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Endless aisle retail: infrastructure & real-time data
A customer walks into your store looking for a specific running shoe in size 11. You carry the brand, but not that model. In a traditional setup, that's a lost sale. Endless aisle changes the math: instead of losing the customer to a competitor's app,...
AI shopping assistants: how they work & what to build
You type "cozy winter sweater" into a search bar and get zero results because no product is tagged with that exact phrase. Keyword search can't tell that a "wool pullover" is the same idea. AI shopping assistants can, using LLMs, vector embeddings, an...
Context poisoning: how bad information breaks agent reasoning
Your agent confidently tells a customer their order shipped two days ago. It didn't. The order was canceled last week, but a stale cache entry surfaced in the agent's context window, and the agent treated that outdated status as fact. Worse, it then w...
Context retrieval for AI agents: what it is & why it matters
Your AI agent can reason, plan, and call tools, so why does it keep giving wrong answers? Most of the time, the problem isn't the model itself, but what the model is working with. Retrieval bottlenecks are the real culprit: when an agent pulls the wro...
What is a context layer? AI agent infrastructure
In a demo, your agent only has to hold one conversation with one user, against fresh data, for a few minutes. Production is different. It has to remember users across sessions, reconcile retrieved documents that disagree, filter out irrelevant search ...
What is a context engine? The platform layer behind production AI agents
Count the systems behind your AI agent. A vector database for embeddings. A separate cache for LLM responses. A memory service for conversation state. A pipeline syncing data from Postgres. Probably a queue. Maybe a feature store. Now count the things...
Long-horizon tasks: building agents that work over hours & days
Early AI agents handled one-shot jobs that took a few minutes: fix this bug, write this function, generate this test. More recent workflows are multi-step, tool-using, and stateful over extended sessions — an agent might spend a full afternoon refacto...
Agentic retrieval techniques: a complete guide
Your AI assistant just answered a complex, multi-part question by pulling data from three different sources, checking its own work, and re-querying when the first results fell short. That's agentic retrieval in action. This guide covers what agentic ...
Prompt bloat: causes, costs & fixes for LLM apps
You shipped an agent that handles customer support tickets. In staging it routed cleanly, called the right tools, and stayed on script. A few weeks into production, things drift. The agent forgets constraints you set in the system prompt halfway throu...
Context compaction for AI agents: a complete guide
Your agent just spent 40 turns debugging a gnarly authentication issue. It found the root cause, mapped out a fix, and started implementing. Then, somewhere around turn 45, it forgot everything it learned and started investigating from scratch. This ...
Context orchestration: what it is & how it works
Your LLM application works fine in a demo. You ship it to production, and it starts hallucinating on stale data, looping through the same tool calls, and burning through tokens in retry cycles. The model itself is probably fine. The system feeding it ...
How Conflict-free Replicated Data Types power active-active database replication
Your application runs in three regions. A customer in Tokyo buys the last unit of a product at the exact moment a customer in Frankfurt buys the same SKU. Both writes succeed locally. Both replicas decrement inventory. Which region's view is correct, ...
Context graphs: when nearest-neighbor search isn't enough
Your retrieval-augmented generation (RAG) pipeline works well on simple questions. You embedded your documents, built a vector index, and retrieval does its job. Then someone asks something that requires distributed facts, and the whole thing falls ap...
Why your AI doesn't understand your business (& how teams fix it)
Your AI can summarize documents and answer questions about almost anything on the internet. But ask it about your business, and things fall apart. It pulls stale pricing, ignores internal policies, or hallucinates details that sound plausible but don'...
Semantic layer vs context layer: where BI modeling ends & AI grounding begins
Your BI semantic layer solved a hard problem: getting every team, dashboard, and report to agree on what shared metrics like "revenue," "active customer," or "customer acquisition cost" actually mean. Those governed definitions won't be enough to grou...
AI reasoning explained: smarter models still need context
Every few months, a new AI model drops with higher benchmark scores, and the reaction is predictable: "This one finally reasons." The leaderboard shuffles. And teams building production AI systems still watch their agents hallucinate or mishandle ques...
Redis vs Memorystore: key differences in 2026
If you're building on Google Cloud and need an in-memory data store, you've probably looked at Memorystore in the console. It's right there, a few clicks to provision, and it speaks the Redis protocol you already know. But the architectural difference...
AI context windows: Why context quality beats context size
Your AI agent has a 128K token context window. You're adding in retrieved documents, conversation history, tool outputs, and system instructions. But the answers are getting worse. You're not alone. Most agent failures in production today are context...
Context engineering for AI agents: the infrastructure behind every decision
Your agent is only as good as the information it can see at decision time. The data sitting in your infrastructure doesn't count, and neither does what the model learned in training months ago. What counts is the specific tokens loaded into its contex...
Context windows in AI: why every token is a budget decision
Some of today's most capable LLMs now support very large context windows. That doesn't mean you should fill them. Context windows have grown fast, but the underlying cost and quality tradeoffs haven't gone away. They've just gotten easier to ignore. ...