docs(docs/ai-coder): replace Coder Tasks references with Coder Agents (#24929)

Updates `docs/ai-coder/index.md`, `docs/ai-coder/best-practices.md`, and
`docs/ai-coder/ai-governance.md` to point readers at Coder Agents and
the AI Governance Add-On instead of Coder Tasks and Agent Firewall
(CODAGT-157).

## Changes

- `docs/ai-coder/index.md`:
- Rename `## Agents with Coder Tasks` to `## Coder Agents`. Drop the
Devin / ChatGPT Codex name-drops and the Tasks pitch. New copy points at
`./agents/index.md`, names the agent loop in the control plane, and
notes that workspaces can be completely network isolated. Image swapped
from `tasks-ui.png` to `agents-hero-image.png` (the hero shot added in
#24915).
- Replace the `## Secure Your Workflows with Agent Firewall` section
with `## Govern AI activity with the AI Governance Add-On`. The new
section opens with adoption-first framing (visibility, guardrails, cost)
and links to `./ai-governance.md`, with bulleted callouts for AI
Gateway, Agent Firewall, and the expanded Agent Workspace Build
allowance the add-on bundles.
- `docs/ai-coder/best-practices.md`:
- In the use-case table, swap `[Tasks](./tasks.md)` to `[Coder
Agents](./agents/index.md)` for the developer-led-investigation and
prototyping rows, and swap the "Tasks API *(in development)*" cell to
`[Coder Agents API](./agents/chats-api.md)` for the background-jobs row.
Retitle the Security section link from "securing agents with Coder
Tasks" to "securing AI agents" since `security.md` does not actually
mention Tasks. Re-ran `markdown-table-formatter` to repad column widths.
- In `## Provide Agents with Proper Context`, add a paragraph describing
how context is provided in Coder Agents (admin-configured system
prompts, centrally registered MCP servers, and skills shipped from repos
or templates under `.agents/skills/`), with a transition line clarifying
that the existing Memory and Tools subsections cover BYO-agent patterns.
- `docs/ai-coder/ai-governance.md`: drop the "Additional Tasks Use (via
Agent Workspace Builds)" bullet from the intro feature list and the
"Expanding the use of Coder Tasks for AI-driven background work" bullet
from the audience list. The `## How Coder Tasks usage is measured`
section and the rest of the Tasks-related prose on this page are
intentionally left for a follow-up PR.

## Notes for the reviewer

- The `[Coder Agents API](./agents/chats-api.md)` link in
`best-practices.md` will need to be retargeted if #24830 (which replaces
`agents/chats-api.md` with auto-generated `reference/api/chats.md`)
lands first.
- This is the first slice of the Tasks-references audit. Remaining files
(`tasks-core-principles.md`, `tasks-lifecycle.md`, `tasks-migration.md`,
`cli.md`, `github-to-tasks.md`, `agent-compatibility.md`, the rest of
`ai-governance.md`, `custom-agents.md`,
`ai-gateway/clients/claude-code.md`, `manifest.json`,
`reference/api/tasks.md`, the `task*` CLI references, the ESR upgrade
guide, `feature-stages.md`, `workspace-scheduling.md`,
`shared-workspaces.md`) will land in follow-up PRs against the same
Linear ticket. Open PRs #24831, #24833, and #24841 cover separate slices
and do not touch any file in this PR.
- Validation: `markdownlint-cli2`, `markdown-table-formatter`,
`scripts/check_emdash.sh`, and `make pre-commit-light` all pass.

PR generated with Coder Agents.
This commit is contained in:
Matt Vollmer
2026-05-04 13:00:39 -04:00
committed by GitHub
parent f6eccbab23
commit 5612bb81cb
3 changed files with 44 additions and 36 deletions
-4
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@@ -14,9 +14,6 @@ that help organizations safely roll out AI tooling at scale:
MCP server management, and policy enforcement
- [Agent Firewall](./agent-firewall/index.md): Process-level firewalls for
agents, restricting which domains can be accessed by AI agents
- [Additional Tasks Use (via Agent Workspace Builds)](#how-coder-tasks-usage-is-measured):
Additional allowance of Agent Workspace Builds for continued use of Coder
Tasks.
## Who should use the AI Governance Add-On
@@ -30,7 +27,6 @@ It's a good fit if you're:
- Looking to centrally observe, audit, and govern AI activity in Coder
Workspaces
- Managing AI workflows against sensitive or regulated codebases
- Expanding the use of Coder Tasks for AI-driven background work
If you already use other AI Governance tools, such as third-party LLM gateways
or vendor-managed policies, you can continue using them. Coder Workspaces can
+12 -8
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@@ -8,18 +8,22 @@ To successfully implement AI coding agents, identify 3-5 practical use cases whe
Below are common scenarios where AI coding agents provide the most impact, along with the right tools for each use case:
| Scenario | Description | Examples | Tools |
|------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|
| **Automating actions in the IDE** | Supplement tedious development with agents | Small refactors, generating unit tests, writing inline documentation, code search and navigation | [IDE Agents](./ide-agents.md) in Workspaces |
| **Developer-led investigation and setup** | Developers delegate research and initial implementation to AI, then take over in their preferred IDE to complete the work | Bug triage and analysis, exploring technical approaches, understanding legacy code, creating starter implementations | [Tasks](./tasks.md), to a full IDE with [Workspaces](../user-guides/workspace-access/index.md) |
| **Prototyping & Business Applications** | User-friendly interface for engineers and non-technical users to build and prototype within new or existing codebases | Creating dashboards, building simple web apps, data analysis workflows, proof-of-concept development | [Tasks](./tasks.md) |
| **Full background jobs & long-running agents** | Agents that run independently without user interaction for extended periods of time | Automated code reviews, scheduled data processing, continuous integration tasks, monitoring and alerting | [Tasks](./tasks.md) API *(in development)* |
| **External agents and chat clients** | External AI agents and chat clients that need access to Coder workspaces for development environments and code sandboxing | ChatGPT, Claude Desktop, custom enterprise agents running tests, performing development tasks, code analysis | [MCP Server](./mcp-server.md) |
| Scenario | Description | Examples | Tools |
|------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|
| **Automating actions in the IDE** | Supplement tedious development with agents | Small refactors, generating unit tests, writing inline documentation, code search and navigation | [IDE Agents](./ide-agents.md) in Workspaces |
| **Developer-led investigation and setup** | Developers delegate research and initial implementation to AI, then take over in their preferred IDE to complete the work | Bug triage and analysis, exploring technical approaches, understanding legacy code, creating starter implementations | [Coder Agents](./agents/index.md), to a full IDE with [Workspaces](../user-guides/workspace-access/index.md) |
| **Prototyping & Business Applications** | User-friendly interface for engineers and non-technical users to build and prototype within new or existing codebases | Creating dashboards, building simple web apps, data analysis workflows, proof-of-concept development | [Coder Agents](./agents/index.md) |
| **Full background jobs & long-running agents** | Agents that run independently without user interaction for extended periods of time | Automated code reviews, scheduled data processing, continuous integration tasks, monitoring and alerting | [Coder Agents API](./agents/chats-api.md) |
| **External agents and chat clients** | External AI agents and chat clients that need access to Coder workspaces for development environments and code sandboxing | ChatGPT, Claude Desktop, custom enterprise agents running tests, performing development tasks, code analysis | [MCP Server](./mcp-server.md) |
## Provide Agents with Proper Context
While LLMs are trained on general knowledge, it's important to provide additional context to help agents understand your codebase and organization.
For [Coder Agents](./agents/index.md), context comes from a few complementary places. Platform admins configure a [system prompt](./agents/platform-controls/index.md) that applies to every chat and register [MCP servers](./agents/platform-controls/mcp-servers.md) once for the whole deployment. Repos and workspace templates can ship reusable [skills](./agents/extending-agents.md) under `.agents/skills/`, which the agent discovers automatically when it attaches to the workspace. Developers don't need to manage memory files or wire up tools themselves.
The rest of this section covers patterns for agents you run yourself inside a workspace, such as Claude Code or Codex.
### Memory
Coding Agents like Claude Code often refer to a [memory file](https://docs.anthropic.com/en/docs/claude-code/memory) in order to gain context about your repository or organization.
@@ -46,7 +50,7 @@ In internal testing, we have seen significant improvements in agent performance
LLMs and agents can be dangerous if not run with proper boundaries. Be sure not to give agents full permissions on behalf of a user, and instead use separate identities with limited scope whenever interacting autonomously.
[Learn more about securing agents with Coder Tasks](./security.md)
[Learn more about securing AI agents](./security.md)
## Keep it Simple
+32 -24
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@@ -14,35 +14,43 @@ for agents such as GitHub Copilot and Roo Code.
These agents work well inside existing Coder workspaces as they can simply be
enabled via an extension or are built-into the editor.
## Agents with Coder Tasks
## Coder Agents
In cases where the IDE is secondary, such as prototyping or long-running
background jobs, agents like Claude Code or Aider are better for the job and new
SaaS interfaces like [Devin](https://devin.ai) and
[ChatGPT Codex](https://openai.com/index/introducing-codex/) are emerging.
In cases where the IDE is secondary, such as prototyping, research, or
long-running background jobs, [Coder Agents](./agents/index.md) is the
recommended way to delegate development work to coding agents in your Coder
deployment.
[Coder Tasks](./tasks.md) is an interface inside Coder to run and manage coding
agents with a chat-based UI. Unlike SaaS-based products, Coder Tasks is
self-hosted (included in your Coder deployment) and allows you to run any
terminal-based agent such as Claude Code or Codex's Open Source CLI.
Coder Agents is a native AI coding agent built into Coder. The agent loop runs
in the Coder control plane on your infrastructure rather than inside the
workspace, so workspaces can be completely network isolated. Developers
interact with agents through the web UI, the CLI (`coder agents`), or the
REST API.
![Coder Tasks UI](../images/guides/ai-agents/tasks-ui.png)
![Coder Agents chat interface with git diff sidebar](../images/agents-hero-image.png)
[Learn more about Coder Tasks](./tasks.md) for best practices and how to get
started.
[Learn more about Coder Agents](./agents/index.md) for architecture details,
supported LLM providers, and how to get started.
## Secure Your Workflows with Agent Firewall
## Govern AI activity with the AI Governance Add-On
AI agents can be powerful teammates, but must be treated as untrusted and
unpredictable interns as opposed to tools. Without the right controls, they can
go rogue.
AI coding tools are quickly becoming core to how engineering teams ship
software. As adoption grows, platform teams want a clear picture of how AI is
being used, consistent guardrails across teams, and predictable cost controls
so they can confidently scale AI tooling to the whole organization.
[Agent Firewall](./agent-firewall/index.md) is a new tool that offers
process-level safeguards that detect and prevent destructive actions. Unlike
traditional mitigation methods like firewalls, service meshes, and RBAC systems,
Agent Firewall is an agent-aware, centralized control point that can either be
embedded in the same secure Coder Workspaces that enterprises already trust, or
used through an open source CLI.
The [AI Governance Add-On](./ai-governance.md) is a per-user license that adds
observability, management, and policy controls for AI tooling across your
Coder deployment. It includes:
To learn more about features, implementation details, and how to get started,
check out the [Agent Firewall documentation](./agent-firewall/index.md).
- [AI Gateway](./ai-gateway/index.md) for centralized authentication, audit
trails of prompts and tool invocations, and policy enforcement against
upstream LLM providers.
- [Agent Firewall](./agent-firewall/index.md) for process-level network and
command policies that restrict what agents can reach and do inside a
workspace.
- Expanded Agent Workspace Build allowances for teams running AI-driven
background work at scale.
[Learn more about the AI Governance Add-On](./ai-governance.md) for use cases,
entitlements, and how to enable it in your deployment.