Task Planning Architecture

Overview

The Task Planning system enables the AI Dev Agent to break down complex engineering tasks into manageable steps.

Software development tasks are rarely atomic. A typical task may require:

• exploring the repository
• designing a solution
• modifying multiple modules
• writing tests
• validating the implementation

The Task Planning Architecture ensures that the agent can structure its work in a clear and systematic way.

This subsystem sits above the Agent Loop and guides the sequence of actions the agent takes.

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Goals

The Task Planning system is designed with several key goals.

Structured Execution

Large tasks should be decomposed into smaller steps that can be executed within the agent loop.

Adaptability

Plans must be adjustable as the agent discovers new information.

Transparency

The planning process should be observable and understandable by humans.

Robustness

The agent must be able to recover from errors and revise its plan.

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Planning Hierarchy

Task planning is hierarchical.

Goal
│
▼
Task
│
▼
Steps
│
▼
Actions

Each level has a different level of abstraction.

Goal

The high-level objective.

Example:

Add caching support to the user service.

Task

A logically grouped unit of work.

Example:

Implement cache layer
Update service integration
Write cache tests

Steps

Concrete operations required to complete a task.

Example:

Create CacheClient module
Add TTL support
Inject cache into UserService

Actions

Executable tool operations performed inside the Agent Loop.

Example:

read_file
write_file
run_tests

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Planning Lifecycle

The planning process evolves throughout the task.

Initial Planning
↓
Execution
↓
Observation
↓
Plan Revision
↓
Continue Execution

This allows the agent to refine its approach as new information becomes available.

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Initial Plan Generation

When a task begins, the agent generates an initial plan.

Inputs include:

• task description
• architecture documents
• repository context
• prior knowledge

Example output:

Plan:

1. Analyze current UserService implementation
2. Design caching strategy
3. Implement CacheClient
4. Integrate cache into UserService
5. Write tests
6. Run test suite

This plan serves as a starting point rather than a rigid script.

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Dynamic Plan Adjustment

Plans may change during execution.

Examples:

• discovering an existing caching module
• encountering failing tests
• identifying architectural constraints

Example revision:

Original Step:
Implement CacheClient

Revised Step:
Extend existing CacheManager module

The system must allow flexible plan updates.

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Step Execution Model

Each step is executed through the Agent Loop.

Step
↓
Plan next action
↓
Execute tool
↓
Observe result
↓
Update step progress

Steps are marked as completed once their objectives are met.

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Plan Representation

The plan is represented as a structured object.

Example TypeScript representation:

type TaskPlan = {
  goal: string
  tasks: Task[]
}

type Task = {
  id: string
  title: string
  status: "pending" | "in_progress" | "completed"
  steps: Step[]
}

type Step = {
  id: string
  description: string
  status: "pending" | "in_progress" | "completed"
}

This structure allows the system to track progress precisely.

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Step Granularity

Steps should be small enough to execute reliably but large enough to represent meaningful work.

Good examples:

Add TTL option to CacheClient
Update UserService to use cache
Add unit tests for cache expiration

Poor examples:

Implement caching system

Too-large steps reduce reliability and observability.

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Dependency Management

Some steps depend on others.

Example:

Create CacheClient
   ↓
Integrate CacheClient into UserService
   ↓
Write tests

Dependencies must be respected during execution.

Example representation:

type Step = {
  id: string
  description: string
  dependsOn?: string[]
}

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Parallel Opportunities

Some tasks may be executed independently.

Examples:

Write documentation
Write tests

Future versions of the system may support parallel execution.

For the initial architecture, execution is sequential.

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Failure Recovery

Failures may occur during task execution.

Examples include:

• compilation errors
• test failures
• runtime exceptions

When a step fails, the agent may:

1. retry the step
2. refine the implementation
3. revise the plan

Example:

Step: run_tests
Result: failure

Next step:
Inspect failing test and update implementation

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Plan Validation

Before executing major changes, the agent may validate the plan.

Checks may include:

• architectural compatibility
• coding standards
• dependency constraints

This reduces the likelihood of incorrect implementations.

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Plan Persistence

Plans should persist across agent sessions.

Example storage:

.memory/tasks/

Persisted plans allow the agent to resume work after interruptions.

Example file:

.memory/tasks/task_42.json

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Human Interaction

Humans may review or modify plans.

Example workflow:

Agent proposes plan
   ↓
Human reviews plan
   ↓
Agent executes approved plan

This is particularly useful for large or risky changes.

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Integration with Other Systems

Task Planning integrates closely with other architecture components.

Agent Loop

The Agent Loop executes individual actions within each step.

Context Engineering

Relevant plan information is injected into the model context.

Tool System

Actions are executed through tools.

Memory System

Completed tasks may be stored as reusable knowledge.

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Observability

Planning activity should be logged.

Example events:

• plan creation
• step completion
• plan revision
• failure recovery

These logs help analyze agent performance.

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Future Improvements

Possible enhancements include:

• hierarchical planning agents
• learned planning strategies
• plan optimization
• collaborative multi-agent planning

These improvements may increase efficiency and reliability.

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Summary

The Task Planning Architecture enables the AI Dev Agent to manage complex development tasks.

The system organizes work into a hierarchy:

• goal
• tasks
• steps
• actions

Plans are dynamic and may evolve as the agent learns more about the codebase.

By structuring work in this way, the agent can execute complex engineering workflows in a reliable and understandable manner.