The Wall
After 15+ years of building platforms that made the right path the easy path, I hit a wall with AI-generated code. The same philosophy that worked for infrastructure—guardrails over gatekeepers, automation over documentation—wasn't being applied to the tools that were supposed to make us faster.
GitHub Copilot knew nothing about my patterns. ChatGPT suggested approaches I'd banned in 2019. Every AI tool generated generic code that broke production. I was spending MORE time fixing AI output than writing code myself.
The Problem With AI Tools
- 3 hours debugging—Copilot missed the custom auth layer
- Failed audit—AI code missing HIPAA logging requirements
- Production incident—deprecated API patterns from Stack Overflow circa 2018
- 60-80% of time adapting AI suggestions instead of shipping
The irony wasn't lost on me. I'd spent my career making infrastructure self-service. Now AI was supposed to be the ultimate self-service tool, but it had zero context about the standards I'd spent years encoding into platforms.
Part 1: Context Engineering
The solution was obvious once I framed it the right way: give AI the same context that made my platforms work. ADRs. Code-maps. Architectural decisions. Compliance requirements. The organizational knowledge that lives in wikis, docs, and senior engineers' heads.
I called it Context Engineering—the practice of structuring organizational knowledge so AI can access it at generation time. Not prompt engineering (that's one-off). Not fine-tuning (that's expensive and static). Context Engineering is dynamic: when AI generates code, it queries your knowledge base first.
How Context Engineering Works
Ingest Knowledge
ADRs, code-maps, standards → vector database
Query at Generation
AI finds relevant patterns before writing code
Validate & Learn
Each failure becomes a new standard
The same pattern I'd used at Pearson (Nibiru), Aetna (Utopia), Liberty Mutual (Fusion), and Comcast (SEED): make the right path the easy path. Except now the "path" was AI-generated code, and the "guardrails" were organizational knowledge encoded as context.
Part 2: Building OutcomeOps
I didn't just theorize about Context Engineering—I built OutcomeOps to prove it. The platform ingests ADRs and code-maps, vectorizes them, and feeds relevant context to AI at generation time. Jira issue comes in, AI queries your standards, generates code that matches YOUR patterns, creates a PR, and the review loop catches anything that slipped through.
In Production at a Fortune 500
OutcomeOps is currently running at a Fortune 500 hospitality company. Not a pilot. Not a proof of concept. Production. These are real metrics from real enterprise development work:
Proof of Scale: MVP in 25 Days, Full Platform in 120
I also used OutcomeOps to build a complete serverless SaaS platform solo. MVP was live in 25 days. By day 120: 90 Lambda functions, AI image generation, AI video, AI chat, affiliate system, payment processing, content moderation—a fully featured platform. Not prototypes. Production code with tests, following consistent patterns, deployable to AWS. Context Engineering scales from enterprise teams to solo developers.
Part 3: Enterprise Ready
OutcomeOps isn't a toy. It's built for enterprise: air-gapped deployment options, GovCloud and FedRAMP ready, SOC2/HIPAA compliance features, ADR traceability for every line of code. The same rigor I brought to Gilead's Landing Zone applied to AI-assisted development.
Enterprise Features
Air-Gapped Deployment
Zero data exfiltration, all processing on your infrastructure
GovCloud & FedRAMP Ready
Deploys to AWS GovCloud with Bedrock for federal workloads
ADR Traceability
Every line of code traceable to architectural decisions
Self-Correction Loop
AI validates and fixes its own output automatically
The Evolution
OutcomeOps is the logical evolution of everything I've built. The philosophy hasn't changed—make the right path the easy path, guardrails over gatekeepers, self-service over tickets. The technology changed. AI is the new infrastructure. Context is the new configuration.
"DevOps automated deployment. OutcomeOps augments development."
Next time AI suggests code, it won't be generic. It'll be yours.
The Thread Through My Career
- 2009 - Broadhop: Self-service MongoDB for telecoms
- 2012 - Pearson: Self-service AWS (Nibiru)
- 2014 - Aetna: Self-service containers (Utopia)
- 2016 - Liberty Mutual: Self-service Docker (Fusion)
- 2019 - Comcast: Self-service infrastructure (SEED)
- 2022 - Gilead: Self-service cloud accounts (Landing Zone)
- 2025 - OutcomeOps: Self-service AI code generation