Hierarchical Task Decomposition - joehubert/ai-agent-design-patterns GitHub Wiki

Home::Overview of Patterns

Classification

Architectural Pattern

Intent

To organize complex tasks into structured hierarchies with different levels of abstraction and specialized sub-agents for specific sub-tasks, enabling efficient problem-solving through methodical breakdown of large problems into manageable components.

Also Known As

  • Task Decomposition Hierarchy
  • Hierarchical Planning
  • Recursive Task Breakdown
  • Divide-and-Conquer Agent Architecture

Motivation

Complex AI tasks often require multiple steps and different types of reasoning or domain knowledge. Traditional approaches may attempt to solve these problems in a single pass, which can lead to:

  • Overwhelmed models trying to handle too many considerations at once
  • Shallow or incomplete solutions that miss important details
  • Difficulty maintaining context across multiple reasoning steps
  • Inefficient use of resources by applying powerful models to simple sub-tasks

For example, when an AI application is tasked with "research and create a comprehensive business plan for a sustainable fashion startup," this involves market research, competitive analysis, financial projections, operational planning, marketing strategy, and sustainability considerations. A single agent would struggle to maintain focus and depth across all these domains.

Hierarchical Task Decomposition addresses this by breaking the problem into a structured tree of sub-tasks, with specialized agents or processes handling each component according to its requirements. This allows for depth in specific areas while maintaining coherence across the overall solution.

Applicability

When to use this pattern:

  • For complex, multi-faceted tasks that span multiple domains or expertise areas
  • When different sub-tasks require different capabilities, tools, or knowledge bases
  • When maintaining coherent context across a long chain of reasoning is challenging
  • For problems that naturally decompose into hierarchical structures
  • When you need explicit tracking of task dependencies and relationships
  • When explainability and auditability of the process are important requirements

Prerequisites:

  • A mechanism for task classification and decomposition
  • Ability to route sub-tasks to appropriate specialized agents
  • Methods for aggregating and integrating results from sub-tasks
  • Clear interfaces between levels of the hierarchy

Structure

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Components

The key elements participating in the pattern:

  • Task Manager: Responsible for initial task analysis, decomposition planning, and orchestrating the overall process. It determines how to break down complex tasks and monitors progress.

  • Decomposition Planner: Analyzes tasks and creates structured hierarchies of sub-tasks with explicit dependencies and sequencing. It decides the appropriate level of granularity for each level of the hierarchy.

  • Specialized Agents: Domain-specific or function-specific agents that handle particular types of sub-tasks. These may include:

    • Research agents
    • Reasoning agents
    • Creative agents
    • Data analysis agents
    • Evaluation agents

  • Aggregation Processor: Combines and synthesizes results from multiple sub-tasks into coherent higher-level outputs. It resolves conflicts between sub-task outputs and ensures consistency.

  • Context Manager: Maintains relevant context across the hierarchy, ensuring that sub-agents have the information they need without being overwhelmed by irrelevant details.

  • Task Registry: Tracks the status, dependencies, and relationships between all tasks and sub-tasks in the hierarchy.

Interactions

How the components work together:

  1. The Task Manager receives a complex task and works with the Decomposition Planner to analyze it and create a hierarchical breakdown.

  2. The Decomposition Planner creates a structured task tree with explicit parent-child relationships, dependencies, and execution order.

  3. The Task Manager schedules execution based on the plan, dispatching top-level tasks first.

  4. When a parent task needs execution, it may further decompose into child tasks or be routed to an appropriate Specialized Agent.

  5. Specialized Agents execute their assigned tasks, which may involve:

    • Requesting further decomposition if the task is still too complex
    • Using tools or external resources
    • Generating outputs for their specific sub-tasks

  6. The Aggregation Processor combines the outputs from child tasks to fulfill their parent task requirements.

  7. The Context Manager ensures that each agent has access to relevant context from parent tasks and sibling tasks.

  8. The process continues recursively until the entire task hierarchy is complete.

  9. The Task Registry maintains the state of all tasks, allowing for monitoring, debugging, and explaining the overall process.

Consequences

The results and trade-offs of using the pattern:

Benefits

  • Improved quality of solutions through specialized handling of different aspects
  • Enhanced clarity through explicit task structures and dependencies
  • Better resource allocation by matching task complexity to appropriate agents
  • Increased reliability as sub-tasks can be independently verified and corrected
  • Improved explainability by providing a clear record of how complex problems were solved
  • Scalability to very complex problems that would overwhelm single-agent approaches

Limitations

  • Overhead in planning, coordination, and context management
  • Potential fragmentation if decomposition is too granular
  • Dependency management becomes more complex as the hierarchy grows
  • Integration challenges when combining results from multiple specialized agents
  • Initial setup costs for defining specialized agents and decomposition rules

Performance implications

  • May increase total processing time due to sequential dependencies between levels
  • Can improve throughput by allowing parallel processing of independent sub-tasks
  • Memory requirements increase with the depth and breadth of the task hierarchy
  • Response latency for end-users may increase due to the multi-stage process

Implementation

Guidelines for implementing the pattern:

  1. Start with a clear taxonomy of task types and complexity levels to guide decomposition decisions.

  2. Define interfaces between hierarchy levels with explicit contracts for inputs and outputs.

  3. Implement a flexible scheduling mechanism that can handle dependencies and prioritize tasks appropriately.

  4. Create specialized agents with clear strengths and limitations:

    • Domain-specific agents for particular subject areas
    • Function-specific agents for particular types of reasoning or processing
    • Tool-specific agents for particular external capabilities

  5. Design aggregation strategies that can intelligently combine outputs from sub-tasks:

    • Simple concatenation for independent sections
    • Integration strategies for interdependent outputs
    • Conflict resolution when sub-tasks produce contradictory results

  6. Implement adaptation mechanisms to adjust decomposition based on feedback:

    • If a specialized agent struggles with a task, it should be able to request further decomposition
    • If decomposition is too granular, consolidation may be needed

  7. Create monitoring and debugging tools that can visualize the task hierarchy and progress.

  8. Build evaluation mechanisms to assess the quality of both the final output and the decomposition itself.

Code Examples

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Variations

Common modifications or adaptations of the basic pattern:

  • Dynamic Decomposition: Instead of planning the entire hierarchy upfront, tasks are decomposed just-in-time as they are processed, allowing for adaptation based on intermediate results.

  • Hybrid Hierarchy: Combining predetermined decomposition rules for well-understood domains with learned decomposition strategies for novel or ambiguous tasks.

  • Multi-Model Hierarchies: Using different sized models at different levels of the hierarchy, with smaller, more efficient models handling routine sub-tasks and larger, more capable models managing complex reasoning or integration.

  • Human-in-the-Loop Decomposition: Involving human expertise in the decomposition planning or at key decision points in the hierarchy.

  • Task Markets: Implementing an internal "marketplace" where sub-tasks are offered to various specialized agents that bid based on their capability and capacity.

  • Self-Improving Hierarchies: Systems that learn optimal decomposition strategies based on performance data from previous tasks.

Real-World Examples

Systems or applications where this pattern has been successfully applied:

  • Research assistants that decompose complex questions into research sub-tasks, fact-finding missions, and synthesis steps, then integrate the results into comprehensive answers.

  • Content creation platforms that break down creation tasks into research, outlining, drafting, editing, and refinement phases with specialized models for each stage.

  • Autonomous planning systems that decompose high-level goals into increasingly specific objectives and action steps.

  • Code generation systems that break down programming tasks into architecture design, component specification, implementation, and testing phases.

  • Complex decision support systems that break down analysis into data gathering, processing, evaluation, and recommendation stages.

Related Patterns

Other patterns that:

  • Planner: Often used to create the initial decomposition and dependencies in the task hierarchy.

  • Router: Used to direct sub-tasks to appropriate specialized agents based on task classification.

  • Multi-Agent: Hierarchical Task Decomposition can be viewed as a structured approach to multi-agent collaboration.

  • ReAct (Reasoning + Acting): May be used by individual agents within the hierarchy to solve their assigned sub-tasks.

  • Chain-of-Thought Prompting: Often employed by specialized agents to solve their particular sub-tasks with explicit reasoning.

  • Reflection: Can be integrated at each level of the hierarchy to evaluate and refine approaches.

  • Tool Use: Specialized agents in the hierarchy often leverage external tools to accomplish their sub-tasks.