Building Enterprise Agent Systems that People can Trust, Verify and Improve

Building successful enterprise agent systems requires tailoring the product to the business workflow rather than defaulting to a chat interface. The process involves breaking down a workflow and assigning each step to the simplest effective mechanism, such as deterministic code, semantic similarity, or a specialized LLM agent. A case study on detecting client churn risk illustrates this by using a summarizer agent for tickets, semantic similarity for grouping, and an investigation agent for judging commercial relevance. The system is designed for human verification, providing alerts with evidence for an account manager who makes the final decision on how to act. This human-in-the-loop approach ensures the agent is trustworthy, verifiable, and improves over time.