AI
Why Most AI ‘Agents’ Are Just Expensive Chatbots
The word agent is everywhere now. Investors, vendors, and blog posts throw it around like confetti. But when you pull back the curtain, many so-called agents are chatbots with a scheduler attached: they call an LLM, parse the response, run a single action, and wait for the next message. That’s not an agent. It’s a fancy webhook.
What makes an agent “real”?
To my mind, a real agent is autonomous, stateful, and safety-aware. Autonomy means it can initiate actions without human prompts, plan multi-step sequences, and adapt when the world changes. Stateful means it keeps a contextual memory beyond a single request — not just a token cache but a model of the current task, constraints, and progress. Safety-aware means it detects risky operations, asks for confirmation when necessary, and has recovery or rollback mechanisms.
Three common failure modes
1) Stateless loops: Many designs call a model for each step and pass only the immediate context. When the environment drifts (API failures, partial success), the system loses continuity. You get brittle behavior and repeated mistakes.
2) Planless action: Some systems generate a single-step intent and execute it. They can’t manage dependencies or handle long-running jobs. They end up as expensive chatbots that forward commands to other services.
3) Over-trusting models: Without verification, systems take a model’s output as truth. That causes costly errors when models hallucinate or misunderstand side effects.
What a robust agent looks like
At minimum: a planner that breaks a goal into verified subtasks, an executor that performs actions and reports outcomes, a state store that tracks progress and contingencies, and a verifier that asserts results against reality. Add human-in-the-loop gates for sensitive actions and automatic rollback for destructive steps.
Practical differences in engineering
Chatbot wrappers are simple to build: HTTP in, call model, parse JSON, run action. Real agents require durable storage, a task scheduler, retry logic, and observability. They also need policies: what can be done automatically, what requires approval, and how to handle uncertain outcomes.
When to use a chatbot vs an agent
If your goal is conversational assistance, a chatbot is fine. If you need end-to-end task completion — booking travel, reconciling invoices, deploying releases — you deserve an agent. But build the agent with humility: start with narrow, verifiable tasks; add autonomy incrementally; and instrument heavily.
The economics
Agents are costlier up front: more infra, more engineering time, more complexity. But they unlock non-linear value when they reduce human wait time, coordinate across systems, and act continuously. If you mistake a chatbot for an agent you’ll pay both in cloud compute and in missed business outcomes.
A closing note
Labeling something an agent is marketing, not engineering. If you want autonomy, focus on the mechanics — state, planning, verification, and safe actuation. That’s where the work is, and where the real gains hide.