John SorianoThe demo worked great. The agent read incoming documents, extracted the key information, and drafted a structured summary in seconds. Everyone in the room was impressed.
Then we tried to run it on actual work and spent the next few weeks learning what “real operational work” requires.
That lesson applies whether the team is processing insurance documents, supplier forms, onboarding packets, or customer requests across multiple markets and time zones. The demo proves the capability. Production proves the workflow.
The gap between demo and production
Demo inputs are clean. Production inputs are not.
Documents arrive in different formats. Fields are missing. The same field is labeled three different ways by three different senders. Attachments are scanned, forwarded, renamed, compressed, duplicated, or incomplete. Some records contain customer data that should only be handled in specific ways.
The agent that handled clean demo data might handle 60% of production data without intervention. That number is not a failure. It is the starting point.
The first serious layer to build is not another prompt. It is instrumentation: capture where the agent is uncertain, where reviewers correct it, which inputs fail, and which cases require escalation. Review those cases manually. Patterns emerge. Improve the system around the most common failures. Repeat.
The build feels less like magic and more like teaching the operation to write down rules it has been carrying informally for years.
What makes a team rely on it
Reliability has two parts: accuracy and predictability.
Accuracy means the agent gets the right answer. Predictability means the team understands what kind of mistakes to expect when it does not.
Teams start to rely on agents when the failure modes become visible and manageable. If the agent is wrong in a specific, recognizable way, a reviewer can learn when to trust it and when to double-check. If mistakes are random or invisible, trust disappears quickly.
The other requirement is control. In most operational workflows, an agent should not send, approve, delete, or commit anything sensitive without a human review path. Every output should start as a draft unless the risk profile clearly allows more automation.
That is not a lack of ambition. It is how adoption survives contact with real work.
What I would build first next time
I would instrument earlier. Logging uncertainty, review decisions, and correction patterns should be part of the first production version, not something added after the pilot struggles.
I would also involve end users in edge-case review from the beginning. The people doing the work usually know the messy cases already. They know which supplier always sends incomplete fields, which customer segment uses different terminology, and which exceptions are actually important.
The agent is only part of the system. The rest is workflow design: review queues, escalation paths, data boundaries, audit trails, and the small pieces of context that make the output useful.
Shipping an agent that does real operational work is not about making it look autonomous. It is about making it reliable enough that the team can stop treating it like a toy.