Harness Engineering is how Kaidera turns AI output into controlled work. It gives AI workers a bounded environment, approved tools, a visible evidence trail, rollback points, and clear review gates before anything important changes.
Controlled work loop
The harness makes the invisible parts of AI work inspectable: boundary, action, evidence, and approval.
The user defines the business result, constraints, and what review-ready should mean.
The platform scopes files, tools, connectors, permissions, and actions for the task.
AI workers act inside the harness, with tool use and changes tied back to the work item.
Tests, screenshots, notes, changed files, and risks become review material.
Humans approve important moves after the evidence is clear.
The goal is not to expose technical internals. The goal is to make AI work predictable: you know what the worker is allowed to do, what it actually did, and what needs your approval.
Each task starts with scope: what the AI worker may change, what it must leave alone, and what evidence is required before the work can move forward.
AI workers do not receive a blank cheque. The harness exposes only the tools, connectors, files, and actions that are appropriate for the task.
Plans, screenshots, tests, notes, changed files, review outcomes, and remaining risks stay attached to the work item instead of living in a chat transcript.
The harness can prepare work and collect proof, but important promotion decisions remain approval-led so business owners retain control.
Review package
Harness Engineering is not just execution control. It is also review design. A business owner should be able to understand what happened, what passed, what remains risky, and what decision is being requested.
Goal, boundary, expected output, and approval conditions are visible before the worker starts.
Tool use, decisions, files, screenshots, and verification results stay attached to the task.
Reviewers see what changed, what passed, what remains risky, and what needs approval.
The harness controls how work happens. Model configuration controls which AI capabilities power that work.
Model Configuration →
