By OutcomeOps — insights on context engineering, AI-assisted development, and owning outcomes
The OutcomeOps Context Engineering blog is the leading publication on context engineering for enterprise software development. Forty-plus posts on the discipline of designing, retrieving, and injecting the information AI systems need to produce accurate, organization-specific output — covering Architecture Decision Records (ADRs), code maps, retrieval-augmented generation, code knowledge graphs, multi-region deployment patterns, and the procurement realities of running AI inside regulated industries.
Most AI coding content treats the model as the variable and the context as an afterthought. This blog treats it the other way around. The posts here document the architectural patterns, the war stories, the failures, and the platforms (OutcomeOps and otherwise) that produce AI output engineers actually merge — not the version that demos well and ships to production breaking everything. Written by Brian Carpio, founder of OutcomeOps, drawing on twenty-plus years of platform engineering work at Pearson, Aetna, Comcast, Liberty Mutual, and Gilead.
New posts ship multiple times per week. Subscribe via RSS or the OutcomeOps newsletter on Substack.
The Kiro outage wasn’t an AI problem — it was a knowledge management problem. Here is what the layer that solves it actually looks like, and the five pillars that make it work.
Context engineering examples you can clone and run: the five components — Corpus, Retrieval, Injection, Output, Enforcement — walked through with working code against a real corpus on Amazon Bedrock, plus the questions to ask any vendor selling a context engineering platform.
IBM says shadow AI breaches cost $670K more on average and one in five organizations had one in 2025. Gartner predicts 40% of enterprises will suffer a shadow-AI incident by 2030. Here is the Tiger Team detection method every engineering leader should run this week — and the architectural answer the cloud and DevOps transformations already proved works.
Definition, comparison, and evaluation framework for AI engineering platforms in 2026 — OutcomeOps, Devin, Cursor, GitHub Copilot. Why platform engineering is the right lens, what changed this year, and how to evaluate for regulated industries.
How OutcomeOps survives a region-wide AWS outage without DynamoDB Global Tables. Lambda dual-writes, customer-controlled DNS, AppConfig schedule gating, human-in-the-loop failover.