An agentic workflow is a process in which one or more AI agents autonomously plan, make decisions, and execute multistep tasks with little to no human intervention.
Agentic workflows are iterative rather than linear. The agent assesses the situation, selects an action, evaluates the result, and adjusts accordingly. This is unlike traditional, rule-based automation like robotic process automation (RPA), which follows fixed logic and reliably handles repetitive tasks but breaks down when conditions change.
Agentic workflows are built from several components working together.
- AI agents that drive decisions and coordinate execution, powered by large language models (LLMs)
- Tools such as APIs, web search, and external databases that the agent can call when needed
- Prompt engineering techniques like chain-of-thought (CoT) that improve multi-step reasoning
- Feedback mechanisms like human-in-the-loop (HITL) for cases where human oversight is needed
- Multi-agent systems (MAS) where multiple specialized agents collaborate and share information rather than duplicating effort
Agentic workflows operate with significant autonomy, so errors can compound across steps before any human catches them. They are also harder to audit than rule-based systems, making it difficult to trace and explain why a particular action was taken.