Definition
Agentic workflows are AI system architectures where language models act as autonomous agents—planning, using tools, executing code, browsing the web, and completing multi-step tasks with minimal human oversight. This represents the shift from AI as a conversational tool to AI as a capable worker that accomplishes goals.
The architecture typically combines planning (decomposing goals into subtasks), tool use (calling APIs, databases, and services via function calling or MCP), memory (maintaining context across steps), reasoning (evaluating progress and adapting strategies), and action execution (taking concrete steps through computer use, code execution, or API calls).
In 2026, agentic capabilities are mainstream. Claude's computer use enables agents that navigate software interfaces visually. OpenAI's Operator and deep research features handle complex multi-step investigations. Google's Gemini agents interact with Workspace and other services. Frameworks like LangChain, CrewAI, AutoGen, and LlamaIndex Workflows provide the infrastructure for custom agent development, as we noted in our introducing agent chat coverage. ChatGPT's large mainstream usage regularly use agentic features.
For GEO, agentic workflows fundamentally change content discovery dynamics. AI agents actively browse, search, evaluate, and synthesize content as part of agentic search—not just answering questions, but conducting research, generating reports, and making recommendations. Content that is discoverable, well-structured, authoritative, and comprehensive serves as fuel for agent research workflows.
To optimize for agentic discovery: ensure content is crawlable by AI crawlers, structure information for efficient extraction, provide clear expertise and authority signals, maintain current and accurate content, and consider publishing llms.txt to guide agent access to your most valuable content. Our agentic SEO explainer breaks down how to prepare for this shift.
Examples of Agentic Workflows
- A research agent that searches the web, reads dozens of sources, evaluates credibility, synthesizes findings, and produces a cited competitive analysis—all from a single goal prompt
- A coding agent that reads requirements, writes code, runs tests, debugs failures, searches documentation, and creates pull requests autonomously
- ChatGPT's deep research mode conducting multi-step web research across dozens of sources to produce a comprehensive, cited report on a complex topic
- A content creation workflow where an agent researches topics, creates outlines, drafts articles, and formats for publication with human editors reviewing final output
- A search team evaluates agentic workflows by checking whether AI systems can retrieve the right pages, verify the claims, and cite the brand consistently across Google AI Mode, ChatGPT, Perplexity, and Copilot.
