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What is an AI workflow? Definition and business implications

An AI workflow is the structured sequence of several AI inference steps to accomplish a complex task, usually with external tools interleaved. It can be pre-defined (linear chain) or dynamic (orchestrated by an agent), and is the dominant operational mode of mature AI deployments in enterprise.

Two families of AI workflows coexist in 2026. Deterministic workflows: the sequence of steps is pre-defined by the developer (step 1 = classification, step 2 = extraction, step 3 = drafting), and the model is called at each step with dedicated prompts. This is the architecture favoured for structured, predictable processes with controlled risk. Agentic workflows: the sequence is decided dynamically by an agent model that chooses, at each step, which tool to invoke and which argument to pass. More flexible but less predictable. According to the Anthropic State of AI Agents 2026 survey, 57% of organisations in production use multi-step workflows, and 16% operate cross-team workflows. The dominant workflow frameworks in 2026: LangGraph (state-graph oriented), n8n and Make for non-developer users, AWS Step Functions and Azure Logic Apps for cloud-native deployments.

Concrete example

An audit firm deploys an AI workflow to analyse its clients' annual accounts. Six structured steps. Extraction of key figures from the PDF (vision model). Classification of the sector of activity (text model). Search for sector comparables (internal database call). Calculation of financial ratios (deterministic function). Drafting of a summary note (text model with context injected). Mandatory human validation before sending. On 150 processed cases, the average time goes from 5 hours to 1 hour 20, with an extraction error rate of 0.8% detected during validation. The workflow is not an autonomous agent: each step is coded, but the model executes the cognitive part.

See also

Further reading

LangGraph documentation, agentic workflows framework (external resource)

Sources

  1. How enterprises are building AI agents in 2026, Anthropic, February 2026. https://claude.com/blog/how-enterprises-are-building-ai-agents-in-2026 (accessed 2026-05-24)
  2. LangGraph documentation, LangChain, 2026. https://langchain-ai.github.io/langgraph/ (accessed 2026-05-24)

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