STOP Prompting—Start Engineering Your AI Agents (n8n)

???? Upgrade your n8n AI Agents with our Advanced workflows https://www.theaiautomators.com/?utm_source=youtube&utm_medium=video&utm_campaign=tutorial&utm_content=context_engineering

RAG Masterclass - https://www.youtube.com/watch?v=75lwkzFxyLs
Newsletter Agent Team - https://www.youtube.com/watch?v=V21J0_RMbJo
Hybrid Search Video - https://www.youtube.com/watch?v=2-6ckhW3Hmo
Multimodal RAG - https://www.youtube.com/watch?v=5aCi42dVOTA

References:
https://www.philschmid.de/context-engineering
https://blog.langchain.com/context-engineering-for-agents/
https://docs.anthropic.com/en/docs/build-with-claude/context-windows?ref=blog.langchain.com#understanding-the-context-window
https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html?ref=blog.langchain.com
https://n8n.io/workflows/2878-host-your-own-ai-deep-research-agent-with-n8n-apify-and-openai-o3/

Chapters:
00:00 Context Engineering Overview
00:54 Short-Term Memory
02:09 Long-Term Memory
04:15 Tool Calling Context
05:44 RAG (Retrieval-Augmented Generation)
08:09 Context Isolation
10:15 Summarizing Context
13:30 Context-Aware Routing & Context Staging
14:51 Formatting Context
16:13 Trimming Context
16:50 Importance of Context Engineering

In this video, we explore the critical concept of context engineering and why it's becoming essential in the age of advanced AI agents. Prompt engineering alone is no longer enough—agents must be able to access, manage, and prioritize context intelligently and efficiently.

We kick things off by explaining how context windows function in large language models (LLMs), acting as the model’s working memory. From there, we dive into nine powerful context engineering strategies you can implement directly within n8n to supercharge your AI workflows.

Learn how to apply short-term memory using simple memory settings or an external Postgres database. Explore how to build long-term memory with tools like Google Docs, Sheets, or even Airtable. You’ll also see how to use tool calling to fetch real-time data (e.g., from Perplexity), and how retrieval-augmented generation (RAG) allows agents to handle massive knowledge bases through vector stores.

We then explore context isolation using sub-agents, helping you avoid overload by delegating responsibilities and keeping context clean. Techniques like summarizing context, routing and staging, and deep research blueprints show how to manage large-scale data and workflows with precision.

You’ll also see how format conversion (like HTML to Markdown) and context trimming can reduce token usage and cost—making your agents faster and more efficient.

Lastly, we cover common pitfalls like context poisoning and distraction, and how to avoid them with smart engineering practices.

If you're building agents in n8n, this video will level up your understanding and give you practical tools to improve accuracy, performance, and reliability.