AI harnessing: embedding AI models in software with control
A language model on its own is not a product. The harness — the structure of prompts, tools, rules, tests and monitoring around the model — is what turns it into a reliable system. Harness engineering is our core craft: we build AI harnessing frameworks for companies that want to run AI in production software.
What harness engineering for AI means
A harness is everything that guides and constrains a model: system prompts and context, the allowed tools, input and output validation, retry and abort rules, evaluation cases and observability in production. Without a harness, a model looks great in the demo and behaves unpredictably in production.
Enterprise AI harness engineering therefore means treating this structure as a software artifact in its own right: versioned, tested, documented — and independent of the specific model, so you can switch vendors without rebuilding everything.
LLM harnessing architecture: deterministic where it counts
Our AI model harnessing strategy follows one principle: the model only decides where judgment is required. Everything else is deterministic code. That leads to an LLM harnessing architecture with clear layers:
- Prompt engineering and harnessing belong together: prompts are code, with tests and versioning
- Structured outputs with schema validation instead of free text
- AI agent control harness: permissions, budgets and abort criteria per agent
- Deterministic AI harnessing: reproducible flows, traceable decisions
- LLM evaluation harnessing: test cases from real data, automated before every release
Sherpa: our open-source tool for harnesses
Because we kept needing the same structure, we turned it into a tool: Sherpa is our open-source harness generator. It creates and manages the skeleton of an AI harness — roles, skills, rules and documentation — and helps migrate existing setups. We use Sherpa in our own projects and develop it in the open.
Who AI harnessing is for
For teams that already have a prototype or a first AI feature and notice that quality fluctuates, costs are unclear, or nobody can say why the model answered differently yesterday. And for teams that want to run AI agents in production and need control for that — not just hope.
Frequently asked questions about AI harnessing
What is the difference between prompt engineering and AI harnessing?
Prompt engineering phrases what the model should do. AI harnessing builds the structure around it: validation, tools, tests, boundaries and monitoring. Good prompts are part of the harness — but only a part.
We already have an AI feature. Do we still need a harness?
If quality fluctuates, costs are unclear or prompt changes have unpredictable effects: yes. A harness makes existing features testable and stable — usually without rebuilding them.
Does the harness lock us into one model vendor?
The opposite. A well-built harness encapsulates the model behind a clear interface, so you can switch vendors and use the evaluation set to check that quality stays the same.
Is Sherpa free to use?
Yes, Sherpa is open source and publicly available on GitHub. We are happy to help introduce it in your project — but that is not a requirement for using it.
Is your AI feature running reliably yet?
Show us your setup — we tell you honestly where the harness is missing and what it would bring.