Service
AI Harness Engineering
We build the execution, memory, and safety layers that wrap around LLMs. By engineering custom harnesses, we ensure your AI agents operate within strict guardrails, handle errors gracefully, and deliver predictable results.
Book an architecture callBecause raw models aren't enough.
An LLM on its own is just a stateless text-prediction engine. To act as an agent, it needs a harness. The harness is the software layer that drives the model, parses its tool-calling intents, executes those functions, manages short- and long-term memory, and decides when the loop should safely stop.
Agent = Model + Harness. We specialize in building the custom harnesses that turn raw models into reliable, production-ready business systems.
Our Harness Engineering Stack
- ◆ Context Engineering Actively managing what goes into the LLM's context window at each step, ensuring the model only receives highly relevant, structured data.
- ◆ Decision Traces & Observability Logging every single step, prompt, tool call, and reasoning path. This creates a transparent audit trail (decision trace) for debugging and compliance.
- ◆ Deterministic Guardrails Hard-coding safety boundaries, budget caps, rate limits, and stop conditions so agents can never run out of control or access unauthorized systems.
Frequently Asked Questions
What is the difference between scaffolding and a harness?
Scaffolding is the static configuration layer (system prompts, tool descriptions, and instructions). The harness is the dynamic execution layer (the code that calls the model, executes tools, handles errors, and manages the stateful loop).
Why do enterprise agent projects fail without a harness?
Most fail because they lack structured error handling, state management, and decision traces. When a model returns unexpected output, the system crashes or loops infinitely. A harness catches these edge cases and enforces deterministic safety boundaries.
Are your harnesses model-agnostic?
Yes. We build harnesses that decouple the execution logic from the underlying LLM. This allows you to swap models (e.g., from Claude to GPT or DeepSeek) as technology evolves, without rewriting your entire system.
Testimonials
What they say about Us?
Here's what partners shared about working with our team.
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“Working with Ali has been a game-changer for us. He is an absolute gem of an expert in Bubble.io, Cloudflare, Websockets, and many other domains. But what I love the most about working with Ali is that he can seamlessly bridge the tech and business sides. He is strategic, visionary, humble, and eloquent. Strongly recommend working with him if you get the chance.”
Next step
Ready to engineer your AI harness?
Book a short strategy call and we'll help you design a secure, robust cognitive architecture for your business.
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