AI Glossarytechniques

Agentic Workflow

A multi-step AI process where an LLM autonomously plans, executes, and iterates on tasks using tools and feedback loops.

How It Works

Agentic workflows go beyond single-prompt interactions. Instead of asking an LLM one question and getting one answer, an agentic workflow lets the model: (1) break a goal into sub-tasks, (2) execute each sub-task using tools (web search, code execution, API calls), (3) evaluate the result, (4) decide what to do next. This loop continues until the goal is met or a stopping condition is reached. The key architectural components are: a planner (decides what to do next), an executor (carries out the step using tools), a memory system (tracks what has been done), and an evaluator (checks if the result is good enough). Frameworks like LangGraph, CrewAI, and OpenAI Agents SDK provide these building blocks. Agentic workflows are powerful but expensive. A single task might involve 10-50 LLM calls. Production systems need cost caps, timeouts, human-in-the-loop checkpoints for destructive actions, and robust error handling. Start with narrow, well-defined workflows before attempting open-ended autonomous agents.

Common Use Cases

  • 1Automated code review and refactoring
  • 2Multi-step research and report generation
  • 3Data pipeline orchestration
  • 4Customer service escalation workflows
  • 5Content creation with fact-checking

Related Terms

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