Agentic Workflow
Definition
An agentic workflow is an AI-driven process where agents autonomously execute multi-step tasks, make decisions, use tools, and adapt their approach based on intermediate results to achieve a goal.
Why It Matters
Traditional automation requires predefined steps that can’t adapt to unexpected situations. Agentic workflows use LLM reasoning to dynamically adjust their approach, handle edge cases, and recover from errors. This enables automation of complex knowledge work that was previously impossible to automate.
How It Works
An agentic workflow starts with a goal and uses an LLM to generate a plan. The agent then executes steps, observes results, and decides whether to continue, adjust the plan, or try alternative approaches. This creates a feedback loop where the workflow can adapt in real-time. Key patterns include ReAct (reasoning + acting), reflection (self-critique), and plan-and-execute.
When to Use It
Agentic workflows shine when: (1) the exact steps can’t be predetermined, (2) the task requires adapting to unexpected inputs, (3) multiple tools or data sources need to be combined intelligently, or (4) error recovery requires judgment rather than simple retries. For straightforward, deterministic processes, traditional automation is simpler and more reliable.