Planning Engine Specification

Document ID: AGENT-004
File Path: docs/04-agent-framework/planning-engine.md
Version: 1.0.0
Status: Draft
Owner: AI Platform Team
Last Updated: 2026-06-26

---

1. Purpose

The Planning Engine is responsible for transforming high-level objectives into executable plans that AI agents can execute within the Wovyr AI Platform.

Instead of directly asking an LLM to produce a final answer, the Planning Engine decomposes complex goals into smaller, deterministic tasks that can be executed by tools, workflows, humans, or other agents.

The Planning Engine enables:

• Task decomposition
• Goal reasoning
• Multi-step execution
• Dynamic replanning
• Parallel task execution
• Dependency management
• Human approvals
• Failure recovery
• Multi-agent coordination

---

2. Objectives

The Planning Engine shall provide:

• Hierarchical planning
• Dynamic planning
• Graph-based planning
• Tool selection
• Agent delegation
• Cost-aware execution
• Retry-aware planning
• Deterministic workflow integration
• Checkpoint compatibility
• Distributed execution support

---

3. Design Principles

1. Plans are immutable after creation.
2. Tasks are independently executable.
3. Every plan is versioned.
4. Planning is deterministic whenever possible.
5. Dependencies are explicitly defined.
6. Planning and execution are separated.
7. Plans are checkpoint-aware.

---

4. High-Level Architecture

                    User Goal
                        │
                        ▼
                Goal Analyzer
                        │
                        ▼
               Planning Engine
                        │
      ┌─────────────────┼──────────────────┐
      ▼                 ▼                  ▼
 Task Planner     Dependency Graph    Cost Estimator
      │                 │                  │
      └─────────────────┼──────────────────┘
                        ▼
                Execution Planner
                        │
                        ▼
                 Workflow Runtime

---

5. Planning Workflow

Goal Received

↓

Analyze Goal

↓

Identify Constraints

↓

Generate Tasks

↓

Build Dependency Graph

↓

Estimate Cost

↓

Validate Plan

↓

Publish Plan

↓

Execute

---

6. Core Components

Component	Responsibility
Goal Analyzer	Understand objective
Planner	Generate task graph
Dependency Manager	Resolve ordering
Optimizer	Improve execution plan
Cost Estimator	Estimate execution cost
Validator	Verify plan correctness
Replanner	Modify plans after failures
Executor Adapter	Convert plans into workflow activities

---

7. Plan Structure

Plan
│
├── Metadata
├── Goal
├── Constraints
├── Tasks
├── Dependencies
├── Resources
├── Execution Policy
├── Retry Policy
└── Outputs

---

8. Plan Metadata

Example:

planId:
workflowId:
agentId:
version:
goal:
priority:
createdAt:
updatedAt:
plannerVersion:

---

9. Task Model

Each task contains:

taskId:
name:
description:
type:
priority:
status:
tool:
agent:
inputs:
outputs:
timeout:
retry:

Tasks are the atomic execution units.

---

10. Task Types

Supported task types:

• LLM Task
• Tool Task
• Workflow Task
• Human Approval
• External API
• Database Query
• Script Execution
• Multi-Agent Task
• Decision Task
• Conditional Task

---

11. Dependency Graph

Tasks are organized into a Directed Acyclic Graph (DAG).

Example:

Task A

├────────┐

▼        ▼

Task B  Task C

    │      │

    ▼      ▼

    Task D

       │

       ▼

    Task E

The graph defines execution order.

---

12. Planning Strategies

Supported strategies:

Strategy	Description
Sequential	Linear execution
Hierarchical	Parent-child decomposition
Tree Search	Explore alternatives
Graph Planning	DAG generation
ReAct	Reason + Act
Planner-Executor	Dedicated planning and execution
HTN	Hierarchical Task Networks

---

13. Goal Analysis

Goal analysis identifies:

• Required outputs
• Constraints
• Dependencies
• Available tools
• Risks
• Estimated complexity

The analysis phase informs task generation.

---

14. Constraint Handling

Constraints may include:

• Time limits
• Budget limits
• Tool restrictions
• Region restrictions
• Compliance requirements
• Security policies
• Human approvals

Constraints are validated before planning.

---

15. Resource Estimation

Each task estimates:

cpu:
memory:
network:
storage:
tokens:
estimatedDuration:
estimatedCost:

Resource estimates aid scheduling.

---

16. Cost Estimation

Cost factors include:

• LLM token usage
• Tool invocations
• Cloud resources
• Storage
• Network traffic
• Human approvals

The planner may choose lower-cost alternatives.

---

17. Parallel Planning

Independent tasks execute concurrently.

Example:

Goal

↓

Analyze

↓

───────────────

│      │      │

▼      ▼      ▼

Task1 Task2 Task3

│      │      │

───────────────

↓

Merge Results

Parallel execution reduces completion time.

---

18. Conditional Planning

Conditional branches are supported.

Validate Code

↓

Compilation Success?

├──────────────┐

Yes            No

│              │

Deploy      Fix Code

Branch conditions are evaluated during execution.

---

19. Dynamic Replanning

Plans may be regenerated after failures.

Triggers:

• Tool failure
• Human rejection
• Timeout
• Missing resources
• Policy changes
• External events

Replanning preserves completed work.

---

20. Multi-Agent Planning

Tasks may be delegated.

Example:

Planner Agent

↓

Assign Tasks

↓

Developer Agent

QA Agent

Documentation Agent

↓

Merge Outputs

Delegation policies are configurable.

---

21. Human-in-the-Loop

Plans may pause for approval.

Example:

Generate Contract

↓

Legal Approval

↓

Continue Execution

Approvals integrate with workflow waiting states.

---

22. Retry Planning

Retry policies are embedded into the plan.

Supported strategies:

• Immediate
• Fixed Delay
• Exponential Backoff
• Fibonacci
• Manual Retry

---

23. Checkpoint Integration

Plans participate in workflow checkpointing.

Stored state includes:

• Current task
• Completed tasks
• Pending tasks
• Dependency graph
• Planner state

Recovery resumes from the latest checkpoint.

---

24. Rust Interfaces

pub trait Planner {
    fn create_plan(
        &self,
        goal: Goal,
    ) -> Result<ExecutionPlan>;

    fn validate_plan(
        &self,
        plan: &ExecutionPlan,
    ) -> Result<()>;

    fn replan(
        &self,
        state: PlanState,
    ) -> Result<ExecutionPlan>;
}

---

25. Module Organization

engine-planner/
├── analyzer/
├── planner/
├── optimizer/
├── graph/
├── constraints/
├── estimator/
├── validator/
├── replanner/
├── execution/
├── metrics/
└── mod.rs

---

26. Testing Strategy

Unit Tests

• Goal analysis
• DAG generation
• Dependency resolution
• Cost estimation
• Constraint validation

Integration Tests

• Workflow Runtime
• Agent Runtime
• Tool Framework
• Human approval
• Multi-agent execution

Performance Tests

• Million-task plans
• Deep dependency graphs
• Large workflows
• Concurrent planners

---

27. Non-Functional Requirements

Requirement	Target
Plan generation	< 200 ms
DAG validation	< 20 ms
Cost estimation	< 50 ms
Replanning	< 150 ms
Horizontal scaling	Unlimited

---

28. Dependencies

• docs/03-workflow-engine/agent-runtime.md
• docs/03-workflow-engine/state-machine.md
• docs/03-workflow-engine/checkpointing.md
• docs/04-agent-framework/tool-framework.md
• docs/04-agent-framework/memory-system.md

---

29. Related Documents

• docs/04-agent-framework/agent-definition.md
• docs/04-agent-framework/provider-sdk.md
• docs/04-agent-framework/context-manager.md
• docs/04-agent-framework/policy-engine.md

---

30. Future Enhancements

• Monte Carlo Tree Search (MCTS)
• AI-assisted plan optimization
• Reinforcement learning planners
• Autonomous cost optimization
• Probabilistic planning
• Cross-workflow planning
• Predictive scheduling
• Plan marketplaces

---

31. Revision History

Version	Date	Description
1.0.0	2026-06-26	Initial Planning Engine Specification