A framework for moving enterprise AI from pilots to production. Each part explores one layer of the stack that makes AI applications dependable, governable, and adopted at scale.
Another new term in the world of AI, ‘System of Intelligence’.Prompt engineering, AI Agents, context engineering, context or knowledge graphs, claws, harness engineering and now ‘System of Intelligence’ For about more than two decades, the way enterprise software competed was by owning the data underneath that layer. The companies that won the era are Salesforce, ServiceNow, […]
This is the first deep-dive in the system of intelligence series. The overview post laid out five pillars: memory, context engineering, tools, harness, and the learning loop. This post is about pillar one, the substrate where everything the agent knows about its world is stored. Memory is the foundation. The other four pillars do nothing […]
This is the second deep-dive in the system of intelligence series. The first covered the memory substrate , the context graph where everything the agent knows about its world is stored. This post is about how the agent gets the right slice of that substrate into its working memory for any given goal, every single […]
This is the fourth deep-dive in the system of intelligence series. The first three covered memory, retrieval, and tools and actions. This post is about the layer that wraps all of them. A model writes confident answers and calls tools with plausible arguments. A model never says “I don’t know.” None of that becomes a […]
This is the third deep-dive in the system of intelligence series. The first two covered the memory substrate and compositional retrieval. Memory holds what the agent knows. Retrieval gets the right slice into working memory. This post is about the third pillar, how the agent actually does anything with what it knows. This post covers: […]
This is the fifth and final deep-dive in the system of intelligence series. The first four covered memory, retrieval, tools and actions, and harness engineering. This post is about the discipline that keeps all four of them from going stale. A model is static. The substrate it consults is not. Customers ask new questions. Policies […]