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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 survey published in December 2025, 57% of organisations deploy agents for multi-step workflows, and 16% run cross-functional processes across several teams. 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
Illustrative case. 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.
FAQ
What is an AI workflow, concretely?
It is the structured sequence of several inference steps to accomplish a complex task, with external tools interleaved. Each step is coded and the model only executes the cognitive part, so it is not an autonomous agent. It is the dominant operational mode of mature enterprise AI deployments.
What is the difference between a deterministic and an agentic workflow?
In a deterministic workflow, the sequence of steps is pre-defined by the developer (step 1 classification, step 2 extraction, step 3 drafting). In an agentic workflow, an agent model decides dynamically, at each step, which tool to call. The first is more predictable, the second more flexible but less predictable.
Should I prefer deterministic or agentic for a controlled-risk process?
For compliance, audit and finance, prefer the deterministic workflow: its fixed sequence is predictable and easier to audit. Keep agentic for cases where flexibility matters most (research, exploratory analysis, multi-channel customer service). It is a trade-off between predictability and flexibility.
Which processes are the best candidates for an AI workflow?
Repetitive, multi-step business processes with clear rules. Creative or highly variable processes are far less suitable. The textbook case: an audit firm that chains extraction, classification, comparable search, ratio calculation, drafting, then mandatory human validation.
How do you measure the value of an AI workflow?
In processing-time reduction and error rate, not in the size of a prompt library. A measurement culture predating AI is a prerequisite: without a baseline, you will not know whether it truly adds value. In the audit-firm example, average time drops from 5 hours to 1 hour 20 with a 0.8% extraction error rate.
See also
Further reading
Sources
- How enterprises are building AI agents in 2026, Anthropic, 9 December 2025 (survey run with Material among 500+ technical leaders). https://claude.com/blog/how-enterprises-are-building-ai-agents-in-2026
- LangGraph documentation, LangChain, 2026. https://langchain-ai.github.io/langgraph/