Most AI agents fail because people build them like chatbots

A pattern I keep seeing: People build “AI agents” as if they are just chatbots with tools. That works for demos. It falls apart the moment the workflow takes more than one session. Example: A customer onboarding agent should not “remember” that it sent the welcome email because that happened somewhere in the chat history. It should know that because there is an explicit state like: LEAD_CAPTURED PLAN_SELECTED CONTRACT_SENT CONTRACT_SIGNED PAYMENT_RECEIVED ONBOARDING_STARTED COMPLETED That state should live in your database, not inside the model’s memory. The model can reason, write, summarize, call tools, and decide what to do next. But the business process needs to be deterministic. The practical architecture I like: Use the LLM for reasoning and language. Use tools for actions. Use a state machine for workflow progress. Use webhooks/events to wake the agent back up. Use logs/evals to prove it did not skip steps. Use human approval for expensive or risky actions. A good agent is not “one giant prompt.” It is closer to a small operating system around a model. That is the difference between a cool demo and something a business can actually trust.