model context
Beyond Market Intelligence keeps model context in one place: 3 stories so far. The section currently leads with “How Context Engines Give Agentic AI a Reliable Memory”, “Designing the System That Lets AI Models Deliver Real Features”, and “Explore How Bounded AI Agents Navigate Knowledge Graphs for Smarter Insights”. A reliable agentic AI needs a memory that knows what to forget. An AI coding agent can write a function in seconds, but that's the easy part. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every model context story on Beyond Market Intelligence, newest first.
How Context Engines Give Agentic AI a Reliable Memory
A reliable agentic AI needs a memory that knows what to forget. Adi Polak argues that the real architecture depends on a context engine that selects, injects, compresses, and expires information before it ever reaches the model. This means separating context from memory and knowledge, then using tiered storage, curated inputs, and token budgets. She also flags common pitfalls like context stuffing and stale data loops. For more on building intelligent systems at scale, see our related piece on recommendation systems.
Designing the System That Lets AI Models Deliver Real Features
An AI coding agent can write a function in seconds, but that's the easy part. Building a real feature requires understanding the project, finding the right files, using tools safely, and verifying the changes work. A model alone sees only text, it needs a system around it. This is the practical work of harness engineering. For a deeper look at what happens when that system falters, see our related piece on how agentic AI demands smarter governance to tame silent failures.
Explore How Bounded AI Agents Navigate Knowledge Graphs for Smarter Insights
Most of us hand our AI agents a search box and call it a day. The approach argues for something sharper: typed tools, hard bounds, and a gate the model cannot talk past. The payoff is letting the agent wander a knowledge graph on its own, within strict limits. Four models and one wrong prediction later, the question is whether that discipline is worth it. We think it is, and the reasoning here is worth your attention.