ACP Agent Registry Practical Introduction Guide: 5 operating standards that must be fixed first when replacing Codex, Claude, and Copilot within the IDE

The reason JetBrains is bringing the Agent Client Protocol (ACP) agent registry to the forefront in the Spring 2026 release is simple. This is because the development team must now design “which task to entrust to which agent within which boundary” rather than “which AI to use.” This article explains why the flow of replacing Codex, Claude Agent, Copilot, and Cursor series agents within the IDE has become important, and how it should actually be translated into operational standards.

1. One-line problem definition

Key takeaway: Attaching agents to the IDE is not an installation issue, but rather an operational design issue of dividing permissions, context, and verification responsibilities.

The target audience is developers and leads who have already used AI coding tools, but are confused about what to fix first when raising it to a team standard. ACP standardizes the connection protocols between editors and agents, increasing the freedom of tool selection, but it also increases the operating standards that teams must set themselves.

The scope of this article is IDE-centric workflow. In other words, it deals with cases where a developer entrusts work to an agent in units of files, blocks, or branches while looking at the code context. Conversely, large-scale batch creation, fully unattended CI agents, and long-time remote execution environment design are only covered as secondary comparisons.

2. First, conclusion

Key takeaways: ACP-based multi-agent IDE workflows are a good fit for teams where “short fixes, quick reviews, and immediate human intervention” are important, while tasks that require long-term state maintenance and isolated execution are better suited to separate sandboxes.

The basic judgment I recommend is as follows.

• Teams that should adopt it now:Teams that are comparing multiple AI coding tools or want to standardize file context passing and approval flow within the IDE
• Teams with better observations:Teams that do not yet have code review standards, branch strategies, or test gates
• Separate paths are better when: When the output depends more on the execution environment, large filesets, or long resume operations than on code

In short, ACP makes it easy to change agents, but it does not take responsibility for quality. Therefore, the operation standard document comes first before installation.

3. Decomposition of core structure

Key takeaways: The value of ACP lies in standardizing the editor-agent interface rather than the model itself.

ACP's official introduction document describes this protocol as a standard interface between editors and agents, like LSP. The key is to avoid locking the IDE to any specific agent.

• Editor/IDE: This is the space where users actually work. The current file, selection area, project structure, and diff are checked here.
• ACP Client Layer: Common language that the IDE uses to talk to the agent. Carry UX units such as messages, plans, permission requests, and diffs.
• Agent runtime:Responsible for actual inference and tool invocation. In the JetBrains case, a runtime like Deep Agents is responsible for this layer.
• Tools/Execution Environment: This is the actual work layer where file reading/writing, shell commands, tests, and subagent execution occur.

This is why JetBrains' 2026.1 release is important. GoLand exposed support agents as One-click installation through the ACP Agent Registry, and made it possible to select Codex, Claude Agent, GitHub Copilot, and Cursor series within the same IDE. In other words, the IDE is starting to become a common workbench rather than a vendor-specific UI.

4. Explanation of design intent: Why is this structure necessary

Key summary: ACP is a structure designed to make “fixing the workflow” easier than “replacing the model”.

Looking at the case of JetBrains' ACP + Deep Agents, the planning phase corresponds almost 1:1 to ACP's agent plans, risk commands to permission requests, and session persistence to checkpointers. This is important. A good agent experience is more about operational elements like Plan, Approval, Resume than great one-shot prompts

The benefits of this structure are threefold:

1. Reduce tool replacement costs: Reduce the need to completely retrain your IDE workflow when changing agents.
2. Improved context passing accuracy: Instead of verbally describing the file path in the terminal, you can pass the currently viewed file and block directly.
3. Permission policy standardization: You can control on a per-session basis which commands are allowed immediately and which edits are approved.

On the other hand, there is also giving up. ACP alone does not address issues such as execution environment isolation, long-term file state, and external data mounting. This is the same reason why OpenAI's Sandbox Agents document explains harness and compute separately. A flow that is clearly visible within the IDE and a flow that safely isolates actual execution are two different issues.

5. Evidence and Comparison

Key summary: ACP IDE-type, terminal-type single agent, and sandbox-type agents are not substitutes for each other, but have different responsibilities.

Comparison criteria	ACP-based IDE workflow	Terminal-type single agent	Sandbox-type agent
Main Strengths	Pass file context, review diff, quick intervention	Simple and fast personal productivity	Run quarantine, maintain file state, resume
Suitable task	Review, refactoring, partial modification, parallel experiment	Personal coding, short implementation, exploration	Long-time work, data room/document set processing, artifact creation
Operation difficulty	Medium: Requires approval policy and team rules	Low: Personal rules are sufficient	High: Requires management of execution boundaries, mounts, and secrets
Failure pattern	Human review falls apart when permission criteria are ambiguous	Incomplete context delivery increases repetitive explanations	The cost of setting up the environment is large, so it is too much for small tasks
Recommendation status	When the team compares and standardizes multiple agents	When increasing productivity quickly alone	When it is a product-type workflow where the execution environment is more important than the result

My judgment is clear. The first stage of team introduction is the most realistic ACP-based IDE workflow. This is because it lowers agent replacement costs while leaving the code context review, which is best done by humans, within the IDE. However, as operation automation progresses to the second stage, sandbox-type execution separation becomes necessary.

6. Actual operation flow / step-by-step execution method

Key summary: It is much more stable to divide the person in charge by task type than to assign many agents.

In practice, it is better to start in the following order.

1. Define only 3 types of tasks first. Example: code description/navigation, limited modification, extensive refactoring.
2. Map one default agent to each task. Example: Copilot family for quick Q&A, Codex/Claude Agent family for long fixes.
3. Separates permission boundaries. Allows file modification, requires permission to run shell, separates package installations like separate permissions.
4. Fix the verification command. Example: which combination of pnpm lint, pnpm typecheck, pnpm test --runInBand should be passed. Document it.
5. Use branch/worktree strategy together. This is why JetBrains emphasized the separation of Git worktrees and AI tasks in GoLand 2026.1.

Example operating rules can start short as shown below.

1. First, only the file/block currently being viewed is skipped.
2. Modifications of 30 lines or less are reflected immediately after IDE approval.
3. For modifications exceeding 30 lines or multiple files, receive a work plan first.
4. Test command execution is permitted, but package installation is approved separately.
5. Do not directly modify the main branch; execute only on the worktree or feature branch.

The advantage of this method is that agent performance differences can be absorbed into team rules. Even if the model changes slightly, operational quality will not be significantly affected if work classification and approval structures are maintained.

7. Pitfalls

Key takeaway: Most failures come from poor boundary setting rather than model performance.

• Plot 1: When you think “it’s safe because it’s not in the IDE”
  Prevention: Require separate approval for shell execution, installation, file movement, and bulk modification.
  Recovery: Always allow per session Reexamine the scope and return critical orders to default deny.
• Pit 2: Leaving all roles to one agent
  Prevention: Separate exploration, modification, and review, at least logically.
  Recovery: View failed job logs to see which job type. Separate and remap if quality has wavered.
• Pitfall 3: When merging only by looking at diffs without verification commands
  Prevention: Minimum verification set for each task type must be fixed.
  Recovery: Now, not after a regression occurs. Require at least one of lint/typecheck/test.
• Pit 4: Trying to solve long-time tasks with IDE chat
  Prevention: Move document set processing, port exposure, and resumable execution to the sandbox.
  Recovery: Prompt for failed flows. Separate execution boundaries instead of holding on to improvements.

8. Strengths and Limitations

Key takeaways: ACP expands your team's options, but makes operational responsibilities clearer.

Strength is clear. Reduce agent replacement costs and communicate current file context more accurately while maintaining IDE-centric development habits. It also simplifies team onboarding documents by standardizing UX units like plans, approvals, and sessions.

marginIt is also clear. The presence of ACP does not automatically make agent quality homogeneous. Additionally, isolation of the actual execution environment, secret management, long-term state retention, and external storage mounting require separate design. So IIt is not recommended to immediately judge that “the agent operating system is over” just because ACP has been introduced.do.

9. Points to study more deeply

Key takeaways: Understanding permissions/session/execution boundaries is more important than understanding protocols when learning the next steps.

• Please read local/remote scenario differences in ACP official documentation first.
• See how agent plans and permission requests correspond in JetBrains' ACP + Deep Agents example.
• Read about separating harness and compute in the OpenAI Sandbox Agents documentation. Introduction to IDE helps you understand why the next step is separation of execution.
• Please document Git worktree, feature branch policy, and test gate first before actual team application.

10. Action Checklist + Author's Perspective

Key summary: The success criterion for ACP adoption is not “what model was used” but “what work was verified and at what boundary”.

• Has our team first divided the task types into three or fewer types?
• Have you determined the default agent and alternative agent for each task type?
• Have you separated approval policies for file modification, shell execution, and installation commands?
• Are there branch or worktree separation rules?
• Is the minimum pre-merge verification command documented?
• Is there a standard for transferring tasks that require long-time execution or external data mounting to a separate sandbox?

Definition of Done: The first round of introduction is complete when the default agent, approval policy, verification command, and branch strategy for one task type are fixed in a document.

My recommendation is clear. ACP should be used as a starting point for team operation standardization, not as an individual productivity experiment. Conversely, if there are no test gates and no branch rules yet, even if you add ACP, you will end up at the level of "an IDE that is easy to chat." In that case, it is better to first fix the task boundary and then introduce it.

Reference material

• Agent Client Protocol Introduction (Accessed: 2026-04-24)
• JetBrains: Using ACP + Deep Agents to Demystify Modern Software Engineering (Published in: 2026-04)
• JetBrains: GoLand 2026.1 Is Released (Published by: 2026-03-26)
• OpenAI: Sandbox Agents (Accessed: 2026-04-24)