The demo is impressive. The agent handles the input gracefully, returns the right answer, and everyone in the room nods. Then it goes to production and falls apart.
Reason one: the data underneath it is unreliable. The agent was tested on clean, structured inputs. Production data is messy, inconsistent, and incomplete. Without solid data pipelines, the agent is working on sand.
Reason two: there’s no evaluation loop. Nobody defined what “good” looks like, so nobody notices when the agent drifts. Production agents need continuous evaluation — not just monitoring that they’re running, but monitoring that they’re right.
Reason three: the workflow wasn’t designed for an agent. Someone tried to bolt an agent onto a process that still expects a human at every step. Agents work best when the workflow is redesigned around what they do well.
Reason four: no infrastructure. The agent runs on a laptop or a single server with no redundancy, no logging, no error handling. It works until it doesn’t, and then nobody knows why.
The fix is unglamorous: build the infrastructure first, design the evaluation loop, rethink the workflow, and only then deploy the agent. It’s slower. It also works.