In an AI workflow, the steps are fixed and AI works inside them. In an AI agent, the model chooses the next step itself. Most production systems combine the two.
Anthropic’s widely used definitions draw the line at control: workflows run on predefined code paths; agents direct their own process and tool use. That single difference decides cost, predictability, testing and audit. This guide shows the patterns on each side, builds one invoice process both ways, and gives a test for choosing.
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An AI workflow is a process whose steps and order are fixed in advance, with a language model doing specific jobs inside them — reading a document, classifying a request, drafting a reply. An AI agent is a system in which the model decides the next step itself, choosing tools and continuing until the goal is met. In AI workflow vs AI agent, the workflow is predictable and cheap to audit; the agent handles paths no one could write down in advance.
Google’s agent toolkit makes the same split in code: its workflow agents run sub-agents in sequence, in parallel or in a loop “without consulting an AI model” for the orchestration, while model-driven agents decide for themselves.
“Systems where LLMs and tools are orchestrated through predefined code paths.” The model does a job; the code decides what comes next.
Systems where models “dynamically direct their own processes and tool usage”. The model decides what comes next.
Anthropic names five workflow patterns it has seen work in production. Each keeps control in code while putting a model where judgement is needed; the sixth card is the point where a workflow stops being enough.
A task split into fixed steps, each model call working on the last one’s output, with checks between.
A model classifies the input and code sends it to the step built for that kind.
Independent parts run at once and are combined in code — or the same task runs several times and the answers are compared.
A model breaks the task into parts at run time and hands each to a worker, then combines what they return.
One model drafts, another critiques against criteria, and the loop repeats until the draft passes.
When none of these fit because the steps cannot be known in advance, let the model direct the work.
Accounts payable receives a supplier invoice. It must be read, matched to a purchase order, and then paid, held or rejected. The process is the same every time; the invoices are not.
A new invoice arrives in the mailbox.
A model reads supplier, totals and lines into typed fields.
Code finds the purchase order and compares totals.
A model picks pay, hold or reject, with a confidence.
Code pays, or sends the invoice to a person to review.
Back to choosing, with each result — until the agent decides the invoice is handled or asks a person.
The workflow fits this process better. The path is known, so fixing it makes every run cheaper, faster and easier to audit, and the model is used only for reading and one judgement. The agent earns its place when exceptions vary — a disputed delivery, a missing order, a supplier who changed name.
Autonomy is paid for in tokens, variance and testing effort, which is why Anthropic advises finding “the simplest solution possible” and adding complexity only when needed. The figures below show the token cost and how many agentic projects analysts expect to be stopped.
Errors compound along an unchecked path. Our arithmetic, not a measurement: if each step is right 95% of the time, independently, ten unchecked steps succeed end to end about 60% of the time (0.95 to the power of 10 ≈ 0.60). A workflow puts a check after each step by design; an agent needs the same checks built into its loop.
OpenAI lists three signs an agent is warranted: complex decisions with exceptions, rules too tangled to maintain, and heavy reliance on unstructured data. Without them, it says, a deterministic solution may suffice.
Can you draw the process as boxes and arrows before it runs?
On Botminds an agentic workflow is an XFlow: a graph of steps on a canvas that run code, call systems, run an agent or make a typed decision. The building blocks nest, and each keeps its own rules wherever it is placed.
Read agentic workflows for the full design, and see the workflow pattern and XFlows and pipelines in the Academy.
In AI workflow vs AI agent, a workflow fixes the steps and their order in code and uses a model inside specific steps. An agent lets the model choose each next step and tool at run time until the goal is met.
No. An agentic workflow keeps a defined sequence of steps and puts agents or model calls inside the steps that need judgement. An AI agent has no fixed sequence; the model directs its own process.
When the path cannot be written down in advance: the next lookup depends on what the last one found, exceptions vary, or the input is open-ended. If you can draw the process as boxes and arrows, build a workflow.
Usually. A workflow calls a model only where it is placed, so the cost per item is known. An agent calls the model on every step and its cost grows with the path it takes; Anthropic reports agents use about four times the tokens of chat.
Invoice to payment, claims intake, contract renewal checks and supplier onboarding: each runs the same steps every time, with a model reading documents or making one judgement inside a step.
Investigating a payment dispute across several systems, answering an open question from company data, researching a topic across many sources — tasks where the next step depends on what was just found.
Yes, and it is the most common enterprise design. The workflow fixes the order and the checks; one step runs an agent to handle the part that needs exploration, and its answer becomes that step’s output.
Anthropic defines workflows as LLMs and tools orchestrated through predefined code paths, and agents as LLMs that dynamically direct their own processes and tool usage. It advises starting with the simplest solution and adding agents only when needed.
Bring one process and its documents. We’ll show you the agents, the controls and the record, running on your data.