Agentic Workflow

Traditional automation is brittle: it breaks the moment the real world deviates from the script. Agentic workflows replace that brittleness with adaptive intelligence. The AI model receives a high-level objective, dynamically determines the steps required, executes tool calls, reads the outputs, and revises its plan based on what it observes — looping until the goal is met or it escalates to a human.

The distinction from a simple chain or pipeline is the feedback loop. A pipeline runs steps in a fixed order. An agentic workflow reasons at each step: "Did this action produce the expected result? If not, what should I try next?" This makes it suitable for tasks like automated due diligence (where the required research steps depend on what each document reveals), multi-source data reconciliation, and customer request fulfillment across heterogeneous systems.

Enterprise adoption of agentic workflows is accelerating in finance (automated trade reconciliation), legal (contract review and redlining), IT operations (incident response and triage), and HR (onboarding orchestration). The critical design principle is that every agentic workflow must have defined exit conditions, budget limits on LLM calls, and a human escalation path — otherwise a poorly-scoped objective can produce runaway, costly, or incorrect behavior.