Enterprises are racing toward “agentic AI” — systems of autonomous agents that can act, decide, and coordinate work with minimal human intervention. According to the Celonis 2026 Process Optimization Report, 85% of enterprises want to become agentic within three years.… Read More »Why Agentic AI Fails Without a Process Intelligence Layer
AT&T’s AI platform was quietly burning through roughly 8 billion tokens every day. For most enterprises, that level of usage is a sign that adoption is working. For Andy Markus, AT&T’s chief data officer, it was also a warning: pushing… Read More »Inside AT&T’s Agentic AI Stack: How 8 Billion Tokens a Day Led to a 90% Cost Cut
Google’s decision to cut off OpenClaw-powered agents from its new Antigravity “vibe coding” platform has jolted the agent developer ecosystem and raised hard questions about how much control cloud providers will retain over agentic AI. What Google framed as an… Read More »Google’s Antigravity Crackdown on OpenClaw: What It Signals for the Future of Agentic AI
Enterprise teams are increasingly betting on AI agents to handle coding, debugging, and software maintenance. Yet many discover the hard way that their agents are brittle: introduce a new library, tweak a workflow, or switch models, and carefully engineered systems… Read More »Group-Evolving Agents: How Collective AI Frameworks Match Human-Engineered Systems at No Extra Inference Cost
OpenClaw’s rapid rise from a niche open-source assistant (previously known as Clawdbot, then Moltbot) to a 180,000-star GitHub sensation with 2 million site visitors in a week is more than a curiosity for developers. It is a live-fire test of… Read More »OpenClaw and the New Agentic AI Attack Surface: A Practical Guide for Security Leaders
As the industry declares 2026 the year of “agentic AI,” attention has centered on model leaderboards, GPU counts, and ever-larger context windows. But for organizations actually deploying autonomous agents in production — to book travel, manage cloud infrastructure, diagnose outages,… Read More »Why Agentic AI Needs a Data Constitution Before More GPUs
As agentic AI shifts from demos to always-on, production-grade systems, one constraint is emerging as a first-order design problem: memory. Not FLOPs, not model size—raw, addressable memory for Key-Value (KV) caches. For teams building long-running, stateful agents, this “memory wall”… Read More »Breaking Through AI’s GPU Memory Wall With Token Warehousing
Enterprise identity and access management (IAM) was built for employees, contractors and admins who log in, change roles and eventually leave. That human-centric model is now colliding with a reality in which machines — including AI agents — outnumber people… Read More »When Machine Identities Outnumber Humans: Rethinking IAM for Agentic AI
Enterprises exploring agentic AI now face an overwhelming number of frameworks, tools, and orchestration patterns. The choice is no longer simply “which large language model (LLM)?” but “what kind of agent architecture are we building, where do we invest training… Read More »How a New Agentic AI Framework Helps Enterprises Choose Between Training Models and Training Tools
Based on an article originally presented by EdgeVerve. From Assistants to Autonomous Agents: What’s Really Changing Enterprise AI is moving from a world of “helpers” to a world of “doers.” For the last several years, most deployments have focused on… Read More »Designing Agentic AI for the Enterprise: From Autonomous Workflows to Trustworthy Platforms