eval harness
eval harness at Beyond Market Intelligence is a file of 3 stories. The newest of them: “From Config Wrangling to Experiment Scaffolding: A PhD Reality Check”, “When AI Sounds Confident, Verify It Knows the Answer”, and “When AI Saves Time but Misses the Bottom Line”. The instinct to keep the eval harness and metric definitions close is the right one. Arun Mishra's eval harness exposed a flaw that should worry any team shipping LLM-assisted tools: the model was most confident precisely when it was most wrong. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every eval harness story on Beyond Market Intelligence, newest first.
From Config Wrangling to Experiment Scaffolding: A PhD Reality Check
The instinct to keep the eval harness and metric definitions close is the right one. That layer is where your scientific judgment lives. Handing off the scaffolding while holding onto the core is a workable line, even if you keep crossing it. The detachment you describe is real. The fix is likely not more careful diff-reading. It is rebuilding a mental model of the system at a higher level, so you can reason about the numbers without needing the code memorized.
When AI Sounds Confident, Verify It Knows the Answer
Arun Mishra's eval harness exposed a flaw that should worry any team shipping LLM-assisted tools: the model was most confident precisely when it was most wrong. Qualitative review caught nothing because the explanations sounded authoritative. That is the trap. We test for fluency when correctness is what matters. Mishra's approach, measuring accuracy against synthetic ground truth, reveals the gap between plausible and correct. It is tedious work, but skipping it means deploying tools that fail quietly.
When AI Saves Time but Misses the Bottom Line
A perfect eval score meant nothing when the CFO ran the numbers. The agent passed every metric in the harness I published, yet leadership killed it because its successful resolutions cost more than the humans it replaced. That is the gap between technical validation and business reality. Metrics can measure task completion, but they rarely capture true economic value. If you are exploring how AI agents actually perform in production, this story is a grounded reminder that context matters.