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Insights on AI infrastructure, Decision Coherence, and building systems for the machine-driven era.
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74 articles
The Thundering Herd Problem in Real-Time Decision Systems
The thundering herd problem looks like a load problem, and single-flight or jittered TTLs treat it as one. In a real-time decision system it's a correctness problem — and the real fix is removing the cache, not tuning it.
Xiaowei Jiang|2026-05-22
ACID for Agents: Why Database Consistency Is the Bottleneck for Production AI
AI agents and automated decision systems need ACID consistency on the data they read at decision time — not just the data they write. The industry agrees the agent data layer is broken; it disagrees on why. The converged-database camp says storage fragmentation. We argue it’s computation fragmentation: the derived state decisions depend on — velocity counters, risk scores, features — is maintained in pipelines outside the transactional boundary, so it’s always slightly stale. A Context Lake converges the computation, not just the storage, ingesting via CDC from existing systems without requiring migration.
Boyd Stowe|May 11, 2026![Top AI Agent Memory Tools in 2026: Vector Databases, Memory Libraries, and Context Lakes Compared](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1 1"%3E%3C/svg%3E)
Top AI Agent Memory Tools in 2026: Vector Databases, Memory Libraries, and Context Lakes Compared
AI agent memory is not one product category. It is four — vector databases, agent memory libraries, OLTP-plus-cache stacks, and Context Lakes — each solving a different slice of the problem. Here is what each category covers and where each one falls short.
Alex Kimball|May 11, 2026LLM Orchestration: How Frameworks Coordinate Control Flow Across Multiple LLM Instances
LLM orchestration frameworks — LangGraph, CrewAI, OpenAI Agents SDK, LangChain — coordinate which agent runs next and how handoffs happen. They do not coordinate the shared state every agent reads and writes. Production multi-agent failures are usually state-coherence failures, not workflow failures, and the orchestrator can’t catch them.
Alex Kimball|May 1, 2026Context-Aware AI: Why Institutional Knowledge Alone Isn’t Enough
Context-aware AI needs two halves to work in production — institutional knowledge of how the business operates, and real-time context of current state. Most enterprise AI tools only solve the first.
Alex Kimball|May 1, 2026Postgres Materialized Views: Create, Refresh, and Optimize
How to create, refresh, and optimize a Postgres materialized view — plus the structural limit every team hits when reads need fresh derived state under concurrency.
Alex Kimball|Apr 29, 2026Ready to get started?
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