Where AI Agents Create Operational Value
Use cases that survive production: triage, routing, structured execution and human-in-the-loop workflows.
Most agent demos do not survive production
Open-ended agents that plan, browse and act look impressive in demos. In production they fail because real workflows have hard constraints, audit requirements and accountability — none of which an autonomous loop respects on its own.
Agents that ship are narrower: scoped tasks, constrained tool surfaces, and a human in the loop where the cost of being wrong is high.
Where agents earn their place
- Triage — classifying, routing or enriching inbound work
- Structured execution — multi-step tasks with a fixed tool surface
- Drafting — generating a first version a human edits and approves
- Investigation — fanning out a query and summarizing what came back
Design rules that hold up
Give the agent a small, well-named tool catalog. Log every tool call. Make the human-in-the-loop checkpoint cheap. Treat the agent's autonomy as a dial, not a destination — start strict, loosen as confidence accumulates.
- Narrow agents in production beat open-ended agents in demos
- Triage, routing, drafting and structured execution are the durable patterns
- Constrain the tool surface and log every call
- Autonomy is a dial — earn it with evidence
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