Harness Engineering
6 pieces in this thread.
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Making aec-bench Trainable with Prime Lab
How aec-bench and Prime Intellect's Lab turn engineering benchmarks into verifier-backed RL environments, adapter training runs, and inspectable traces.
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Executable Standards
Better tools and verifiers are not enough. The next harness boundary is the clause itself — turning standards, briefs, and codes into versioned predicates and replayable certificates.
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The Third Axis
What happens when you let the harness improve itself — two experiments in feedback-driven harness evolution, and an honest look at how rough the trajectory actually is.
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What If the Harness Could Improve Itself?
Applying the autoresearch pattern to self-improve an engineering agent harness. Automated prompt optimisation across HVAC audit tasks on Claude and GPT-4.1-mini, showing how harness engineering compounds when the improvement loop runs itself.
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Benchmarking Agents on Real Engineering Work Is Already Teaching Us Something Important
Benchmarking AI agents on real HVAC engineering tasks across Claude and GPT models. Results on harness-dependent capability, agent evaluation design, and why AEC-domain benchmarks reveal what general benchmarks miss.
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Where Capability Actually Lives in Agentic Engineering
In AEC and domain-specific engineering, AI agent capability lives not in the model alone but in harness engineering — the tools, verifiers, orchestration, and process design that make agentic work reliable.