We build agentic workflows as bounded systems: a model that chooses among defined actions, each action governed, each result recorded, so that automation gains judgement without losing accountability.
The word covers two very different things
An agent, in the loose sense, is a model left to pursue a goal by any means it can find. An agent, in the sense we build, is a model given a fixed set of tools, each with a declared effect and a declared permission, and a loop in which it selects the next tool from the state it can observe. The first is a demonstration; the second is a system.
What the bounds are
Every tool is a function with a schema. The model can call it with arguments, and the call is validated against the schema before anything happens. Actions that change state are separated from actions that only read, and the changing ones carry the permissions of the user or service on whose behalf the workflow runs, never the model’s own.
Why the knowledge graph matters here
A model choosing actions needs to know what exists and how it relates. We give it the same enterprise knowledge graph that grounds retrieval, so that “the customer’s open orders” resolves to a defined query rather than a guess, and so that the model’s plan can be checked against the model of the business before it is executed.
Accountability
Each step is logged with its inputs, its chosen action and its result, in the same attested form as any other record. Your review of an automated decision is then a reading of what was observed, what was chosen and why, in order. That is the difference between automation you can defend and automation you can only hope about.
