SELECTED BLOG POSTS

Anthropic and OpenAI had better hope more companies don’t follow the example of AT&T.
The telecommunications firm plans to keep its employees’ spending on Anthropic and OpenAI models flat in the coming years by using more open-source models such as Nvidia’s Nemotron, according to Mark Austin, an AT&T vice president. 

Anthropic’s US customers are using cheaper alternatives to its most powerful AI tool, raising questions about the group’s high-spending business model ahead of what is expected to be the biggest IPO of all time.

I’ve been thinking lately about what happens when AI agents stop being something that a few people experiment with and become a normal way of doing work inside a company.

The user experience is already surprisingly good. I can ask Claude or ChatGPT to inspect something, write some code, search through documents or interact with other applications. With a little configuration, an agent can use Slack, GitHub, Jira or whatever else I happen to need.

The problems start when I want to treat this as infrastructure.

Agentic AI is becoming the leading use case in enterprise AI adoption, and their reliability now decides whether that adoption succeeds. But these agents don’t always fail in obvious ways: an agent can generate valid SQL, complete a query successfully and return figures that appear reasonable while still misinterpreting the original request. 

Because enterprise AI agents cannot rely on evaluation alone, we propose a comprehensive operating model that guardrails reliability at every layer.

Enterprise AI initiatives often fail because business data lacks shared meaning across systems and departments. This article explains how enterprise ontologies, OWL reasoning, SHACL validation, and governed knowledge graphs create a trusted semantic foundation for AI, analytics, and cross-domain decision-making across insurance and financial services.

For much of the past two years, the general belief in enterprise AI has been that more autonomy equals better performance. Build agents that can plan, decide, and act across multi-step workflows, and give them as much room to run as possible. That assumption is now being tested at scale, in real production environments — and in a lot of deployments it’s failing. The companies that end up benefiting from agentic AI won’t necessarily be the ones that have given their agents the most flexibility. They’re the ones who create AI agents with specific responsibilities and make sure they operate within clear rules.