AI agents on your own infrastructure

An AI agent that can only chat is a demo. An agent that can query your database, open a ticket, file a report and restart a job is an employee. The difference — and the risk — is access.

The harness

We never wire a model directly to production systems. Agents run inside a harness: a layer that decides what the agent may touch, records everything it does, and inserts human checkpoints where the cost of a mistake is real.

Concretely, the harness gives you:

• Scoped access. The agent sees the tools it needs for its job and nothing else.
• Full logs. Every action, every input, every output — auditable after the fact.
• Checkpoints. Reversible actions run autonomously; irreversible ones wait for a human click.

With that structure, agents can safely operate real parts of a company: triaging inboxes, reconciling data, drafting the weekly report, watching pipelines and escalating only when something breaks.

When the data can’t leave the building

For most workloads, a frontier model over an API is the right call — best capability, zero maintenance. But some data genuinely cannot leave your premises: patient records, deal flow, anything under a strict compliance regime.

For those cases we deploy a local model — currently GLM 5.2 — on your own hardware. It trades some raw capability for a hard guarantee: the prompt and the data never cross your firewall. The harness stays identical, so workloads can move between API and local deployment as sensitivity requires.

Boring first, smart second

The uncomfortable truth after five-plus years of automating workflows: most “AI problems” are cron problems wearing a costume. We add the agent where judgment is genuinely needed — and a plain scheduled job everywhere else. That’s usually the difference between a system that demos well and one that runs for years.