Subagents in Claude Code: Why Business Needs Them

What are subagents in Claude Code and why does business need them

Imagine this: you hired a smart employee, gave them a task, and instead of working they spend their time shuffling papers and waiting for your instructions at every step. That is exactly how a regular AI agent works without subagents — a powerful tool that gets bogged down in its own context and requires constant micromanagement.

Subagents in Claude Code change this picture dramatically. It is a mechanism in which one AI agent independently creates and delegates tasks to other agents — each working in its own isolated context without interfering with the main process. The result: parallel work, token savings, and significantly more complex tasks that AI can solve without human involvement.

In this guide, you will find everything an entrepreneur needs to know about subagent architecture: how it works, what problems it solves, and how to implement it in your business processes.

Why a regular AI agent stops coping

The context window problem

Any AI agent operates within a so-called context window — a limited amount of information that the model can retain at once. When an agent performs a long task, each intermediate step (searching, reading files, analyzing data) is added to that context. Very quickly, the context overflows.

And here is the paradox: even with models that have a million-token context, performance drops sharply as it fills up. This is a fundamental architectural problem — not a bug in a specific version, but a characteristic of transformer models. The agent retains information from the middle of the context less effectively, starts to "forget" details, and makes mistakes.

The "smart executor" problem

When a developer writes code, it is hard for them to be a tester at the same time — they are too immersed in implementation details. An agent suffers from the same problem. If one agent writes code, checks it, and oversees the architecture, it loses objectivity. Subagents with different roles and different contexts create a healthy "internal contradiction" that improves the quality of the result.

The micromanagement problem

Without subagents, a human turns into "the dumbest tool in the agent's arsenal" — literally shuffling files, opening new chats, and copying results from one window to another. This is not automation; it is manual work with a smart assistant.

How subagents work: the architecture from the inside

Subtask vs Subagent: what is the difference

It is important to distinguish between two terms that are often mixed up:

Subtask — running a task in a separate, clean context. No traces of previous work. The agent receives only what is needed for the specific task, completes it, and returns the result. System instructions, permissions, and the tool set are inherited automatically from the parent agent.

Subagent — running a task in a separate context with a specific, preconfigured agent. You define yourself which model to use, which tools to allow, which actions to prohibit, and which system prompt to apply. It is a fully-fledged specialized "employee" with its own working rules.

The difference is fundamental: a subtask is simply context isolation, while a subagent is also role specialization.

The Task tool under the hood

In Claude Code and similar agent systems, subagents are implemented through a special tool — Task. When the main agent calls it, the following happens:

1. A new, fully isolated context is created
2. Only the prompt with the task is passed into it (without the history of the main session)
3. The subagent performs the task using its own tools
4. The result (and only the result) is returned to the main context

All the "junk" from intermediate steps — files read, search queries, draft conclusions — remains isolated and does not clutter the main working context. The main agent sees only the clean final outcome.

Parallel work of subagents

One of the key benefits of the architecture is parallelism. The main agent can launch several subagents at once for different parts of the task. While one searches for information through GitHub CLI, another is simultaneously scanning web sources. Execution time is reduced many times over — exactly as it happens in real teams, where a task is divided among several performers.

Skills: how to dynamically manage prompts and tools

What is a skill

A skill is a prompt that is loaded into the agent's context only when needed. Imagine a library of instructions: the agent sees a short description of each skill, and when the task matches the description, it loads the full prompt and uses it.

This solves two problems at once that were previously addressed through subagents:

• Dynamic prompts: you do not need to keep all instructions in the permanent context — they are loaded on demand
• Heavy MCP tools: instead of keeping the giant GitHub MCP schema in context (which consumes tens of thousands of tokens), you can write a compact skill with the instruction "here is how to use the gh command from the terminal" — and the agent will load it only when GitHub is needed

Combining skills and subtask

The most powerful yet simple architecture looks like this:

1. The main agent understands that a research block is needed
2. It creates a subtask (a new isolated context)
3. Inside the subtask, it loads the needed skill (for example, "deep research")
4. The subagent works with full instructions, but in an isolated space
5. The result is returned to the main agent

This makes it possible to do without creating full-fledged agent folders with custom configurations in most cases.

When full-fledged subagents are still needed

It is worth creating a separate subagent (with a configuration file, model, and permissions) only in two cases:

1. Custom permissions. For example, a research agent that is forbidden to modify files — only read and search. Or a QA agent that can open a browser and run user scenarios, but cannot edit code.

2. Model selection. A specialized agent for working with confidential data that uses a local model instead of a cloud one — the data never leaves the company's perimeter.

Practical scenarios for business

Scenario 1: Automated QA

The main agent writes code and, when finished, launches a QA subagent. It opens a browser (via Playwright CLI), runs the specified user scenarios from MD files, and records what works and what does not. The result is returned to the main agent — it sees the list of issues, fixes them, and calls QA again. The cycle repeats until approval.

Key point: the QA agent cannot change code. Only test. This prevents situations where the agent "fixes" the tests to match the code instead of fixing the code to meet the requirements.

Scenario 2: A private agent for confidential data

There is data that cannot be sent to the cloud — financial reports, contracts, customers' personal data. A separate subagent is created on a local model (for example, via Ollama). The main agent delegates work with sensitive files to it. All processing remains on the local machine. Only the structured result is returned to the main context.

Scenario 3: Orchestrator and executors

An architecture that is as close as possible to a real team. The main agent-manager has no access to the code at all — only to the folder with tasks and documentation. Its only function is to formulate the task correctly and hand it off to the executor. The developer agent receives tasks and writes code, but cannot change the documentation or the task definition. The tester agent checks the result and cannot edit the production code.

This creates a system of checks and balances in which no agent can “cheat” — rewrite the tests instead of the code or change the requirements to fit its own capabilities.

Scenario 4: Parallel research

Need to gather information from several independent sources? We launch parallel subagents: one scans GitHub repositories via CLI, another searches the web, and a third analyzes internal documentation. Collection time is reduced dramatically, and the main agent’s context remains clean — it receives only the final digest.

Ralf Vigum Loop: an agent that does not stop until it gets results

One of the big problems with AI agents is that they like to “smooth over the edges.” Asked to write 10 tests — they write 2 and report, “everything is ready.” This pattern is especially characteristic of Claude models with their tendency toward tidy completions.

The solution is the so-called Ralf Vigum Loop. The principle is simple: the agent runs in a loop and works until the line COMPLETED

1. We create a detailed task plan in an MD file with a clear definition of done
2. We launch the agent with the instruction: “work according to this file; when you’re done, put COMPLETED at the end”
3. The loop launches the agent again if COMPLETED does not appear
4. The agent literally cannot “finish” the job without completing all the items

This removes manual micromanagement. Instead of periodically checking the chat and asking, “Are you sure you did everything?”, you simply wait for the final signal.

An important detail: for this approach to work, you need preconfigured infrastructure — linters, unit tests, end-to-end tests. The agent must have the ability to objectively verify that the result meets the requirements, rather than simply deciding, “I think I’ve done enough.”

Roles in a multi-agent system: do not overcomplicate

The Cursor team’s research revealed a non-obvious conclusion: for an effective multi-agent system, three roles are enough — not a whole “zoo of specialists.”

Manager (Planner) — studies the codebase, formulates tasks, checks results. Does not write code itself. Its only tool is proper task formulation.

Executor (Worker) — receives the task and does it. Does not think about architecture or reinterpret requirements. Simply implements.

Tester/Reviewer (Reviewer) — checks the result without access to implementation details. It is this isolation of context that makes its review objective.

Frontend engineers, backend engineers, architects, and DevOps in an agent system are unnecessary complexity that creates overhead without proportional benefit.

Why review is the main bottleneck in 2025-2026

The paradox of the AI-agent era: code is being generated faster and faster, while the quality of its verification becomes the main limitation. If review does not keep up with generation, contradictions, broken invariants, and technical debt accumulate in the codebase. Adding new functionality starts breaking existing functionality.

There are two ways to speed up review. First, use specialized tools (Codex CLI currently shows the best results in automated review). Second, do not fully trust automated review.

An experienced developer has something an agent does not: implicit context. Why a particular architectural decision was made three years ago. Which client required exactly this behavior. Which bug had already existed in this place and how it was fixed. This context is not embedded in the code — it lives in people’s heads. And it is precisely this that makes human review indispensable even in the presence of powerful AI tools.

A step-by-step implementation plan for an entrepreneur

Step 1: Start simple (Claude.md / agents.md)

There is no need to build a complex multi-agent architecture right away. Start with one agent and fill out the instruction file as you work. Record where it makes mistakes, which patterns repeat, and where additional rules are needed. This is your future foundation.

Step 2: Move repeating blocks into skills

Notice that the agent regularly performs the same sequence of three actions? Turn those three actions into a script and a skill with instructions on how to run it. The agent gets a ready-made tool instead of reinventing the wheel every time.

Step 3: Learn to delegate via subtask

Start by manually asking the agent to create separate contexts for heavy tasks: “Create a separate context, complete this research block, and return only the result.” Practice the delegation pattern and find your own successful formulas.

Step 4: Automate delegation

When the patterns are clear, write the rules into the agent instructions: in what situations it should create a subtask on its own, which skills to load. Now it does this without your involvement.

Step 5: Create specialized subagents (only if needed)

Have you reached tasks that require custom permissions or model selection? Only then create full-fledged subagents with configuration files. Not before.

Frequently asked questions

Are subagents expensive? Do tokens get used up faster?

Not necessarily. Yes, running several agents in parallel increases total token usage. But subagents also save tokens by isolating context: intermediate steps do not clutter the main context, do not require compaction, and do not degrade the quality of the main agent’s work. In practice, an architecture with subagents is often more efficient in terms of “cost/result” than a single agent with an inflated context.

How are subagents different from simply running multiple chats?

When managing multiple chats manually, the person becomes the “link” — moving results from one window to another, keeping the context in their head, and monitoring synchronization. Subagents automate this coordination: the main agent decides on its own when and what to delegate, receives the results on its own, and continues working on its own.

Are multi-agent systems where agents communicate with each other at the same level already a reality?

Technically — yes, the hype is huge. In practice — still unreliable. A tree-like architecture (an agent communicates only with its parent agent) works stably. Cross-interactions between agents at the same level are still experimental territory with unpredictable behavior.

Does two-level subagent nesting work?

Yes, and it is the most advanced production-ready scenario. The main agent creates a research subagent, which in turn splits the task among several parallel subagents. It works especially well for read-only tasks — parallel collection of information from independent sources does not create conflicts.

Which skills should be added first?

From practitioners’ experience: a skill for browser testing (Playwright CLI), a skill for working with the project’s memory/context (how to update and search for information in the project’s MD files), and a skill for deep research (deep research through the web and repositories). Also extremely useful is the skill of “how to properly delegate tasks to subagents” — by default, agents do this inefficiently.

Bottom line: what does this lead to

The subagent architecture is not technological hype, but a logical extension of how effective teams work. Role separation, context isolation, parallel work, proper delegation — all of this has long been working in human organizations. AI agents simply reproduce these patterns in the digital environment.

The main shift happening right now: the time an agent can work autonomously is growing every month. A year ago, an agent could confidently work for 10–15 minutes. Today — an hour and a half according to a pre-prepared plan. Tomorrow — more. And the one who masters the skill of properly assigning tasks, building infrastructure, and delegating will gain a disproportionate advantage.

An entrepreneur in this system stops being an “operator of a tool” and becomes what they are meant to be — a strategist who formulates tasks, defines the definition of done, and checks the result. Everything else is delegated.