Your AI agents forget everything between sessions. We detect context failures before they reach production, enforce structural fixes automatically, and compound every lesson into your system.
Article 9, 15, 17 compliance mapping with continuous enforcement verification
Risk management framework alignment across Govern, Map, Measure, and Manage functions
Security, availability, and processing integrity controls with audit trail export
AI agents are getting smarter. Their context management is getting worse.
Financial services firms face the highest AI failure rate. Most failures stem from context degradation that static checklists cannot catch.
EU AI Act penalties reach 35M EUR or 7% of global turnover. The cost of non-compliance dwarfs the cost of prevention.
40% of enterprise apps will embed AI agents by end of 2026. Only 6% have advanced context management strategies. The gap is the risk.
Hooks, tests, and CI gates enforce rules automatically. No one has to remember to check.
Every context failure becomes a lesson. Lessons get promoted to enforcement rules. Your system gets stronger over time.
Purpose-built for the August 2026 deadline. Risk classification, documentation, and audit trails from day one.
Whether you need a full platform, a SaaS integration, or expert hands, we have a path that fits.
Full-stack context engineering for enterprise agent deployments. Enforcement hooks, failure detection, pattern clustering, and continuous context health monitoring. Your AI agents stay on track, automatically.
Context health as a service. Plug into your existing CI/CD pipeline and get structural enforcement, context failure dashboards, and compliance tracking without building anything in-house.
Hands-on context engineering consulting. We assess your current state, deploy structural enforcement, and transfer knowledge to your team. Start with an assessment, scale to a retainer.
“FastAPI context health audit revealed a 71% enforcement gap -- automated recommendations generated in minutes, not months.”
— Open Source Context Health Audit, 2026
“LangChain has early context health signals but zero enforcement hooks -- 25 potential hardcoded secrets found across the codebase.”
— Open Source Context Health Audit, 2026
“75% of AI coding models introduce regressions on sustained maintenance. Our enforcement ladder detected 3,706 context failures and shipped 145+ specs autonomously with zero regressions.”
— Production Enforcement Results, March 2026
“Karpathy ran 276 autoresearch experiments in 48 hours. We applied the same pattern to enforcement rule optimization — 20 iterations, measurable improvement, pennies in API cost.”
— Autoresearch Validation, March 2026
Practical insights on enforcement automation, context health, and structural AI management. No spam.
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Submit your repository and we will deliver a structural context assessment -- failure mode scan, risk classification, and enforcement recommendations. No commitment required.