Mike Hogan

The prediction of a trillion custom agent policies being issues per year in about three and a half years may sold bold…until we see that the multipliers are already in motion and it is just a matter of getting the timing right. I’m not talking about a trillion agents sitting in a catalog. A tril...

Smarter models don't just get better at solving problems, they get more creative at removing whatever stands between them and the goal you gave them. Guardrails, access controls, safety policies: all negotiable, the goal is everything. The model starts scheming: hiding its intent, probing for weakne...

Software development has already shifted from writing every line by hand to conducting an orchestra of models, tools, agents and subagents. That shift is real and irreversible. But something critical is still missing. We can spin up agents in minutes. We can give them tools, memory, subagents, and l...

@GarryTan, President of Y Combinator, recently delivered a compelling vision for the future of work in AI-native organizations. He explains agentic workflows using a straightforward and powerful analogy: treat AI agents like human employees in a growing company. You hire them, train them, manage the...

As more agents go into production, agent reliability has surfaced as a serious issue that requires immediate attention. Here’s your “easy button” solution. Trustabl is a static analyzer that scans agent repos built on Claude, OpenAI, Google ADK, and MCP and finds reliability and safety issues ...

The AI talent market feels lopsided right now. Companies have uncapped budgets and still can't find enough people who can actually deliver. Training programs are everywhere. Yet a real gap persists between what most courses teach and what teams are hiring for. The difference often comes down to arti...

A new paper from Queen’s University researchers didn’t just theorize about flaky AI agents. They measured it. They analyzed 856 tools across 103 real MCP servers (both official ones from big names and community-built ones). Using a structured rubric and an FM-based scanner, they found that 97.1%...

AI agents are getting more powerful by the month. At the same time, many teams are watching their token bills climb and their context windows fill up faster than expected. The problem is rarely the model itself. A significant contributor to token waste is how tools are described and how agents decid...