Agentic workflow
- Definition
- A multi-step process where an AI agent plans, executes, evaluates, and iterates on tasks with minimal human intervention. Unlike single-turn prompts, agentic workflows involve loops, branching logic, and tool calls that unfold over minutes or hours.
- Why it matters
- The shift from prompt-response to agentic workflows is the most consequential product transition since mobile. A single prompt gets you a draft; an agentic workflow gets you a finished deliverable. This means AI can now own entire processes, not just assist with steps. For executives, the implication is that headcount planning, vendor contracts, and org design all need to account for workflows that an agent can run end-to-end. If your competitors adopt agentic workflows and you do not, you are competing with a 10x cost disadvantage on every process that can be automated.
- In practice
- Anthropic's Claude Code runs agentic coding workflows where it reads a codebase, plans changes across multiple files, writes tests, runs them, fixes failures, and commits, all autonomously. GitHub Copilot Workspace takes a GitHub issue and produces a full pull request through planning, implementation, and validation steps. Enterprise platforms like ServiceNow and Workday are embedding agentic workflows into IT ticket resolution and employee onboarding, reporting 40-60% reduction in resolution times for Tier 1 support cases.
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Related terms
Agent
An AI system that can autonomously plan, use tools, and execute multi-step tasks on behalf of a user. Agents are the next major product paradigm after chatbots, with every major lab shipping agent frameworks.
Orchestration
The coordination layer that manages the flow of data, context, and control between multiple AI models, tools, and data sources within a complex application. Orchestration frameworks handle routing, error recovery, state management, and multi-step workflows.
Human-in-the-loop (HITL)
A design pattern where a human reviews, approves, or corrects AI outputs before they take effect in the real world. HITL balances AI automation benefits with human judgment for high-stakes decisions.
Tool use
The ability of an AI model to invoke external tools, such as web search, code execution, or database queries, to augment its capabilities. Tool use transforms models from knowledge stores into action-taking agents.
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