From engagement to fulfillment: How Agentic AI is rewriting product metrics
As AI agents begin executing tasks on users’ behalf, traditional engagement metrics are becoming less meaningful. In the age of agentic AI, product teams may need a new north star: measuring whether user intent was successfully fulfilled.
When AI judges AI: The hidden dangers of reasoning models in alignment
The race to build more capable AI systems has created an unexpected problem:
As we push toward more sophisticated models, we need equally sophisticated ways to evaluate and align them.
Unlocking the power of data: How we built text-to-SQL with agentic RAG at Rocket Mortgage
Your company’s data holds answers, but accessing them is often the hard part. Here’s how Rocket Mortgage built a text-to-SQL system with agentic RAG to make data accessible to everyone.
Meta buys Moltbook: The social network where AI agents talk to each other
Meta’s acquisition of Moltbook highlights a growing focus on agent-to-agent systems and the infrastructure required to support them. It’s a small deal that signals bigger shifts in how AI ecosystems may evolve.
RAG shows its work. That’s not the same as being right.
How GenAI turns first-party data into revenue with LLM tagging, RAG traceability, and governance that protects trust.
The Google tool helping small AI models outperform the giants
What if the secret to better AI isn’t bigger models, but better tools?
Researchers at Google DeepMind have shown that smaller language models can outperform larger ones when they’re given the ability to write their own code.
How AstraZeneca is quietly rewiring Boston’s AI ecosystem
For AI professionals tired of hype decks and stalled pilots, AstraZeneca’s Boston strategy offers a practical blueprint for making AI work in complex, regulated environments.
The emergence of the AI Architect: Engineering the future of tech
According to Gartner, over 80% of enterprise AI projects fail to move beyond the prototype stage, highlighting the need for professionals who can design systems that work in the real world. Enter the AI Architect...
Operational stability for mission-critical ML systems
If observability tools can capture everything happening in modern infrastructure, why can’t AI systems clearly explain the decisions they recommend? This tension lies at the heart of the growing explainability crisis in applied AI.
AI’s new rule: Demonstrating reliability
Enterprise adoption is shifting from “capability” to “credibility.” Organizations without strong oversight, documentation, and risk management risk losing trust and market momentum. Are you ready?