Marvin

Most enterprise AI governance frameworks were designed with a specific mental model in mind: a model receives input, produces output, a human reviews it. That mental model is now dangerously out of date. Autonomous agents don't just answer questions. They call APIs, write files, trigger workflows, modify databases,

AI coding assistants have made code generation dramatically faster. They have not made verification any faster at all. That gap is where production incidents live, where security vulnerabilities hide, and where the promise of shipping velocity quietly collapses. The fix is not to slow down generation. It is to build

Routing every request through your most powerful model is the single most expensive mistake in production AI. A well-designed routing layer can cut inference spend by 40 to 85 percent while preserving 90 to 95 percent of the quality you get from frontier models on every query. This article

Multi-agent AI systems fail silently. That is the central, uncomfortable truth separating production deployments from impressive demos. An agent completes its task, returns a confident answer, and hands off to the next stage. The pipeline finishes, the logs show green, and somewhere downstream a customer is looking at a

Software teams are living through a productivity paradox. AI coding assistants have made it faster than ever to write code, but that speed has created a new and urgent bottleneck: your team cannot review code as fast as AI can generate it. And the code being generated carries real, measurable

Most AI agents fail not because the model is wrong, but because they forget. An agent can reason brilliantly for hours and then collapse under the weight of its own accumulated context, losing track of what it tried, what it learned, and where it left off. The lesson is deceptively

Autonomous AI agents are moving into production faster than organizations can govern them. Industry analysts estimate that 75% of enterprises plan to deploy agent fleets by the end of 2026, yet fewer than 30% have governance strategies actually designed for agentic systems. The rest are running autonomous software on frameworks

Bigger context windows were supposed to solve the memory problem for AI agents. They haven't. Despite a roughly hundredfold expansion in token limits since 2020 (from a few thousand tokens to over a million in models like Gemini 1.5 Pro and Claude), agent performance on complex, long-

The Model Context Protocol crossed 97 million SDK downloads in mid-2026. Every major AI vendor now supports it. The conversation inside enterprises has shifted from "should we adopt MCP?" to "how do we keep it from becoming a governance nightmare?" The answer lies in three

The autonomous AI agent market is projected to reach $35 to $45 billion by 2030, but a messy infrastructure problem is threatening to slow the ride. Every agent framework has its own conventions for connecting to tools, discovering capabilities, and coordinating with other agents. The result is a combinatorial explosion