non-production environment

Coder Agents is under active development. APIs, behavior, and configuration may change between releases without notice. Run your evaluation on a dedicated test or staging deployment to avoid disruption to production developer workflows. See Early Access for the full set of expectations and limitations.

Set a deployment-wide system prompt

Administrators can set a system prompt that applies to all chats 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 the Admin dialog on the Agents page or via the API at PUT /api/experimental/chats/config/system-prompt. See Platform Controls 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 with tighter network policies.

Plan for LLM costs

Every chat 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 in the admin panel (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 enables programmatic access to Coder Agents. This is useful for building automations such as:

• Triggering chats from CI/CD pipelines when builds fail.
• Creating chats 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 chat via the API:

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

For service-to-service automation, use API keys 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 chat 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 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 IDs, or links to the Agents page help the team investigate quickly.

Your input directly influences product direction during Early Access.

Next steps