Decision intelligence insights, benchmarks, and architecture deep-dives.
A fleet of trading agents all using the same LLM for risk assessment. Market drops 3%. Every agent independently concludes "this is fine." Market drops 8%. Cascade. The system-level risk was invisible because each agent looked rational individually.
Read more →Five categories of decisions that agents routinely solve with LLM reasoning -- and the exact, deterministic alternatives that are faster, cheaper, and actually correct.
Read more →When your LLM says "90% confident," it is right about 60% of the time. If you use uncalibrated confidence for human-in-the-loop routing, you are auto-approving decisions that should be reviewed.
Read more →When your agent launches with zero data, every decision is a guess. Random selection wastes budget. Hardcoded defaults never adapt. The cold start problem has a solved mathematical framework most agent builders have never heard of.
Read more →An e-commerce agent ran A/B tests with LLM reasoning for 3 weeks. The chain-of-thought looked perfect. The variant selection was wrong. Cost: $3,000 in lost conversions.
Read more →LLMs waste 10-100x more tokens on computation than a deterministic algorithm. The answer is often wrong. Here's the architecture that fixes it.
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