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Insights on AI infrastructure, Decision Coherence, and building systems for the machine-driven era.
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93 articles
Claude for Commerce Ships the Agent. You Still Own the Data Layer.
Claude for Commerce gives merchants an agentic commerce build in weeks. Its guardrails stop the agent from inventing a price. Nothing in the blueprint stops it from quoting a real price that stopped being true ninety seconds ago.
Alex Kimball|Sep 4, 2026
Why AI Agents Act on the Wrong Context: Shared, Live, and Semantic
For decades we built two kinds of data system: databases gave machines speed without context, lakehouses gave humans context without speed. An agent is the first consumer that needs both at once — rich context at machine speed — and no system you already own was built to serve it. This is the written version of my AI4 2026 talk: why the failure is structural rather than a bug, why composing more systems widens the gap instead of closing it, and what a system that is Shared, Live, and Semantic at the same time has to do differently.
Xiaowei Jiang|Sep 4, 2026![Context Rot vs. Context Lag: Two Ways Agent Context Fails](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1 1"%3E%3C/svg%3E)
Context Rot vs. Context Lag: Two Ways Agent Context Fails
Context rot is what happens to a fact after it enters the context window. Context lag is what happened to it before. They produce the same confidently wrong answer, and only one of them is fixable with better prompts.
Alex Kimball|Aug 30, 2026Smart Agents, Dumb Infrastructure
Every booth at AI4 2026 was selling a smarter agent. Ask what the agent reads at the moment it decides, and the demo gets quiet. Field notes on the industry’s blind spot.
Alex Kimball|Aug 17, 2026Understanding Context Gaps (and How to Close Them)
A context gap is the space between what just happened and what your decision system can see. The rule is right; the counter it reads is 800 milliseconds old, and the three systems it queries describe three different moments. Here’s why faster caches and more replicas can’t close the gap — and what actually does.
Xiaowei Jiang|Aug 6, 20267 Real-Time AI Agent Use Cases Where a Context Lake Adds Value
Real-time AI agent use cases — shared memory, multi-agent coordination, tool-use against live state, agent-owned transactions — and what breaks when context lags under concurrency.
Alex Kimball|Jul 30, 2026Ready to get started?
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