Agentic Workflow

An agentic workflow is an AI-driven process where language models go beyond single-turn question answering to autonomously plan and execute multi-step tasks, making decisions at each stage about what action to take next based on accumulated context and intermediate results. These workflows combine the reasoning capabilities of LLMs with tool access, memory, and iterative execution to handle complex processes that require adaptation and judgment.

Agentic workflows differ from traditional automation in their flexibility and decision-making capability. While conventional automation follows predetermined paths (if-then rules, fixed sequences), agentic workflows allow the AI to dynamically decide which steps to take, what tools to use, and how to handle unexpected situations. This makes them suitable for tasks with variable inputs, ambiguous requirements, or situations where the optimal process depends on intermediate results.

Common agentic workflow patterns include ReAct (Reasoning and Acting), where the model alternates between reasoning about the current state and taking actions; Plan-and-Execute, where the model first creates a plan and then executes each step; and Reflexion, where the model evaluates its own outputs and iterates to improve them. Multi-agent workflows assign different steps or roles to specialized agents, enabling parallel execution and expertise division.

Enterprise agentic workflows are being applied to research and analysis tasks (gathering information from multiple sources, synthesizing findings, generating reports), software development (writing code, running tests, debugging, creating pull requests), customer service escalation (investigating issues, pulling account data, proposing resolutions), and business process automation (processing applications, conducting reviews, generating documentation). Reliability, cost control, and human oversight are critical production concerns, often addressed through structured guardrails, approval gates, and comprehensive logging.