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Plain-language AI glossary

Term 51TechniquesMeaning / context / connections

Techniques / Definition

Agentic Workflow

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

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MeaningThe one-sentence definition.

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ContextHow the idea works in practice.

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UsesWhere the concept becomes useful.

01 / Plain-language context

How Agentic Workflow 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.

02 / Practical uses

Where it helps.

  1. 01Automated code review and refactoring
  2. 02Multi-step research and report generation
  3. 03Data pipeline orchestration
  4. 04Customer service escalation workflows
  5. 05Content creation with fact-checking
03 / Keep exploring
AI AgentAn AI system that can autonomously plan, reason, use tools, and take actions to accomplish goals with minimal human intervention.Autonomous AgentsAI systems that can independently plan, execute multi-step tasks, use tools, and adapt their approach based on results, with minimal human oversight.Function Calling (Tool Use)An AI capability where the model can decide to invoke external functions or APIs based on the conversation context.Chain of Thought (CoT)A prompting technique that improves AI reasoning by instructing the model to break down complex problems into intermediate steps before giving a final answer.