Welcome

Purpose-built AI tooling cuts per-task cost 21-68%. Three-cohort model and four-phase operating framework for engineering leaders past the 50% adoption plateau.

Misclassifying reversible decisions costs more than the decision itself. Four frameworks unblock AI delivery: Type 1/Type 2, Eisenhower, DACI, and PMBOK.

Can an AI agent predict scope without hallucinating? We ran 90 trials. It added 1.7 phantom files per change. Error budgets and trust ladders are the gate.

7,432 pages to 3-second answers. Production RAG for legacy systems with model-agnostic reranking. No vendor lock-in, validated across 4 LLM families.

50% cost reduction with subagent architecture for AI coding. Capable models for planning, fast models for building. Real metrics from Goose.

AI made writing code faster. Without governance controls, it makes reviewing and understanding code slower. The accountability layer is the structural response.

We tested prompt repetition on 20 parallel AI agents. Ceiling effects dominated both experiments. The null result is a finding about evaluation design.

Why your AI agent failed: missing decision provenance, not metrics. The 3 observability gaps traditional monitoring won't catch.

Layer AI agents over legacy systems without modernization. 30-80% productivity gains in 3-6 months. Patterns that bypass technical debt.

Slopsquatting: attackers register packages AI hallucinates. XZ Utils showed the stakes. A framework to assess your AI supply chain exposure.