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Engineering notes from the AI frontier
Architecture deep-dives, production war stories, and decision frameworks from the teams building enterprise AI, data, and automation systems — written by the engineers doing the work.
Designing Guardrails for Production Agentic AI Systems
A layered architecture for constraining autonomous agents in production: tool permissioning, plan validation, budget enforcement, human checkpoints, and post-hoc audit — with concrete failure modes each layer prevents.
RAG vs. Fine-Tuning: An Enterprise Decision Framework
When to retrieve, when to fine-tune, and when to do both — a decision framework based on knowledge volatility, behavior specificity, latency budgets, and total cost of ownership, with numbers from production deployments.
LLM Observability: What to Measure Before Your Users Do
A practical observability stack for LLM applications: structured traces, online quality signals, drift detection, and evaluation suites that catch regressions before deployment — with the specific metrics that matter.
From RPA to Agentic Automation: A Migration Path That Doesn't Break the Back Office
How to evolve a brittle RPA estate into agentic automation without a risky rewrite: triaging bots by failure economics, an API-first interception layer, and where LLM agents genuinely outperform deterministic scripts.
Cloud Architecture for AI Workloads: Engineering the Cost Curve
Why AI workloads break traditional cloud cost models, and the architecture decisions that fix it: serving-tier design, GPU capacity strategy, caching layers that cut inference spend 40-70%, and a FinOps model for tokens.
Machine Learning Models That Survive Production: An MLOps Field Guide
Why most ML value is lost after deployment, not before — training-serving skew, feature stores, shadow deployment, drift monitoring, and retraining triggers, drawn from operating models in regulated enterprises.
Data Contracts for Reliable AI Pipelines
AI systems fail at the data layer more than the model layer. How data contracts — schema, semantics, SLAs, and enforcement — stop upstream changes from silently corrupting features, RAG corpora, and training sets.
Event-Driven Architecture for Enterprise Integration: Patterns That Scale, Traps That Don't
A field guide to event-driven integration at enterprise scale: event design and versioning, outbox and saga patterns, exactly-once myths, and why your event backbone is becoming the substrate for AI and agentic systems.
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