When agents forget purpose, governance has a context problem
When long-running AI agents summarize their own context to stay within token limits, they're deciding what to forget. That's not an engineering problem — it's a governance one.
LLMs create a new blind spot in observability
LLMs break traditional observability — and that creates a compliance gap most governance teams haven't addressed yet. If you can't trace the full AI pipeline, you can't audit it.
Without observability, AI fails in silence
SaiKrishna Koorapati's piece in VentureBeat makes the case that observable AI isn't about adding monitoring dashboards. It's about audit trails that connect every AI decision back to its prompt, policy, and outcome
Why AI agents work in demos but fail in production
A new research paper from Stanford, Harvard, UC Berkeley, and Caltech — "Adaptation of Agentic AI" — provides the clearest framework I've seen for diagnosing what goes wrong when agentic AI systems move from controlled demonstrations to real-world deployment.
Linux Foundation governance gives AI agents the trust infrastructure they need
Before MCP, every AI application needed custom connectors for each data source. Without foundation governance, that success creates three risks: proprietary lock-in, protocol fragmentation, or de facto control by a single company. AAIF prevents all three
AI agents fail in production because teams skip the boring parts
You can't eliminate non-determinism in LLMs, and you shouldn't try. The goal is management, not elimination.
What if the genius isnt?
Most teams building agentic AI are trying to make LLMs do something they're fundamentally bad at: making consistent, defensible business decisions. Decision platforms were built for this exact problem - and most teams don't know they exist.
Red Teaming
At its core, AI red teaming is a multidisciplinary, creative, and interactive process of investigation
