Field note / Forward deployed AI engineering
The Agent Did Not Fail. The Workflow Did.
A practical note on why forward deployed AI engineering starts by encoding the workflow before expanding agent authority.
A support agent gives a confident answer from an old policy. A sales agent follows up after the deal has already moved. An internal agent updates a field no one owns anymore. It is tempting to say the model failed.
Often, the agent did exactly what the workflow allowed.
The missing part is usually not another prompt. It is the contract around the work. Which system is the source of truth. Who owns the exception. Which action needs approval. Which handoff is safe. Which metric shows the pilot improved the work. Where the system must stop.
If those rules live in Slack, Notion, a manager's memory, and a few old tickets, every agent starts from folklore. It can sound capable while guessing the operating model.
HEUREMA is a small team of forward deployed AI engineers. We work inside one real workflow at a time and make that operating model explicit before giving an agent more authority.
That means mapping the current loop, naming systems of record and owners, defining data boundaries, keeping risky actions human-approved, and deciding what evidence would justify expanding the pilot.
The result we care about is not a demo. It is a workflow contract above the model layer. The agent can draft, route, check, summarize, and escalate because the boundary is visible.
When the workflow is missing, a stronger model makes confusion faster. When the workflow is encoded, AI work becomes reviewable.
That is our starting point for forward deployed AI engineering. Stay close to the work. Encode the process. Keep authority explicit. Measure one workflow before scaling.