Planning Agents

Planning agents transform high-level goals into executable task sequences. They are the "project manager" layer in multi-agent systems.

Core Capabilities

Goal decomposition — breaking a high-level objective into concrete, achievable sub-tasks
Dependency resolution — identifying which tasks must complete before others can begin
Resource allocation — assigning tasks to the most appropriate tool or sub-agent
Replanning — adapting when a step fails or produces unexpected results

Architecture Pattern

Planning Approaches

Approach	Description	Best for
Chain-of-Thought (CoT)	Agent generates one task at a time in response to context	Dynamic, unpredictable environments
Tree-of-Thought (ToT)	Agent explores multiple plan branches simultaneously	Complex problems requiring exploration
ReAct	Interleaves reasoning (thought) and action in alternating steps	Tool-using agents with feedback loops
Handcrafted sequences	Designer explicitly defines the task chain	Repeatable, well-understood workflows

2025 Context

Reasoning models (o3, DeepSeek R1, Gemini Deep Think) substantially improved planning quality. Their ability to "think through" a complex goal before committing to a task sequence reduces mid-plan failures.

Framework support for planning has matured: LangGraph's explicit state machine model and Google ADK's hierarchical delegation both directly support planning architectures.

Source

LangGraph planning patterns; Google ADK architecture