Parallelism and Task Decomposition

Task decomposition is the process of breaking a large goal into smaller subtasks. Parallelism is the process of executing independent subtasks at the same time. Together, they allow agents to handle larger workloads, reduce latency, and organize complexity.

A single agent can work sequentially, but many tasks contain independent parts. For example, if a user asks for a comparison of three tools, the agent can research each tool separately before combining the results.

Why decompose tasks?

Decomposition helps because large goals are often too vague to execute directly.

User goal:

Create a competitive analysis of three AI coding assistants.

Decomposed subtasks:

1. Identify evaluation criteria.
2. Research Assistant A.
3. Research Assistant B.
4. Research Assistant C.
5. Compare strengths and weaknesses.
6. Write final recommendation.

This structure improves coverage. It reduces the chance that the agent forgets a candidate or mixes criteria.

Types of decomposition

Decomposition can be based on:

• Entities: research each product, company, document, or file separately.
• Stages: gather, analyze, draft, review.
• Skills: research, code, test, write, critique.
• Data partitions: process chunks of a dataset or document.
• Workflow branches: handle different cases based on conditions.

The right decomposition depends on the task.

Parallel execution

If subtasks are independent, they can run in parallel.

Example:

Research Tool A ┐
Research Tool B ├── Merge findings → Compare → Final answer
Research Tool C ┘

Parallelism can reduce latency, especially when tools involve network calls or document retrieval.

Pseudocode:

async def compare_tools(tools):
    research_tasks = [research_tool(tool) for tool in tools]
    findings = await gather(*research_tasks)
    comparison = synthesize_findings(findings)
    return comparison

This architecture is useful for research agents, data-processing agents, multi-document summarizers, and evaluation systems.

Map-reduce pattern

A common architecture is map-reduce.

Map: process each item independently
Reduce: combine outputs into a final result

For document summarization:

Document chunks
  ↓
Summarize each chunk in parallel
  ↓
Combine chunk summaries
  ↓
Create final summary

Example:

def map_reduce_summarize(chunks):
    partials = parallel_map(summarize_chunk, chunks)
    final = summarize_summaries(partials)
    return final

This can handle documents that exceed a model’s context window. The risk is that details may be lost during summarization, so the reduce step should preserve important facts, conflicts, and citations.

Merge problems

Parallel outputs must be merged carefully. Subagents may use inconsistent formats, duplicate information, or disagree.

A merger should check:

• Are all subtasks complete?
• Are outputs in the expected format?
• Are there contradictions?
• Which sources are more authoritative?
• Are confidence levels stated?
• Are there gaps requiring another pass?

A structured merge format helps:

{
  "item": "Tool A",
  "strengths": ["fast setup", "good IDE integration"],
  "weaknesses": ["limited enterprise controls"],
  "evidence": ["official docs", "pricing page"],
  "confidence": "medium"
}

When every subtask returns the same schema, synthesis is easier.

Dependency management

Not all tasks can run in parallel. Some depend on earlier results.

Example:

1. Search for relevant documents.
2. Fetch selected documents.
3. Extract facts from fetched documents.
4. Write answer.

Step 2 depends on step 1. Step 3 depends on step 2. Trying to parallelize everything can produce errors.

A task graph can represent dependencies:

A: identify candidates
B: research candidate 1 depends on A
C: research candidate 2 depends on A
D: research candidate 3 depends on A
E: synthesize depends on B, C, D

This graph supports parallelism where safe and sequencing where required.

Parallelism tradeoffs

Parallelism is not free. It can increase:

• API cost
• Tool load
• Rate-limit pressure
• Coordination complexity
• Merge difficulty
• Debugging complexity

Use parallelism when tasks are independent and the latency savings or coverage improvements justify the cost.

Practical example: code review agent

A code review agent might decompose review into parallel checks:

Security reviewer: look for injection, secrets, unsafe permissions.
Performance reviewer: look for slow queries or inefficient loops.
Style reviewer: check readability and conventions.
Test reviewer: check coverage and missing cases.

The final reviewer merges these findings into one report.

This works because each reviewer has a distinct lens. It would be less useful if four agents all performed the same generic review.

Practical takeaway

Task decomposition gives agents structure. Parallelism gives agents speed. But both require careful merging, dependency tracking, and evaluation.

Use decomposition to make large goals manageable. Use parallelism only when subtasks are truly independent or can be safely merged. The architecture should make the task clearer, not merely more complicated.