Agentic Workflows
Systems where AI agents autonomously break down goals, plan steps, and execute tasks using tools without continuous human intervention.
An Agentic Workflow refers to a multi-step process orchestrated by autonomous AI agents. Unlike standard prompt-and-response interactions with Large Language Models (LLMs), agentic workflows involve providing the AI with a high-level goal, which the agent then autonomously breaks down into smaller, actionable steps.
Key components of an agentic workflow typically include:
- Planning: The agent creates a step-by-step roadmap to achieve the goal.
- Tool Use: The agent can invoke external tools (like calculators, web browsers, databases, or APIs) to gather information or execute actions.
- Reflection: The agent evaluates its own output, identifies errors, and refines its approach before proceeding.
- Memory: The agent retains context across multiple steps or sessions to maintain continuity.
By shifting from single-shot prompts to iterative, agentic processes, the reliability and capability of AI systems improve dramatically, enabling them to solve complex, real-world business problems autonomously.
Related Terms
Retrieval-Augmented Generation (RAG)
A framework that improves AI responses by dynamically retrieving facts from an external knowledge base before generating an answer.
Chain of Thought (CoT)
A prompting technique where an AI model explicitly generates a step-by-step reasoning path before providing a final answer.