Agents and Sub-Agents: How AI Splits Complex Tasks

First: What is an agent?

In a normal chat it’s like a phone call: we ask, the AI answers, we ask again. That works fine for one-off questions – but with multi-step tasks it gets tedious, because we have to steer every step ourselves.

An agent works differently: we describe the goal, and the AI plans the steps on its own. It reads files, researches, calculates – and delivers the result at the end. We watch and step in when needed.

Chat mode

We ask, the AI answers – ping-pong, one step at a time.

Agent mode

We describe the goal. The AI plans the steps, executes them and delivers the finished result.

Back to the cost analysis example: an agent could work through all five locations on its own – but it does so one after the other. With larger tasks, the context keeps filling up and quality drops towards the end. This is exactly where sub-agents come in.

So what are sub-agents?

A sub-agent is an agent that is started by another agent. For a larger task, the main agent breaks it into independent parts and launches a dedicated sub-agent for each one – and those sub-agents then work at the same time.

Each sub-agent has:

Its own context (its own “working memory” – it only sees what it needs for its part of the task)
Access to the same tools (files, web research, etc.)
A clear goal that it pursues independently

At the end, each sub-agent hands its result back to the main agent, which consolidates everything.

An example

It looks like this:

“Analyze the cost development of our five locations for Q4. For each location: personnel costs, material costs, variance to budget, and the three largest cost drivers.”

Without sub-agents, the AI works through one location after another – and quality drops towards the end because the context keeps filling up.

With sub-agents, five analyses run in parallel. Each sub-agent has a clean context and focuses on just its location. The main agent consolidates the results and identifies cross-location trends. What would take 12–15 minutes in sequence is done in 3–4 minutes.

Core principle: Sub-agents pay off whenever a task can be split into independent parts. If part B depends on part A, the AI has to work sequentially – and rightly so.

When are sub-agents worth it?

Most AI tools decide themselves when sub-agents make sense. Typical situations:

Supplier comparison

Analyzing three offers at the same time: prices, terms, payment conditions – and a recommendation at the end.

Month-end close reports

A dedicated report per cost center – independent of one another, in parallel instead of one after the other.

Budget variance analysis

Evaluating multiple departments independently and identifying patterns that only emerge in comparison.

Invoice review in batches

Reviewing large invoice stacks in chunks and consolidating any anomalies.

How to trigger sub-agents on purpose

We don’t need to know any technical commands. Keywords like “each”, “in parallel”, “simultaneously” or “for every” signal to the AI that the work can be done at the same time. Even more reliable: ask for sub-agents directly.

Well phrased

“Analyze these 4 quarterly reports each individually and then summarize the trends.”

Direct instruction

“Launch a dedicated sub-agent for each cost center that analyzes actuals, budget, and the top 3 variances.”

Why it pays off

Higher quality

Each sub-agent has its own context and focuses fully on one sub-task. A single agent working through five topics in sequence loses a bit of focus with every new topic.

Speed

With five independent sub-tasks, processing time can drop by a factor of 3–5.

Watch the token usage

Sub-agents use more tokens than a single agent because each one builds up its own context. Five sub-agents use roughly 2–3× the tokens. On flat-rate subscriptions from the major AI providers, that’s included in the monthly limit.

When sub-agents are not the right fit

Not every task benefits from parallel execution:

Tasks with dependencies – if step 2 needs the result of step 1, the work has to run sequentially
Short, simple tasks – writing a single email doesn’t need a sub-agent
Creative work with a common thread – a continuous piece of writing should come from one agent, so style and argument stay consistent

In general: the more steps an agent takes on its own, the more important our oversight becomes. Sub-agents are not autopilot – they’re like a team we delegate to, but whose results we still review. Especially for financial data, we should always sanity-check the main agent’s summary against the source data.

Where do sub-agents exist?

Sub-agents have arrived in every major AI tool by now – the implementation differs, but the core principle is the same:

Claude Cowork – Claude’s interactive work mode, where it can read files, research the web, and work on tasks over longer stretches. Starts sub-agents automatically for complex tasks. Available on all paid Claude plans (Pro, Max, Team, Enterprise)
ChatGPT – ChatGPT’s agent mode uses parallel workstreams, and Codex Subagents (for developer tasks) distributes work across specialized child agents
Google Gemini – Gemini Enterprise and the developer tools support multi-agent workflows with parallel sub-agents
Microsoft Copilot – Copilot Studio uses an orchestrator/sub-agent pattern where a main agent coordinates specialized child agents
Claude Code / Cursor / Windsurf – developer tools that use sub-agents for parallel coding tasks

The details vary by tool, but the underlying principle is always the same: break complex tasks into independent parts, process them in parallel, and consolidate the results.