Agentic Workflows

An agentic workflow is a multi-step process in which a language model plans the work, calls tools, inspects the results and decides the next step, repeating until a goal is met or a human checkpoint is reached. The term describes the process design. The AI agent is the system that runs it.

Most agentic workflows are built from the same parts:

• a planner that splits the goal into steps
• tools the model can call through function calling or the Model Context Protocol
• state or memory that carries results from one step to the next
• checks such as tests, graders or a second model reviewing the output
• stop conditions and approval gates for risky actions

A few patterns cover most real deployments. The simplest is a single loop: reason, act, observe, repeat. Routing sends a task to a cheap model first and escalates hard cases. Orchestrator and subagent designs let one model split work into parallel pieces. Evaluator loops have one pass draft and another critique until the result meets a bar. Vendors now ship these patterns as product features. OpenAI's GPT-5.6 launch introduced an 'ultra' setting that coordinates multiple agents across parallel workstreams, and GPT-6.1 Sol, released in September 2026, supports multi-agent delegation in beta in the Responses API. Anthropic positions Claude Haiku 5.5, released October 7, 2026, as a fast subagent next to Opus 5.5 and Sonnet 5.5. Teams that build their own use AI agent frameworks such as LangGraph, CrewAI or the OpenAI Agents SDK.

Reliability is the main engineering problem. Errors compound across steps, so production workflows place humans at defined points: before a payment, before an email goes out, before code merges. Long runs need durable state, so a failure at step 40 does not restart the job, which is why checkpointing has become a standard framework feature. Tool access also raises the stakes of prompt injection: a web page read in one step can try to redirect the actions in the next.

For search visibility, agentic workflows matter because many of them read the web as an intermediate step. A research workflow may fetch 50 pages to write one competitive brief, and a procurement workflow may read pricing and security pages before any human looks at them. That reading is a form of agentic search. Content that states facts plainly in server-rendered HTML, with dates and sources, is easier for these steps to extract and reuse. The same workflows run on the marketing side too, where content teams use them to research topics, draft briefs and refresh pages that are losing citations, as covered in our piece on what SEO agents are.