HeyJaredEnterprise Context Management
Enterprise Context Management is an emerging technology that helps enterprises extract and apply context, turning everyone’s AI into your AI.
- Indexed issues, last 90 days
- 4
- Latest publication
- Sep 29, 2026
- Audience
- Checking…
- Earliest in this view
- Jul 6, 2026
Latest issues
Four Lanes: Where Enterprise AI Actually Stands in September 2026 (opens the original)
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Enterprise AI in September 2026 is best read in four lanes: the context layer above the model, sovereignty and open weights, token economics, and long-horizon models. Each lane moved a long way over the summer. In each one the market produced a product that looks like the answer, and in each one the answer turns out to stop short of where enterprise work actually happens. This is our view of the four lanes, drawn from what vendors announced and from what we see in live enterprise deployments. Th
Your AI Agent Is Slowly Turning Into a DAG (opens the original)
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Agent-first development is a fast way to discover a workflow. As the workflow becomes predictable, stable execution should move into code, while the model remains available for the cases where fixed rules and conventional software are no longer enough. But how do you tell when that’s happening?<a class="image-link image2 is-viewable-img" href="https://substackcdn.com/image/fetch/$s_!8mZS!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages
Agentic development did not fix delivery. It exposed it (opens the original)
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Enterprise delivery has always moved slower than development. A feature can be built quickly, but getting it into production still depends on access, security reviews, environments, permissions, data ownership, integration, UAT, operational readiness, and customer adoption.For years, we could manage that gap through spreadsheets because build effort still represented a meaningful share of the delivery timeline. Agentic development changed that balance dramatically and exposed how wide the gap co
The Determinism Problem (opens the original)
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Current generation agent systems usually reach production in a state that is difficult to describe honestly. They work often enough to justify the investment, fail rarely enough to resist simple diagnosis, and vary just enough between runs to make every incident expensive.More often than not, the failure is not particularly dramatic. Two sessions receive the same request, use the same model, and appear to call the same tools. One completes. The other takes a slightly different path, nothing dram
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