AI Orchestration Revolution: Development Automation Through Boomerang Mode

The Orchestration Burden I Didn't Notice

After introducing Memory Bank in my previous article "Efficient Collaboration with AI that Resets Memory for Each Task", I've been using it in my own projects. The "AI memory reset problem" was certainly solved. However, as I continued with daily development, I noticed a new challenge: I was spending more time than I realized on "creating prompts for the next task."

This orchestration between tasks was actually creating a significant cognitive load. I hadn't realized this fact until I started using Boomerang Mode. I had been automatically repeating the cycle of "giving instructions to AI," "AI working," "reviewing results," and "thinking about the next instruction."

The moment I tried the newly released Boomerang Mode in Roo Code¹², I thought, "Ah, this is exactly what I've been wanting." What had previously been my manual job—giving instructions for the next task between tasks—was now something the AI could think about on its own.

This wasn't just a convenient feature; it fundamentally changed my development flow. On the first day I used Boomerang Mode for a project, I noticed that time I would normally spend thinking about and modifying prompts was strangely available. This sense of "liberation" exceeded my expectations.

In this article, I'll explore the innovative nature of Boomerang Mode, its practical application, and how it complements the Memory Bank I introduced previously. Let's aim for a state where we can truly "entrust" tasks to AI in the fullest sense.

What is Boomerang Mode: Autonomous Task Management by AI

Boomerang Mode is an innovative feature introduced in Roo Code v3.8.0 in March 2025. Its essence lies in "autonomous task management by AI."

In traditional collaborative development, humans needed to think about "what should be done next" and assign tasks to AI. With Boomerang Mode, however, you only need to communicate the big goal, and AI will divide it into appropriate steps and execute them on its own.

This feature liberates us from the paradoxical situation of "working for the AI" in AI collaboration. In traditional AI collaboration, while AI was an excellent worker, deciding "what should be done next" was always the human's job. Boomerang Mode represents a dramatic shift, transferring this "orchestration" burden to AI and allowing humans to focus on more creative thinking.

How Boomerang Mode Works and Practical Application

Let's look at how Boomerang Mode operates, its internal structure, and practical usage methods.

Hierarchical Structure of Parent and Child Tasks

The most distinctive feature of Boomerang Mode is its hierarchical structure of "parent tasks (orchestrator)" and "child tasks (subtasks)."

The parent task (orchestrator) understands the big goal and acts as a conductor, dividing it into appropriate subtasks. It monitors the progress of child tasks and ultimately integrates the results to grasp the overall picture.

Child tasks (subtasks) focus on specific specialized work (design, implementation, testing, etc.). Each child task is executed in an independent context and returns a summary of results to the parent upon completion.

In actual use, this structure has allowed me to provide only high-level instructions. From a single statement like "implement a new feature," the AI automatically creates an appropriate workflow. The AI takes over judgments such as "first design, then implement, and finally test."

Automatic Processing of new_task and attempt_completion

Another important feature of Boomerang Mode is that AI automatically handles the use of two tools, new_task and attempt_completion³.

• new_task: A tool for the parent task to generate child tasks
• attempt_completion: A tool for child tasks to complete and return results to the parent

Users don't need to explicitly use these tools. Roo Code automatically handles them internally. For example, when I instructed in Boomerang Mode, "Investigate the visual testing issue," the following subtask was automatically generated:

# Analysis of Current Visual Test Setup

Please execute the following task:

1. Investigate current Storybook configuration and analyze integration with Next.js application
2. Examine existing story files and identify implementation issues
3. Check Playwright's visual test configuration
4. Identify main issues in connection with Next.js App Router

This analysis will serve as foundation information for actual fixes in subsequent tasks.

When completing the task, please use the `attempt_completion` tool to summarize the analysis results.
These results will be input information for the next task, "Specific Difference Verification and Fixes."

What's noteworthy is that at the end of this subtask instruction, the AI automatically directs the use of attempt_completion. This means that when a child task is completed, its results are automatically returned to the parent task, which then generates the next subtask based on those results. This cycle runs automatically without any intervention from me.

This automated cycle of "task generation → execution → result collection → next task generation" achieves liberation from the orchestration burden.

Information Flow and Context Management

The flow of information in Boomerang Mode has some very interesting characteristics.

The most important point is that only summaries are returned to the parent task, not the detailed processes of child tasks. This allows for:

1. Context separation and focus - Each child task can concentrate on its own purpose without being distracted by details of other child tasks
2. Conciseness in parent task dialogue - The parent task's dialogue history always consists of high-level summaries and doesn't become cluttered
3. Appropriate abstraction of information - Details are contained within child tasks, and only information necessary for decision-making is conveyed to the parent

This structure has enabled me to efficiently progress with complex projects without losing sight of the big picture.

Actual Usage Experience: Sense of Liberation and New Challenges

What I felt most strongly when actually using Boomerang Mode was a "sense of liberation." Let me give an example.

When planning to introduce visual regression testing in a Next.js project, I simply communicated that "We need a testing foundation to ensure visual consistency during refactoring." The AI then broke this down into steps and proceeded with the work on its own:

1. Analysis of current Storybook and Playwright configurations
2. Identification of page components to be tested
3. Proposal of necessary configuration files
4. Formulation of test execution procedures

The AI automatically generated and sequentially executed each of these steps as subtasks without my instruction. I only needed to review the final results and summaries of subtasks, freed from having to give instructions on "what to do next."

What I learned from this experience is that the "cognitive load of thinking about the next task," which I wasn't consciously aware of, was actually significant. Even when I thought I was delegating work to AI, the responsibility to constantly think about the next step and communicate it appropriately to AI still rested with humans. Boomerang Mode removed this burden, allowing me to focus more on creative thinking and important design decisions.

However, I also noticed that this autonomous orchestration brings new challenges. The more autonomous the AI becomes, the harder it is to identify the cause when "something starts to go off track."

For instance, when I gave instructions about Storybook and Playwright integration, Boomerang Mode created subtasks on its own and executed them one after another. However, looking at the final result, the handling of some issues related to compatibility with Next.js App Router was insufficient. Identifying which subtask caused the deviation in direction and why it happened became more difficult than before. This highlighted a new challenge: the "trade-off between autonomy and transparency."

Nevertheless, overall, I've been able to manage complex tasks more efficiently, dramatically improving my development experience. The transition from "working for AI" to "truly entrusting to AI" has brought more value than I imagined.

Complementary Relationship Between Boomerang Mode and Memory Bank

While Boomerang Mode excels at task management, Memory Bank, which I introduced in a previous article, is strong in knowledge inheritance between sessions. These two solve different problems, but combining them creates an even more powerful AI collaborative environment.

Boomerang Mode focuses on "task division and execution management." It breaks down one complex user instruction into multiple smaller subtasks and efficiently completes work by executing each in an appropriate mode. However, when that session ends, the detailed history and knowledge there are essentially reset.

On the other hand, Memory Bank focuses on "knowledge accumulation and inheritance." It stores important information and decisions about the project in a structured form and maintains consistent context across different sessions. However, it lacks the functionality for automatic task division and execution management.

In my actual experience, Memory Bank allowed me to maintain a continuous context for the project, but judging "which task should be done next" and creating instructions remained my job. Conversely, Boomerang Mode automatically divides and executes tasks but cannot inherit knowledge across sessions. It's clear that the weaknesses of each complement the other.

By integrating these, an ideal environment can be realized where "tasks can be automatically divided and executed while inheriting project knowledge." Specifically, an effective method is to load Memory Bank contents during Boomerang Mode initialization, allowing task division and execution management with an understanding of important project information.

What surprised me most was being freed from the "cognitive load of thinking about the next task," which I wasn't even aware of. By being able to entrust to AI the role of "orchestration" that I had been taking for granted, I can now focus more on creative aspects and essential design decisions.

Challenges of Autonomy and Finding Balance

While Boomerang Mode's autonomous task management is powerful, it's not optimal for all situations. In particular, I've come to think deeply about the challenge of the "trade-off between autonomy and transparency" that I faced.

From my experience, I've found the following situation-specific approaches to balancing effective:

1. Exploratory new development

   • Prioritize higher transparency
   • Confirm and adjust direction after each subtask completion
   • Set frequent checkpoints until the direction is established

2. Routine implementation work

   • Allow high autonomy
   • Check only the final results
   • Completely entrust implementation of well-known patterns

3. Scenarios requiring important design decisions

   • Moderate autonomy with high transparency
   • Confirm once after design proposal presentation
   • Proceed with implementation autonomously after approval

In other words, to master Boomerang Mode, it's important to be able to adjust "what balance of autonomy and transparency is appropriate" according to the nature of the project and task.

Practical Tips and Future Possibilities

Here are some practical tips for using Boomerang Mode.

How to Give Effective Initial Instructions

The quality of initial instructions is crucial to maximize the effect of Boomerang Mode. Methods I've found effective are as follows:

1. Clearly define goals and deliverables

   • Specifically communicate "what you want to achieve"
   • Specify the format and requirements of expected deliverables

2. Communicate constraints

   • Technologies that should be used and conventions to be followed
   • Approaches to avoid and points of caution

3. Indicate degree of freedom in judgment

   • How much judgment to delegate to AI
   • At which points human judgment should be sought

For example, rather than a vague instruction like "Introduce visual testing," it's more effective to communicate specifically like this:

"I'd like you to build a test foundation to ensure visual consistency during refactoring of a Next.js app. Focus on the article detail page and top page as key test targets, and consider compatibility with App Router."

Or:

"I'd like you to build a test foundation with Storybook and Playwright to ensure visual consistency during refactoring of a Next.js app. Focus on the article detail page and top page as key test targets, and consider compatibility with App Router."

Future Outlook: A New Stage of AI Collaboration

The direction shown by Boomerang Mode gives us a glimpse of the future of AI collaborative development. Anticipated developments include:

1. More advanced self-correction ability

   • Detecting and correcting directional deviations on its own
   • Improving accuracy by learning from human feedback

2. Evolution to multi-agent systems

   • Multiple specialized AIs working cooperatively
   • Management of more complex entire projects

3. Dynamic adjustment of autonomy level

   • Automatically adjusting transparency according to the nature of the work
   • Automatically confirming with humans when important decisions are needed

In the longer term, there's also the possibility that AI itself will handle project management, automating task prioritization and human resource (AI agent) allocation. This would shift the human role more toward strategic decision-making and creative problem-setting.

Conclusion: Liberation from Burdens I Didn't Notice

The greatest value of Boomerang Mode is liberation from the "orchestration burden" that I wasn't aware of myself. We had unconsciously accepted the cognitive load of always giving instructions to AI, seeing the results, and thinking about the next instruction. But now that AI can take on that role too, I can focus more on creative aspects and essential design decisions.

Of course, there are new challenges that come with complete autonomy. A new consideration issue has also emerged: the "balance between autonomy and transparency." However, just being freed from this hidden burden of "orchestrating instructions" has greatly changed my experience with AI collaborative development.

We've now taken one step closer to the ideal form of collaboration with AI that "works autonomously while having memory." By combining complementary technologies like Boomerang Mode and Memory Bank, we can get even closer to that ideal.

I hope you'll try Boomerang Mode too. I wish to share that moment of surprise when you realize, "This is exactly what I've been wanting."