Agentic Workflow

Agentic Workflows are AI systems designed to autonomously plan, execute, and adapt multi-step processes to achieve complex goals. Unlike single-turn interactions, agentic systems maintain state, use tools, make decisions, and iterate until objectives are met or they require human intervention.

Key patterns in agentic workflows include: ReAct (Reasoning + Acting loops), plan-and-execute (creating plans then following them), self-reflection (evaluating and improving outputs), multi-agent collaboration (specialized agents working together), and human-in-the-loop (checkpoints requiring human approval).

Building effective agentic workflows requires: careful task decomposition, robust error handling, clear success criteria, cost and latency management, safety boundaries, and appropriate human oversight. While powerful, agents can also compound errors and consume significant resources, so engineers must design them thoughtfully with proper guardrails and monitoring.