An agentic workflow is a multi-step process whose control flow is partly decided by AI agents at runtime rather than fully specified in advance. It mixes defined structure — the overall stages, the available tools, the stopping conditions — with model-driven choices about which action to take next, producing a path through the work that can differ from one run to the next.
Why agentic workflows matter
Conventional workflow engines run deterministic graphs: every step and branch is written by a developer, and the same inputs follow the same path. That model is reliable but rigid. It cannot handle work where the right next step depends on facts discovered along the way — classifying an ambiguous request, deciding which system to query, or recovering from an unexpected response.
Agentic workflows relax that constraint by letting a model make some of the decisions while the surrounding structure stays fixed. The benefit is adaptability: a single workflow can handle inputs whose exact handling was not foreseen. The cost is that part of the control flow is now non-deterministic, which makes the run harder to predict, reproduce, and debug. The engineering work in agentic workflows is largely about regaining that predictability — through durability, tracing, and constraints — without giving up the flexibility that motivated them.
How it works
An agentic workflow typically interleaves two kinds of steps:
- Defined steps — fixed stages, sequencing, and stop conditions that the author specifies, such as run a retrieval step, then a drafting step, then a review step.
- Agentic steps — points where a model decides what happens, such as which tool to call, which branch to follow, or whether the task is complete.
- Tool calls and handoffs — the actions an agentic step can take, including passing control to another agent for a sub-task.
- Checkpoints — the recorded outcome of each completed step, so the workflow can resume in place rather than restart after an interruption.
Because some steps are model-driven, two runs with the same input may take different paths. The defined structure keeps that variation bounded, and checkpointing keeps each run recoverable.
Agentic workflow vs. deterministic workflow
A deterministic workflow encodes every decision at design time; given the same input it always does the same thing, which makes it predictable but unable to handle the unforeseen. An agentic workflow defers some decisions to a model at runtime, trading guaranteed reproducibility for the ability to adapt. The two are not exclusive: a robust system usually wraps agentic steps inside a deterministic skeleton, so the unpredictable parts are isolated and the overall shape of the run stays under control.
In practice
Reliability for an agentic workflow comes from running it on a durable execution layer. A durable, observable runtime persists every step server-side, so a crash, redeploy, or pause resumes from the last completed step instead of re-running model calls and tool side effects. This combines durable execution with orchestration of the steps and, when several agents are involved, a multi-agent structure. For the broader case, see why durable agents.