Agentic Workflow
An agentic workflow is a multi-step process where AI agents autonomously execute tasks, make decisions, and handle errors to accomplish a defined goal.
Definition
An agentic workflow is a structured sequence of operations where one or more AI agents work toward a goal with significant autonomy. Unlike traditional automation (rigid, deterministic scripts) or simple AI calls (stateless, single-turn), agentic workflows involve:
- Dynamic decision-making: Agents decide what step to take based on current context
- Error recovery: The workflow can detect failures and attempt remediation
- State management: Context persists across steps, enabling coherent multi-step execution
- Goal orientation: All steps contribute to an overarching objective
Why It Matters
Agentic workflows enable automation of tasks that were previously too complex or variable for traditional approaches:
Handling ambiguity: Unlike scripts that fail on unexpected inputs, agentic workflows can reason about ambiguous situations and make judgment calls.
Adaptive execution: Workflows can modify their approach based on intermediate results, rather than following a fixed path.
Complex task completion: Multi-step tasks that require different skills (research, analysis, synthesis) can be orchestrated coherently.
Scaling expertise: Domain expertise embedded in agent instructions can be applied consistently at scale.
Common Misconceptions
"Agentic workflows are just complex prompts" While prompts are part of agent behavior, agentic workflows involve infrastructure: state management, tool integration, error handling, and orchestration logic.
"More autonomy is always better" Excessive autonomy without appropriate guardrails leads to unpredictable behavior and errors. Effective workflows balance autonomy with checkpoints.
"Agents can handle any task" Agents are effective for tasks that benefit from reasoning and adaptation. Highly deterministic tasks are often better served by traditional automation.
Practical Example
A research agentic workflow might:
- Parse the research question and decompose into sub-questions
- Search multiple sources for relevant information
- Evaluate source credibility and relevance
- Synthesize findings into coherent insights
- Draft initial content with citations
- Review for accuracy and completeness
- Revise based on quality checks
Each step involves agent reasoning, and the workflow adapts based on what the agent finds at each stage.