Agent harnesses for
AI-native business systems.
A custom business ontology and an agent harness turn generative models into governed, verifiable work.
The idea
Models generate.
Harnesses deliver.
Ontologies govern.
A language model can write and reason. An agent harness is the runtime around it—planning, tools, memory, approvals, recovery, and telemetry—so work finishes reliably instead of stopping at a clever answer.
A business ontology is the shared map of your objects, relationships, rules, and permitted actions. Agents act on how the enterprise actually operates—not on generic text from the open web.
Progress is jagged where verification is easy. The harness and ontology make verification—and therefore automation—real in the business.
Closed systems, open options
Your alpha stays yours.
The durable pattern is vertical: AI-native applications inside your own boundary—identity, data, networking, and audit under your control. Rent frontier capability when that is efficient, mix open and closed weights when you need specialization, and keep the operating edge in your application layer.
Not open versus closed as ideology—a governed compound system: the right models, wrapped in a harness, grounded in an ontology, validated against outcomes that matter.
The argument
Said plainly by people who built the stack.
“Traditional computers can easily automate what you can specify in code. LLMs can easily automate what you can verify.”
“We have this thing called ontology that now everyone’s copying, but de facto it takes a large language model and makes it safe and useful and precise.”
“The ontology is the body to the AI brains. You cannot actually interact with the enterprise or affect the world; your agents can go nowhere without ontology.”
“Frankly, I think closed models are cheaper. If you don’t have to build yourself… there is nothing cheap about doing all of that yourself. The reason you need open models is because you need to have control, because you need to adapt something for your own very specialized use cases.”
The pattern
Why this is a good way to build
You can check answers without an ontology—and still not know what the business allows. You can map the business without a harness—and still never finish the work. Models alone produce text; they do not know your objects, rules, or next step. The working system is a custom ontology that names the enterprise, plus an agent harness that plans, calls tools, remembers, asks for approval, recovers, and proves what happened.
That combination is what turns a general model into something that can do underwriting, logistics, field services, or knowledge work—inside real permissions, real data, and real outcomes—rather than producing generic text about them.
Going further
If the framing clicks.
Some people want the deeper cut—how a harness and ontology are assembled, trained, and run. That conversation starts here.
agent@benefitbalance.com