From Tools to Agents: How orchestrated decision intelligence is reshaping S&OP

Charles Hoskinson defended Cardano’s AI content tests, saying agents will help Midnight City manage marketing, trading and user growth.

The Coast Guard’s digital transformation strategy hinges on automating repetitive tasks, harnessing data and “empowering” the workforce with new tools.

OpenAI has introduced spend controls and enhanced usage analytics for ChatGPT Enterprise to enable organizations to monitor AI adoption, track consumption across teams, and set budgets for AI usage. But, analysts cautioned, it still can’t show how those costs lead to business benefits. The new features provide administrators with centralized dashboards showing how ChatGPT is being used across an organization, enabling them to understand adoption patterns, and manage AI costs by setting budgets and tracking spending. “The Global Admin Console brings ChatGPT and Codex credit usage into one view, so admins can see a more granular breakdown of credit consumption across users, products, and models — helping them understand where spend is coming from and how it maps to actual credit usage,” OpenAI said. A shift toward AI cost governance The introduction of budgeting and usage analytics reflects a broader change in enterprise priorities, according to Biswajeet Mahapatra, principal analyst at

Claude Code's Artifacts enhance collaborative development, fostering dynamic, secure internal app sharing, potentially boosting enterprise productivity. The post Claude Code launches Artifacts for sharing interactive apps and dashboards appeared first on Crypto Briefing.

Artificial intelligence is moving from the lab bench to the production floor, and the companies enabling that transition are building the infrastructure that defines how agents are created, trained, deployed, and governed. The companies doing it most consequentially are not wrapping chat interfaces around existing models; they are constructing the runtimes, the training environments, the […]

Insider Brief PRESS RELEASE — SWARM Engineering, the decision intelligence company for agrifood and manufacturing, has announced it has raised $10 million in an oversubscribed Series A funding round led by S2G Investments and AgRogue Growth Partners, with participation from Radicle Growth, Grit Road Partners, Middleland Capital, Open Prairie, Serra Ventures, and Trailhead Capital. The funding is […]

First came vector databases, then RAG. Now, the next frontier in enterprise AI is taking shape: context layers that give autonomous agents a shared understanding of the business, a vision Databricks is advancing with Genie Ontology. Currently in preview, Genie Ontology automatically extracts business context from enterprise data, dashboards, queries, pipelines, documents, and applications and organizes it into a living graph that AI agents can use to understand how an organization operates. Showcased at the company’s Data + AI Summit, Genie Ontology uses a ranking system inspired by Google’s PageRank to identify the most authoritative business definitions within an organization. Rather than treating all sources equally, it weighs factors including who created the information, how widely it is used, its links to certified datasets and assets, and how recently it was updated before determining which answer an AI agent should rely on, Databricks CEO Ali Ghodsi said during his keynote late

Coinbase’s AI Advisor and new “Agents” trading ship with guardrails and derivatives access. Can DeFi wallets keep retail onboarding as super‑apps consolidate flows?