Task Decomposition Strategies for Planning in Large Language Model Agents

Large language models (LLMs) are powerful, but they aren’t magic. Ask one to write a legal contract, analyze a year’s worth of financial data, or answer a multi-part question about a scientific paper-and it often stumbles. Not because it’s dumb, but because it’s trying to do too much at once. That’s where task decomposition comes in. Instead of throwing the whole problem at the model, you break it down into smaller, bite-sized pieces. It’s like giving someone a recipe instead of asking them to invent a meal from scratch.

Why Task Decomposition Matters for LLM Agents

LLMs have a hard time with tasks that require more than a few steps. Their context windows are limited. They forget earlier parts of a long chain. They hallucinate when under pressure. In 2024, benchmarks like SATBench and Spider showed that even the best models failed on over 60% of complex, multi-step problems when given a single prompt. But when those same tasks were broken into subtasks, accuracy jumped by up to 40 percentage points. That’s not a small win-it’s the difference between a prototype that works in a demo and a system you can trust in production.

The core idea is simple: complex tasks are hard because they’re messy. Decomposition turns a tangled knot into a series of clean, manageable loops. Each subtask is focused, short, and designed to be solved by a single LLM call. This reduces cognitive load, cuts down on errors, and makes it easier to fix things when they go wrong.

How Task Decomposition Works in Practice

There’s no one-size-fits-all way to break down a task. But several proven strategies have emerged. Here are the most effective ones used today.

• Decomposed (DECOMP) Prompting: This method assigns each subtask to a specialized handler. For example, if you’re building a customer support bot that needs to check order status, pull up shipping details, and suggest a replacement, DECOMP would split those into three separate prompts. Each one is given clear instructions and context. The agent then stitches the answers together. Developers using this approach on Reddit reported a 32% drop in hallucinations after implementing it.
• Recursion of Thought (RoT): Think of this as a loop. Instead of trying to solve a complex math problem all at once, the model solves a small part, then feeds the result back into itself to solve the next part. It’s especially useful for multi-digit arithmetic or recursive logic. One FinTechDev user on HackerNews saw their error rate drop from 22% to 8% using RoT for financial forecasting-though latency went up by 400ms per query.
• Chain-of-Code (CoC): When reasoning needs calculation, let code do the math. CoC combines language reasoning with code execution. The LLM writes a short Python script to compute a value, runs it, and uses the result in the next step. This outperformed standard chain-of-thought by 18.3% on math benchmarks, because it avoids the model’s weak arithmetic skills.
• Task Navigator: Designed for multimodal tasks (like analyzing images), this method breaks down visual questions into smaller, answerable sub-questions. If you ask, “What’s the total cost of items in this receipt?”, it might first ask, “What items are visible?”, then “What are their prices?”, then “Sum them up.” This approach improved accuracy by 22.7% on visual reasoning tasks compared to monolithic models.
• ACONIC: Introduced in early 2025, ACONIC is the most mathematically rigorous approach. It treats tasks as constraint satisfaction problems and uses a metric called treewidth to measure complexity. It then decomposes the task to minimize local complexity while keeping the overall goal intact. On the Spider benchmark (database queries), ACONIC improved success rates by 40% over chain-of-thought methods. It’s not easy to implement, but for structured tasks like SQL generation, it’s unmatched.

When Decomposition Shines-and When It Fails

Not every problem needs decomposition. In fact, forcing it on simple tasks makes things worse.

Decomposition works best when:

• The task has clear, sequential steps (e.g., “Generate a report from this dataset”)
• Subtasks can be handled independently (e.g., data extraction, analysis, summarization)
• You’re dealing with structured data (databases, spreadsheets, code)
• Accuracy matters more than speed

It struggles when:

• Subtasks are deeply interdependent (e.g., creative writing where each paragraph relies on the tone of the last)
• You need real-time responses (decomposition adds 35% average latency)
• The task is too vague or open-ended (e.g., “Write a novel”)
• There’s no clear way to define subtask boundaries

A 2025 analysis from ApX Machine Learning found that decomposition succeeded in 89% of database querying tasks but only 67% of creative writing tasks. That’s not a flaw in the method-it’s a sign that you need to match the strategy to the problem.

Cost, Speed, and the Hidden Trade-Offs

One of the biggest selling points of decomposition is cost savings. Amazon Science reported in March 2025 that using smaller, cheaper LLMs with decomposition for website generation cut infrastructure costs by 62% compared to running a single large model. That’s huge for startups and enterprises alike.

But here’s the catch: decomposition isn’t free. You’re trading computational cost for engineering cost. Setting up a robust decomposition workflow takes time. Developers on GitHub say it takes 2-4 weeks to get the subtask boundaries right. One user spent three weeks tweaking prompts before their customer support bot stopped misrouting requests.

And then there’s coordination overhead. If you split a task into 10 subtasks, you now have 10 LLM calls to manage, 10 outputs to validate, and 10 chances for something to go wrong. Too many fragments break the flow. As the Amazon Science blog warned: “Excessive decomposition leads to diminishing returns.”

The sweet spot? Between 3 and 7 subtasks per task. More than that, and you’re adding complexity faster than you’re gaining reliability.

Real-World Implementation: What Works in the Field

You don’t need to build everything from scratch. Frameworks like LangChain and LlamaIndex have made decomposition accessible. LangChain’s decomposition module, updated in May 2025, now supports parallel execution-meaning multiple subtasks can run at once instead of waiting in line. Users report setup time dropped from 80 hours to 25 hours.

Here’s how a real team used it:

A healthcare startup wanted to automate patient intake summaries from doctor’s notes. Instead of asking the LLM to extract symptoms, diagnoses, and treatment plans all at once, they broke it into three steps:

1. Extract all medical terms from the note.
2. Classify each term as symptom, diagnosis, or medication.
3. Generate a structured summary using the classified data.

The result? A 41% improvement in accuracy and fewer legal risks from misinterpretations. The team used LangChain’s built-in context summarization to pass key info between steps, solving the biggest pain point: losing track of details across subtasks.

On the flip side, a marketing team tried using decomposition to generate ad copy variations. They split it into 12 subtasks: “Write 5 headlines,” “Generate 3 CTAs,” “Check tone consistency,” etc. It took 11 seconds per output. The final product felt robotic. They scrapped it and went back to a single prompt with better examples.

Common Pitfalls and How to Avoid Them

Even experienced teams mess this up. Here are the top mistakes-and how to dodge them:

• Over-decomposing: Don’t split a task just because you can. If a subtask is too small, the LLM loses context. Ask: “Can this be done in one clear step?”
• Ignoring context flow: Each subtask needs the right background. Use summarization or memory buffers to pass key info forward. Without it, the agent forgets what it’s trying to accomplish.
• Not testing edge cases: What happens if subtask 2 fails? Does the system retry? Fall back? Alert a human? Design error handling from day one.
• Assuming all models are equal: A smaller model might be perfect for data extraction but terrible at summarization. Match the model to the subtask.
• Skipping documentation: Decomposition workflows are complex. If no one knows how they work, they’ll break when someone else touches them. Keep a simple flowchart.

What’s Next for Task Decomposition?

The field is moving fast. In June 2025, ACONIC got an update that auto-calculates treewidth-making it easier to apply without deep math knowledge. LangChain added parallel execution. Google Research announced plans for automated decomposition boundary detection, meaning the system will learn how to split tasks on its own.

The future isn’t just about better tools-it’s about smarter patterns. The Fast-Slow-Thinking approach, introduced in April 2025, mimics how humans solve problems: fast intuition for simple parts, slow deliberate reasoning for hard ones. This hybrid model is already being tested in enterprise systems.

By 2026, experts predict decomposition will be as standard as using APIs. MIT Technology Review found that 83% of AI leads expect it to be a core part of LLM architecture within 18 months. The question isn’t whether you’ll use it-it’s whether you’ll use it well.

Frequently Asked Questions

Next Steps for Developers

If you’re new to task decomposition:

1. Pick a task that’s currently failing-something with a 30%+ error rate.
2. Break it into 3-5 clear subtasks. Write each one as a standalone instruction.
3. Use LangChain to chain them together. Start with sequential execution.
4. Measure accuracy and latency before and after.
5. Iterate. Adjust subtask boundaries. Add context summarization. Try parallel execution.

If you’re already using decomposition:

• Look for subtasks that are too small or too vague.
• Check if you’re using the right model for each step.
• Build error recovery: retry, fallback, or human override.
• Document your workflow. Share it with your team.

Task decomposition isn’t a silver bullet. But for complex LLM applications, it’s the closest thing we have to a reliable foundation. The best agents don’t just think-they plan. And planning starts with breaking things down.