Welcome
I'm Hugues Clouâtre, a technology executive specializing in AI and platform engineering. Former AWS and Oracle, now at Slalom.
Practical insights for CTOs and engineering leaders. Check out my About page to learn more.
Featured
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AI SDLC Governance: Three Layers for Engineering Leaders
• UpdatedA three-layer governance stack (comprehension, review gate, and observability) gives engineering leaders measurable control over AI-generated code volume.
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How Does Context Engineering Drive Multi-Agent Reliability?
• UpdatedContext engineering turns AI context into infrastructure, preventing the accumulation, starvation, and leakage failures that undermine multi-agent reliability.
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Open-Weight LLMs Reach the Structured Output Quality Ceiling
• UpdatedOpen-weight models now match closed-source on structured output at 95x lower cost. Pre-registered blind eval, 30 samples, zero quality delta.
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What a Null Result Taught Us About AI Agent Evaluation
• UpdatedWe tested prompt repetition on 20 parallel AI agents. Ceiling effects dominated both experiments. The null result is a finding about evaluation design.
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Orchestrating AI Agents: A Subagent Architecture
• Updated50% cost reduction with subagent architecture for AI coding. Capable models for planning, fast models for building. Real metrics from Goose.
Recent Posts
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AI Approval Gates: Engineering Oversight at Machine Speed
• UpdatedAI approval gates: reversibility-tiered human-in-the-loop design with four health metrics that prevent reviewer atrophy at machine speed.
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AI Adoption in Engineering: Breaking the 50% Plateau
• UpdatedPurpose-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.
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AI Delivery Decision Frameworks: Type 1, Type 2, DACI
• UpdatedMisclassifying reversible decisions costs more than the decision itself. Four frameworks unblock AI delivery: Type 1/Type 2, Eisenhower, DACI, and PMBOK.
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SRE for AI Agents: Error Budgets, Trust, and 90 Trials
• UpdatedCan 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.