AI Workflow vs. AI Agents: What’s the Difference?

As organizations adopt AI, understanding the distinction between AI workflows and AI agents is crucial for making informed technology investments.

AI Workflows

AI workflows are orchestrated sequences of tasks—often involving data ingestion, model inference, and post-processing—typically managed by automation platforms or scripts. They are linear, predictable, and best suited for well-defined processes.

AI Agents

AI agents are autonomous entities that can perceive, reason, and act within an environment. They can make decisions, adapt to changing conditions, and interact with users or systems dynamically.

Key Differences

• Flexibility: Workflows follow predefined paths; agents adapt in real time.
• Autonomy: Agents can set and pursue goals; workflows require explicit instructions.
• Complexity Handling: Agents excel in unstructured, variable scenarios.

When to Use Each

• Workflows: For repeatable, rules-based processes (e.g., ETL pipelines, batch scoring).
• Agents: For customer support, process optimization, or environments with uncertainty.

Executive Perspective

A hybrid approach—combining robust workflows with intelligent agents—can maximize value and resilience in enterprise AI deployments.