Part one ended with a claim: enterprise AI will succeed when institutions learn how to build the loop itself. This essay is about what the loop stands on. An agent working inside a real company needs two things the company almost certainly does not have today: a map of the work, and rails for the consequences. The Map Here is the uncomfortable fact underneath most stalled AI programs: the company cannot hand the agent a description of its own work, because no such description exists. Most… [...]
Enterprise AI has largely been built around context engineering. Teams connect enterprise systems, generate chunks and embeddings, build retrieval pipelines, and assemble the context needed by individ [...]
Perplexity, the AI-powered search company valued at $20 billion, announced on Wednesday at its inaugural Ask 2026 developer conference that its multi-model AI agent, Computer, is now available to ente [...]
Enterprise AI has a new infrastructure problem: companies are accumulating agents faster than they are developing systems to govern them.Gartner estimates that the average global Fortune 500 company w [...]
Jensen Huang walked onto the GTC stage Monday wearing his trademark leather jacket and carrying, as it turned out, the blueprints for a new kind of monopoly.The Nvidia CEO unveiled the Agent Toolkit, [...]
Were you a Lego set kid or a giant-bucket-of-Legos kid? I was a sets kid all the way — I loved, and still love, the zen feeling of building something incredible a little bit at a time. Also, every t [...]
Were you a Lego set kid or a giant-bucket-of-Legos kid? I was a sets kid all the way — I loved, and still love, the zen feeling of building something incredible a little bit at a time. Also, every t [...]
Were you a Lego-set kid or a giant-bucket-of-Legos kid? I was a sets kid all the way — I loved, and still love, the zen feeling of building something incredible a little bit at a time. Also, every t [...]