.1AI Across IndustriesSeptember 15, 2026 Karina Governance and Traceability for Agentic AI: Closing the Accountability GapEnterprises run AI agents in production faster than they build controls for them. Agents decide in the moment, based on what they retrieved and in what order, so an API log shows that an agent acted but never why. In regulated sectors, an answer you cannot trace is an answer you cannot use. Here is what it takes to reconstruct every agent decision.
.2AI Across IndustriesSeptember 1, 2026 Karina EU AI Act 2026: What the Delay Actually Changed for Enterprise AIBoards heard "the AI Act is delayed" and stood down their programs. Annex III high-risk obligations did move to December 2027, but Article 50 transparency applied on 2 August 2026, and the Article 99 penalty regime has been live since 2025. The conformity evidence due in 2027 has to accumulate from the systems running today.
.3TechnologyAugust 18, 2026 Karina The Knowledge Layer Multi-Agent Systems Actually NeedEnterprises are building multi-agent systems backwards. All the investment goes into how agents communicate, almost none into what they reason on. Orchestration coordinates the workflow, but over ungoverned data it just helps agents pass flawed assumptions to each other faster. The fix is a shared knowledge layer
.4AI Across IndustriesAugust 11, 2026 Karina Adopting AI in a Regulated Enterprise: Moving Beyond the Proof of ConceptEnterprise AI has split into two camps: those running experiments and those in production. For regulated industries, crossing that gap is brutal — 62% of enterprises stay stuck in pilots. The blocker is rarely the model. It is a foundation that treats data governance as a patch instead of the structure holding everything up.
.5AI Across IndustriesAugust 4, 2026 Karina How Enterprise AI Should Actually Be Built: Context First, Model SecondMost enterprise AI starts with the model and recovers context later — which is why accuracy breaks in regulated work. The fix is to reverse the sequence: build a validated semantic layer before the LLM touches your data. See how a context-first architecture hit 97% accuracy where conventional RAG stalled at 80%.