Chatbots answer; agents act. The gap is tool use, planning, memory and guardrails. A practical look at what separates a demo agent from one you can trust in production.
Chat is the easy 20%
Generating fluent text is largely solved. The hard part is letting a model take real actions — query a database, file a ticket, move money — without doing the wrong thing at scale.
Tools, planning, memory
An agent that acts needs a well-typed set of tools, a planner that can sequence them, and memory so it doesn't repeat itself or lose the thread across steps. Each is a deliberate design decision, not a default.
Guardrails are the product
Permissions, dry-runs, confirmation steps and hard limits are what make autonomy safe. We design the failure modes first: what the agent must never do, and how a human takes back control.
Observability or it didn't happen
Every action an agent takes is logged, traceable and replayable. When something goes wrong — and eventually it will — you need to see exactly which step, tool and input caused it.