AI Agents & Agentic Tooling
Agents that do the work, not just talk about it, and do it under control. Every action runs behind approvals, an audit trail, and rollback, grounded on your data with guardrails, evaluation, and observability built in from day one.
Agents that act need brakes.
A chatbot answers. An agent acts, calling tools, updating records, moving money and work. That leverage is the whole point, and the whole risk. We build agentic automation the way you would build any system that touches production: least-privilege access, approvals on the actions that matter, a complete audit trail, and rollback when something has to be undone. Retrieval-augmented generation keeps answers grounded on your data instead of on guesswork, and evaluation harnesses prove behavior before it ships.
- ✓Approvals and audit, not autopilot. High-impact actions pause for a human; everything the agent does is logged with the reason it did it.
- ✓Grounded on your data. RAG over your systems and vector stores, so answers cite sources instead of inventing them.
- ✓Your data stays yours. On-prem and private VPC options; we do not train shared models on your content.
Illustrative offline-eval results across build iterations. Actual pass-rates measured on your task suite.
What “governed” should mean.
The full agentic stack.
From retrieval to guardrails to observability, engineered as one governed system, not a demo bolted to an API.
Agentic automation with approvals
Multi-step orchestration where the agent plans, calls tools, and completes real work, with human approval gates on anything sensitive, an audit trail on every action, and rollback for anything reversible. Autonomy where it is safe, a checkpoint where it is not.
Retrieval-augmented generation
RAG pipelines and vector databases that ground answers on your documents and systems, with citations, so responses are traceable, not hallucinated.
Tool use & function calling
Typed tools your agent can call safely, each one gated, logged, and scoped so the agent acts only within the boundaries you set.
Guardrails, policy & evaluation
Policy guardrails that block disallowed operations before they run, plus evaluation harnesses, offline evals and human-in-the-loop review, that measure quality, safety, and regression on your own task suite before anything reaches production.
Observability & tracing
Full-trace visibility into every step, prompt, tool call, token, and cost, so you can debug behavior and watch drift over time.
Model selection & fine-tuning
We benchmark candidates, including the latest Claude models and open-weight options, on the Zoneits Lab GPU cluster, and fine-tune when it earns its keep.
The controls around every action.
Governance is not a feature we add later. These are the primitives every Zoneits agent ships with.
| Control | Purpose | How it works |
|---|---|---|
| Approvals | Keep a human on high-impact actions | Sensitive steps pause and route to an approver before the agent proceeds |
| Audit trail | Prove what ran and why | Every prompt, tool call, and decision is logged with inputs, outputs, and rationale |
| Rollback | Undo cleanly when needed | Reversible actions are checkpointed so a change can be reverted to a known-good state |
| Evals | Catch regressions before release | Offline evaluation harnesses plus human-in-the-loop review score each build on your task suite |
| Access scoping | Limit the blast radius | Least-privilege credentials restrict the agent to only the systems and data its task requires |
From use case to governed production.
Frame
We pick a use case with real payback, map the data and systems it touches, and define what “good” and “safe” mean.
Retrieve
Build the RAG layer and vector store over your data, wire up tools, and select and benchmark models on the Lab cluster.
Guard
Add approvals, audit, rollback, policy guardrails, and access scoping; stand up evals and tracing to measure behavior.
Run & improve
Deploy on-prem or in your VPC, monitor traces and cost, and iterate on evals as the workload and models evolve.
AI agents, answered.
Is this just a chatbot?
How do you stop an agent from doing something harmful?
Do you use our data to train models?
On-prem or cloud, which do you recommend?
Which models do you use?
Automate the work.
Keep the control.
Tell us the workflow you want an agent to own. We will scope the data, the tools, and the guardrails, then prove it on your evals before it ever touches production.