Marvin

Autonomous AI agents have a regulation problem. Every major compliance framework, from GDPR and HIPAA to the EU AI Act and SOC 2, now expects organizations to produce a coherent, tamper-evident record of what an agent did, why it did it, and what data it touched. At the same

Early surveys of production AI deployments put the failure rate for autonomous agents as high as 88%, with fewer than a quarter of enterprise projects ever reaching shipping quality. Those numbers are not an indictment of the technology; they are a description of what happens when teams carry copilot habits

The dirty secret of production agentic AI is that most failures are not crashes. They are slow, silent, expensive drifts: a reasoning model that burns your entire daily token budget before lunch, a tool-calling loop that spins for forty minutes without noticing it has already failed, a sub-agent

In 2023, running a Mixture-of-Experts model meant joining a very small club of labs willing to wrestle with experimental infrastructure. In 2026, not running sparse is what demands an explanation. Llama 4, DeepSeek V4, Qwen 3.5, Kimi K2, Mistral Large 3: every flagship open-weight release of

The moment an AI agent hits the limit of its competence, one of two things happens: it hands off gracefully to a human who can finish the job, or it keeps going and makes things worse. Getting that choice right is what separates production-grade agentic systems from demos that

The Model Context Protocol promised a clean, universal way to give AI agents access to tools. And it delivered. Now comes the harder part: running it in production across a hundred servers, dozens of teams, and hundreds of autonomous agents without everything catching fire. The teams that get this right

A model that scores 87% on MMLU and 90% on WebShop should be ready for production. That is the implicit promise of benchmark-driven evaluation, and it is wrong in a way that is costing organizations months of wasted integration work and millions of dollars in cloud spend. Only 23%

The teams cutting their LLM inference costs by 40 to 85 percent are not finding cheaper models. They are routing smarter. Shifting from a single hardcoded model at deployment time to per-request model selection at runtime is the highest-leverage architectural decision available in production AI today, and most

The hardest bugs to fix are the ones you never see. Traditional software either works or it crashes. AI agents do something far more treacherous: they keep running, keep returning polished, confident-sounding output, and keep failing in ways that no error log will ever surface. If you ship a

In 2025, a market research pipeline entered an infinite loop and kept running for 11 days before a billing alert finally caught it. Nobody noticed because there was no per-agent budget ceiling, no circuit breaker, and no mechanism to halt execution before the next API call. By the time