Agentic Architecture: Why Files Aren't Always Enough

What are the limitations of using a file-based agent workflow? Why do massive context windows tend to collapse? This week on the show, Mikiko Bazeley from MongoDB joins us to discuss agentic architecture and context engineering. Mikiko is an applied AI engineer. She helps developers and organizations build AI and ML applications using MongoDB. We dig into the debate of files versus a database. What are some of the limitations of building an agent with just a folder of files? We explore the surprising limitations of massive context windows and strategies for fixing them. Mikiko also shares advice and resources to help you get up to speed on building your own agent skills. Our conversation touches on multiple topics in the current development landscape. This episode is sponsored by SerpApi. Video Course Spotlight: Building Type-Safe LLM Agents With Pydantic AI Build type-safe LLM agents in Python with Pydantic AI using structured outputs, function calling, and dependency injection. Topics: 00:00:00 – Introduction 00:02:31 – Catching up with MongoDB 00:07:02 – Are the files all you need? 00:15:14 – What is a workflow agent? 00:24:43 – Sponsor: SerpApi 00:25:45 – Model vs harness 00:29:57 – Context rot and tool loadouts 00:41:07 – Sharing state and coordination of agents 00:47:27 – Video Course Spotlight 00:49:16 – What do dataflows look like 01:00:38 – The human-in-the-loop & coding agents 01:10:30 – Resources to explore 01:17:49 – What are you excited about in the world of Python? 01:18:38 – What do you want to learn next? 01:22:54 – Thanks and goodbye Show Links: The “files are all you need” debate misses what’s actually happening in agent memory architecture - The New Stack MongoDB: The World’s Leading Modern Data Platform Karpathy shares ‘LLM Knowledge Base’ architecture that bypasses RAG with an evolving markdown library maintained by AI - VentureBeat Files Are All You Need: Context, Search, Skills Guide | LlamaIndex Converged Datastore For Agentic AI - MongoDB Why Developers Need Vector Search - The New Stack Why Multi-Agent Systems Need Memory Engineering – O’Reilly The New Skill in AI is Not Prompting, It’s Context Engineering - Phil Schmid How Long Contexts Fail - dbreunig.com How to Fix Your Context - dbreunig.com AI Agents Need Memory Control Over More Context - arxiv.org AINews - Is Harness Engineering real? - Latent.Space The Model vs. the Harness: Which Actually Matters More? Embeddings and Vector Databases With ChromaDB – Real Python 12-factor-agents: What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers? The Twelve-Factor App MongoDB Courses and Trainings - MongoDB University mongodb-mcp-server: A Model Context Protocol server to connect to MongoDB databases and MongoDB Atlas Clusters. What is the MongoDB MCP Server? - MongoDB Docs mongo-python-driver: PyMongo - the Official MongoDB Python driver agent-skills: Use the official MongoDB Skills with your favorite coding agent to build faster. Reachy Mini - Open-Source Desktop Humanoid Robot 👩🏻‍💻 Mikiko B. - LinkedIn Building AI Products From Scratch - Mikiko Bazeley - Substack Level up your Python skills with our expert-led courses: Using Pydantic to Simplify Python Data Validation Getting Started With Claude Code Building Type-Safe LLM Agents With Pydantic AI Support the podcast & join our community of Pythonistas