Agent State Management
The systems and patterns for tracking, persisting, and restoring an agent's working context across steps, sessions, and failures. State management enables agents to handle long-running tasks and recover gracefully from interruptions.
Agent state management solves the challenge of maintaining continuity in multi-step workflows. An agent processing a complex task needs to track what it has accomplished, what remains, what data it has gathered, and what decisions it has made. This state must survive individual step failures, session timeouts, and system restarts.
For production agent systems, choose your state management approach based on workflow complexity and durability requirements. Simple agents can use in-memory state within a single session. Long-running agents need persistent state stores (databases, Redis, or dedicated state management services). Complex multi-agent systems need distributed state that multiple agents can read and write safely. Key design decisions include state schema design (what to store), serialization format (how to store it), consistency guarantees (how to handle concurrent access), and cleanup policies (when to garbage collect). LangGraph provides built-in state management with checkpointing. For custom solutions, model your state as an event log for easy debugging and replay.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.