Most AI Agents Fail Because They Are Built Like Chatbots

This content originally appeared on HackerNoon and was authored by Abhilash Pakalapati

Building AI agents as conversational chatbots with tools works for a demo, but shatters in production when a process spans multiple sessions. Relying on chat logs to track business milestones causes context rot, high token bills, and cascading failures. For a system to succeed at scale, engineers must treat agents like mini-operating systems: decoupling the model's reasoning from the business state. The workflow progress must be managed by a deterministic database state machine, using the LLM strictly as a processor that acts on explicit, unalterable status gates.

This content originally appeared on HackerNoon and was authored by Abhilash Pakalapati