# Building AI Agent Applications: Lessons on Model Size, Context, MCP, and RAG

DevFeed: [Building AI Agent Applications: Lessons on Model Size, Context, MCP, and RAG](<https://devfeed.tech/articles/de-mystifying-ai-agent-applications-19719.md>)

Original publisher: [Read original article](<https://deliveroo.engineering/2026/02/16/demystifying-ai-agents.html>)

Author: Mai Lubega

Published: 2026-02-16T00:00:00Z

Content type: tutorial

Language: en

Sources: [Deliveroo](<https://devfeed.tech/sources/deliveroo.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [Retrieval-Augmented Generation](<https://devfeed.tech/topics/retrieval-augmented-generation.md>), [Ollama](<https://devfeed.tech/topics/ollama.md>), [Go Language](<https://devfeed.tech/topics/go-language.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [go](<https://devfeed.tech/tags/go.md>), [llms](<https://devfeed.tech/tags/llms.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [ollama](<https://devfeed.tech/tags/ollama.md>), [rag](<https://devfeed.tech/tags/rag.md>), [software](<https://devfeed.tech/tags/software.md>)

## AI overview

This tutorial shares lessons from an Ardan Labs workshop on building AI agent applications. It covers using smaller local models with Ollama, managing conversation context, generating vector embeddings, building RAG applications, and creating an MCP client and server.

## Source excerpt

LLMs, MCPs, RAG. There are lots of acronyms in the AI space, but what do they all mean? Dear reader, despite being a software engineer who works in the machine learning space, I confess there was a time I wasn't really sure. Fortunately, with the financial support of Deliveroo's Women-in-Tech Employee Resource Group, I took the Ardan Labs Building AI-Powered Applications in Go workshop that helped me understand what's really going on behind the chat interface and where software engineering meets LLM-based applications. We went through a series of modules to incrementally build a RAG (Retrieval-Augmented Generation) AI Agent application. I started with generating vector embeddings from text and eventually built an MCP Client & Server able to process image and text input and respond to simple queries, after which I felt I understood the new AI landscape much, much better. For my dearest gentle reader, I'm happy to share these three takeaways from the course: Size Matters Full-size language models are very large and can require several hundred gigabytes of memory to run, certainly much more than is available on a personal laptop. To work around this constraint, we used Ollama -- an open-source project that provides smaller, more size-efficient LLM models - that can be run locally. In the real world, some applications get around this problem by sending data directly to OpenAI/Anthropic's APIs. They don't have to host any models locally, just send and receive data (subject to usage tier and API limits, of course). But applications that need a model to run locally for proprietary reasons will have to manage the infrastructure themselves -- which usually involves a very spicy cloud bill or a server in the corner that doubles as a space heater. Don't Lose the Plot (Manage your Context) Think of Context as the LLM's short-term memory. When we chat with an AI, we don't just send the current question; we send the entire conversation history, system instructions, and any retrieve