Agentic AI engineering for real business work

We build AI agents that gather context, use approved tools, and complete measured tasks. You keep control of data, access, and deployment.

From chat to completed work

An AI model can suggest an answer. An agent can inspect a report, call an API, update a system, and verify the outcome.

Operations agents: process reports, tickets, documents, and routine checks.
Software agents: inspect repositories, prepare changes, and run tests.
Research agents: collect evidence from approved sources and produce cited findings.
Private assistants: work with internal knowledge without exposing broad system access.

We engineer the layer around the model. That layer controls what the agent sees, which actions it may take, and when a person must approve a step.

Each tool gets a clear contract, limited access, timeouts, and useful error messages.

The agent receives current task data without filling its context with unrelated history.

Logs, test cases, costs, and outcomes show where the workflow succeeds or fails.

Choose one workflow. We define the input, desired result, risks, and success measure.
Build a controlled pilot. The agent receives only the tools and data needed for that task.
Test real cases. We measure quality, cost, speed, and unsafe actions before launch.
Deploy and hand over. You receive the code, operating notes, controls, and agreed support.

Our approach comes from daily work with OpenCode and our own small Python harness. Read the practical details in our harness engineering article.

Bring one repetitive process and several real examples. We will identify useful actions, required approvals, and a clear acceptance test.