Connecting AI agents to enterprise knowledge

Business and technology leaders need no convincing that the time of agentic AI is here. Organizations are rapidly adopting agents, and few executives doubt the technology’s

Scaling agentic AI across an enterprise requires patterns that preserve flexibility while avoiding vendor lock-in. In this second post of our multi-agent series, we examine how ML

Agents are only as intelligent as the context they can reason over. Today, that context is scattered across data lakes, data warehouses, lakehouses, databases, and streams, and in

<!-- SC_OFF --><div class="md"><p>Heptabase just dropped a CLI so Claude Code / Codex can create, read, and update a local knowledge base from the terminal. It’s a smart move.</p>

arXiv:2606.15575v1 Announce Type: new Abstract: Organizational knowledge is fragmented across a variety of software systems, tacit expertise, and manual documents that have

<p>Most people still think AI is about better answers. That phase is already behind us. What is emerging now is something fundamentally different: AI that reasons. Systems that do

<!-- SC_OFF --><div class="md"><p>Gemini 3.5 Flash launched Tuesday, and Google barely framed it as a conversational product. Instead, the company focused on coding pipelines,

<h1> Google Open Knowledge Format: Why Enterprise Agents Need a Knowledge Layer, Not Just More Tools </h1> <p>Most enterprise AI conversations still start in the wrong place.</p>