Stop Overfeeding Your AI Agent's Context Window

Picture a checkout page throwing the dreaded 500 error at 2 a.m. Someone opens an AI agent and asks it to fix things. The instinct is to be generous. Paste in the runbooks. Drop in three dashboards. Attach a pile of customer complaints. Let the model sort it out.

More context should mean a smarter answer. Right?

Not really. Researchers who studied how language models actually use long inputs found something inconvenient for anyone who pastes first and thinks later.

"Performance can degrade significantly when changing the position of relevant information" Source: Liu et al., "Lost in the Middle: How Language Models Use Long Contexts," arXiv:2307.03172

In plain English, an AI agent buried under ten documents isn't automatically wiser than one working with the two documents that actually matter. The fix for agentic AI isn't a bigger context window. It's giving the agent the right kind of knowledge, delivered the right way, at the right moment.

There are four main ways to do that: skills, MCP, RAG, and memory. Each one solves a different problem. Mixing them up is where a lot of enterprise AI projects quietly go wrong.

Skills: The Onboarding Manual Nobody Wrote

A skill is a set of instructions for doing one specific job, sometimes with a bit of code attached. Think of it as the manual you'd hand a sharp new hire on day one. Check the error rate first. Then check recent deployments. If neither explains the problem, stop guessing and escalate to a human.

Without that manual, a capable model will improvise, and improvisation is exactly what you don't want during an outage. A good skill doesn't just list steps. It carries judgment about when to follow them and when to stop.

The trick is that skills only load when they're relevant, which is what keeps them cheap. Anthropic, which built this pattern into Claude, puts it plainly.

Only relevant content occupies the context window at any given time (Source: Anthropic, Agent Skills documentation, )

A library of fifty skills doesn't cost fifty skills worth of context. It costs one: the one the agent actually needed for this task. But a skill can't reach outside itself. It can tell an agent to check the error rate. It can't get the agent to the dashboard.

MCP: Giving the Agent Hands

That's where the Model Context Protocol, or MCP, comes in. MCP is a standard way for an agent to connect to outside systems: logging tools, databases, ticketing systems, whatever a company already runs. The agent is the "host." Each connected system sits behind an "MCP server" that knows how to talk to it.

Before MCP, wiring an AI assistant into five internal tools meant writing five custom integrations, then doing it again for the next assistant. Anthropic built MCP to close exactly that gap.

An open standard that enables developers to build secure, two-way connections (Source: Anthropic, "Introducing the Model Context Protocol," )

Back to the checkout error. With MCP wired up, the agent doesn't just know it should check the error rate. It can go pull the number from the logging stack and the metrics dashboard itself. That solves the access problem. It doesn't solve judgment. MCP hands the agent raw numbers. It has no opinion about whether last month's number was normal for this particular system.

RAG: The Library Card

That's where retrieval-augmented generation, or RAG, earns its keep. Instead of stuffing every manual and dependency map into the prompt up front, RAG lets the agent search a collection of documents and pull back only the passages that match the question, using semantic search rather than a keyword match.

The original RAG paper, published by Facebook AI researchers back in 2020, was blunt about the problem it set out to solve. Language models are good at sounding confident. They're less reliable at being precise.

Their ability to access and precisely manipulate knowledge is still limited (Source: Lewis et al., "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks")

For the checkout bug, RAG might point the agent to the exact paragraph in the payment gateway's integration guide that explains why timeouts spike under load, buried in a document nobody on the team has fully read in a year. RAG is knowledge a person deliberately wrote down and filed away. It's only as good as what gets filed.

Memory: What the Agent Remembers From Last Time

Memory looks a lot like RAG from a distance. Both retrieve relevant information on demand. The difference is where the knowledge comes from. RAG pulls from documents a person wrote. Memory pulls from what the agent picked up itself while working.

Say this exact 500 error happened three months ago, and the real cause turned out to be a stale feature flag nobody had documented anywhere. Memory is what lets the agent recall that the hard way this time, then write the fix back down for next time. A recent academic survey framed the shift well.

Memory is increasingly the substrate through which agents self-evolve (Source: "A Survey of Agent Memory in the Second Half")

That's the piece a pure RAG setup or a static skill file can't give you. An agent that gets measurably better at your specific systems the longer it works on them, instead of starting fresh every time.

Four Tools, One Simple Rule

None of these four replace each other. None of them do much alone beyond a demo. A rough rule of thumb, borrowed from how production teams tend to use them:

• Knowledge someone wrote down on purpose: RAG
• Knowledge the agent picked up from experience: memory
• A repeatable procedure with judgment attached: a skill
• A way to reach the outside world without custom glue code: MCP

Most serious agentic systems end up using all four together. The skill tells the agent what steps to follow and when to escalate. MCP gets it into the logging and metrics tools. RAG surfaces the relevant page from the documentation nobody memorized. Memory remembers that this exact error showed up before, and what actually fixed it.

Skip any one layer and the agent falls back to guessing. That's the same problem as throwing everything into the context window in the first place, just with better manners.

Choosing Which One to Build First

The four rarely get built at once, and deciding where to start is one of the harder calls in enterprise AI architecture. Get the skill and the connective tissue right before reaching for a memory layer, and the sequencing tends to hold up. Build memory first, on top of a shaky procedure, and the agent will remember the wrong lessons very efficiently.

This is the kind of tradeoff Faisal Feroz works through regularly as a Chief Technical Architect and Fractional CTO, helping enterprise teams turn legacy platforms into AI-first, event-driven systems. Readers weighing the same decisions on their own stack can find more of his writing on enterprise AI architecture at fferoz.medium.com, or connect with him on LinkedIn at linkedin.com/in/faisalferoz to talk through where skills, MCP, RAG, or memory actually fit.