Stateful agent
A stateful agent persists state — memory, learned facts, long-running context — across sessions, in contrast to stateless agents that start fresh on every conversation.
Stateful agents are the basis for long-running assistants in 2026 — Claude Projects, ChatGPT memory, Gemini Gems, custom internal agents. State usually lives in a database (per-user facts, preferences, conversation summary) and gets selectively loaded into the agent's context per turn. The hard engineering: deciding what to remember (signal vs noise), what to expose to the model (context budget), how to update state (write back), and how to forget (TTL or explicit user control). The biggest UX risk is the assistant treating outdated state as current — design forgetting and verification carefully.
When to use stateful agent
- Long-running assistants with returning users.
- Customer support, personal tutors, recurring research agents.
Common mistakes
- Loading all state into every turn — context blows up.
- Skipping a forgetting strategy — agents become confidently wrong over time.
FAQ
What is stateful agent?
A stateful agent persists state — memory, learned facts, long-running context — across sessions, in contrast to stateless agents that start fresh on every conversation.
When should I use stateful agent?
Long-running assistants with returning users. Customer support, personal tutors, recurring research agents.
What are the most common mistakes with stateful agent?
Loading all state into every turn — context blows up. Skipping a forgetting strategy — agents become confidently wrong over time.
Related terms
- AI agent — An AI agent is a system where a language model autonomously plans and executes a sequence of tool calls to accomplish a goal.
- Context distillation — Context distillation summarises an agent's growing conversation history into a compact representation, so each step's input stays small while preserving the relevant signal.
- Retrieval-augmented generation (RAG) — Retrieval-augmented generation (RAG) injects relevant documents into the prompt at query time so the model answers from your data instead of its training memory.
- System message — A system message is the highest-priority instruction message in a chat-style API call — used to set role, constraints, and behaviour for the entire conversation.
Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/stateful-agent.md.