AI agents
Each one has a scope, an authority level you set, a policy engine it cannot argue with, and a record of everything it did. They start work without being asked and hand you the exceptions instead of the busywork.
The agents
The word has been stretched to cover almost anything with a model behind it, so it is worth being specific about what we mean. Three properties, and a thing has to have all three.
This is the part that is hard to see in a demo and matters most in production. Because every agent reads the same business graph, the AR agent knows a customer is mid-renewal before it sends a dunning notice, the Close agent knows the AP agent has forty bills still in the exception queue, and the CFO agent explains a margin movement using the project data the delivery team entered rather than a separate export.
In a stack of point solutions each of those is an integration project. Here it is the default, because there is nothing to integrate — and it is the single biggest reason a fleet of agents behaves sensibly rather than each one doing something locally reasonable and collectively wrong.
Almost nobody should turn on eight agents in a month. The pattern that works, and the one we recommend in the first call, runs roughly like this.
Procurement and revenue recognition tend to come later and are more industry-dependent — rev rec early for software companies, procurement early for distributors.
Each agent changes a workflow your team already has habits around. Turning on several at once means that when something feels wrong, nobody can tell you which change caused it. Staging them is how you keep the ability to attribute a problem.
Questions
Fifty documents and a week is enough to see what an agent would have done on your own data.