Engineering, security, formal methods, and building trustworthy multi-agent systems.
MAREF Engineering
Gartner says 40% of enterprise apps will have AI agents. 88% already had an incident. 17x more spent on AI-powered security than securing AI itself. Only 6% have a mature strategy. 7 numbers, one message: incidents are catching up to scale.
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630M repos, half of new code is AI-written, trust dropped from 77% to 60%. CMU found 6M fake stars. AI code has 1.7x more critical bugs. AI users are 19% slower but think they're 20% faster.
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Multi-agent systems are entering production. Without governance, every agent is an unmonitored vector for data leaks, hallucination propagation, and adversarial exploitation.
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Most security tools are 'tested.' MAREF is proved — using TLA+ model checking, Lyapunov stability analysis, and mathematical convergence guarantees.
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OWASP published the Agentic Top 10 — the definitive threat model for multi-agent systems. MAREF is the first open-source framework to cover all 10.
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LangGraph vs CrewAI vs AutoGen vs MAREF: a buyer's guide to agent governance frameworks. Compare runtime safety, formal verification, state machines, and production readiness.
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Runtime safety for multi-agent AI systems: identity isolation, circuit breakers, drift detection, and blast radius control. Production patterns for preventing agent runaway.
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Lyapunov-proven convergence, red-blue adversarial training, and constitutional safety constraints that enable autonomous AI improvement without runaway risk.
Read more →Frankie
After three decades as a steadicam operator on Hollywood sets, I walked away to build safety systems for AI agents. Film sets taught me something Silicon Valley hasn't learned: when things go fast, safety isn't optional.
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