Feature-Flagged AI Runtime Controls
Build a local Python runtime-control layer that uses feature flags to select model routes, prompt versions, tool authority, retrieval corpora, reasoning modes, and kill switches before an AI workflow executes.
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Long-form AI engineering articles for systems, workflows, architecture, and production decisions.
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Aug 29, 2026latest update
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Evaluation & Governance
13 articles found
Evaluation & Governance
13 articlesChain-of-Thought Control Contracts for AI Systems
Turn chain-of-thought from a prompt habit into an AI engineering contract with private reasoning modes, visible rationales, evidence, logging policy, and evaluation checks.
Tool Result Admission Gates for Production AI Agents
Build a Python tool-result admission gate that rejects unsafe tool output, redacts sensitive fields, packs only admitted context, and uses Microsoft Foundry for final synthesis.
Golden Dataset Governance for AI Evaluation Systems
Build a markdown-first governance package that treats golden eval cases as controlled production assets with labeling rules, adjudication, versioning, leakage controls, and release evidence.
Two-Layer Content Moderation Gates for AI Systems
Build a Python moderation gate that scores text with Detoxify, asks a local Ollama model for structured review when policy requires it, and keeps the final content-risk decision in deterministic code.
Prompt and Context Lineage for Reproducible AI Systems
Define a production prompt and context lineage contract that records effective model inputs, protects sensitive content, and supports evidence-based incident replay.
Human Approval Gates for High-Risk AI Actions
Build a local C# approval workflow where risky AI-proposed actions create explicit approval requests, consume scoped expiring tokens, and leave behind auditable decision records.
MCP Tool Contract Gates for AI Systems
Treat live MCP servers as versioned tool contracts. Discover live schemas, replay frozen probes, and block risky drift before promotion.
Model Upgrade Gates for AI Systems
Treat model swaps as controlled experiments: frozen evals, shadow replay, deterministic scoring, and explicit promotion gates before an AI system changes models.
Trusted Agent Operations with Least-Privilege Tools and PII-Safe Context
Build a support agent workflow in C# with trusted vs untrusted content labels, deterministic tool authorization, approval-gated sensitive actions, and redacted audit storage.
Prompt Versioning and A/B Evaluation
Build a local-first prompt experiment harness with frozen eval cases, deterministic scoring, live LM Studio execution, and evidence-based prompt promotion.
Evaluation, Observability, and Feedback Loops in Production AI Systems
Build an evaluation harness with telemetry and feedback loops to gate local AI releases with measurable reliability. Track quality over time, catch regressions early, and ship with clear deployment thresholds.
Guardrails, Constraints, and Failure Modes in Production AI Systems
Build a minimal but production-aligned guardrailed AI assistant in C# that runs fully locally using Ollama, focusing on clear boundaries, strict contracts, deterministic execution, and safe failure modes.