AI agents need workflow state, not just chatbot memory
AI agents often work in demos but fail in real business workflows when they are built like chatbots with extra tools. A agent should not rely on to know whether it already sent a welcome email. Steps such as d, plan chosen, contract sent, contract signed, payment received, started, and completed should be stored as clear state in a database.
The LLM can reason, write, summarize, call tools, and choose the next action. The business process itself should follow fixed rules instead of depending on the model’s memory. A practical setup uses the LLM for thinking and language, tools for real actions, a to track progress, and webhooks or events to restart the agent when something changes.
Logs and evals help prove that no steps were skipped, and human approval should be required for costly or risky actions. A reliable agent is less like one huge prompt and more like a small built around a model.
Key points
- Multi-step AI agents become fragile when workflow progress lives only in .
- Important progress state should be stored in a database.
- The LLM should handle reasoning and language, while tools handle actions.
- A can keep the workflow moving through the right steps.
- Human approval should be added before expensive or risky actions.