An AI Agent is powered by a large language model, augmented with retrieval, tool calling and workflow orchestration. It does not merely answer questions. Within defined business boundaries, it understands user intent, breaks down tasks, calls system APIs and returns structured results. Examples include checking order status, drafting work orders, aggregating reports and screening forms under rules. These repetitive tasks can be partially automated by agents.

From an engineering perspective, agent quality heavily depends on data quality, clear workflows and well-defined scope. Simply connecting an LLM will not deliver reliable results. We start with scope assessment: identify suitable automation candidates and decide which steps still need human review. We then define deliverables and acceptance criteria to avoid unnecessary complexity.

Task Execution Flow