AI agent projects rarely fail because the model cannot produce text. They fail because the workflow around the model is not production-ready. A demo can call a tool, summarize a record, or create a ticket. A production agent needs permission boundaries, input validation, failure handling, audit logs, human approval for risky actions, and clear ownership when something goes wrong.
The first mistake is starting with the model instead of the business process. Before choosing a framework, define the workflow. What is the agent allowed to read? What is it allowed to change? Which actions require approval? Which systems are authoritative? Which events must be logged?
The second mistake is treating all tools equally. Searching a policy document is not the same risk as updating a customer record or issuing a refund. Read-only tools can usually move faster. Write actions need stronger controls.
The third mistake is ignoring evaluation. Agents need test scenarios, expected outcomes, and monitoring. If the team cannot measure whether the agent did the right thing, the agent is not ready for production.
The safer path is simple: design narrow workflows, use approved tools, log every action, add human approval where business risk exists, and expand only after the first workflow is reliable.