39 posts tagged with "context-engineering"

Most AI agent bugs are not model bugs. The model is doing exactly what it's told—it's what you're putting into the context that's broken. After a certain point in an agent's execution, the problem isn't capability. It's entropy: the slow accumulation of noise, redundancy, and misaligned attention that degrades every output the model produces. Researchers call this context rot, and every major model—GPT-4.1, Claude Opus 4, Gemini 2.5—exhibits it, at every input length increment, without exception.

Context engineering is the discipline of managing this problem deliberately. It's broader than prompt engineering, which is mostly about the static system prompt. Context engineering covers everything the model sees at inference time: what you include, what you exclude, what you compress, where you position things, and how you preserve cache state across a long-running task.

You connect your agent to 50 MCP tools. It can query databases, call APIs, read files, send emails, browse the web. On paper, it has everything it needs. In practice, half your production incidents trace back to tool use—wrong parameters, blown context budgets, cascading retry loops that cost ten times what you expected.

Here's the part most tutorials skip: every tool definition you load is a token tax paid upfront, before the agent processes a single user message. With 50+ tools connected, definitions alone can consume 70,000–130,000 tokens per request. That's not a corner case—it's the default state of any agent connected to multiple MCP servers.

Most agent demos are stateless. A user asks a question, the agent answers, the session ends — and the next conversation starts from scratch. That's fine for a calculator. It's not fine for an assistant that's supposed to know you.

The gap between a useful agent and a frustrating one often comes down to one thing: whether the system remembers what matters. This post breaks down how to architect durable, personalized memory into production AI agents — covering the four-phase lifecycle, layered precedence rules, and the specific failure modes that will bite you if you skip the engineering.