Discussion - Topic: Context Engineering

This week's topic: Context Engineering

Discussion resources to help guide the conversation will be posted below a few days before the meetup.

Zoom link will be added about 5 min before the event starts.

As described in Thoughtworks Technology Radar Vol. #34.

Context engineering has evolved from an optimization tactic into a foundational architectural concern for modern AI systems. Unlike prompt engineering, which focuses on wording, context engineering treats the context window as a design surface and intentionally constructs the AI’s information environment.

As agents tackle more complex tasks, dumping raw data into large context windows leads to “context rot” and degraded reasoning. To combat this, teams are shifting from static, monolithic prompts to progressive context disclosure. Instead of front-loading every instruction and reference an agent might need, these systems start with a lightweight index of what’s available. The agent determines what prompts or contexts are relevant and pulls in only what’s needed, keeping the signal-to-noise ratio sharp at every step.

We’re seeing several techniques mature in this space: Context setup leverages prompt caching to front-load static instructions, reducing costs and improving time to first token. Dynamic retrieval goes beyond basic RAG by selecting tools and loading only the necessary MCP servers, avoiding unnecessary context expansion. Context graphs model institutional reasoning — such as policies, exceptions and precedents — as structured, queryable data. Context management techniques use stateful compression and sub-agents to summarize intermediate outputs in long-running workflows.

Treating AI context as a static text box is a fast track to hallucinations. To build resilient enterprise agents, teams must engineer context as a dynamic, precisely managed pipeline.

Discussion Resources :

The rise of "context engineering" By Harrison Chase
https://www.langchain.com/blog/the-rise-of-context-engineering

Context Engineering By The LangChain Team
https://www.langchain.com/blog/context-engineering-for-agents

Context Engineering with OpenAI: Making Enterprise AI Agents Production-Ready By Shikhar Kwatra (OpenAI), Soumo Chakraborty, Sakshi Ray, Amey Gujre, Suvam Ray
https://fractal.ai/blog/context-engineering-openai/

Context Engineering for Personalization - State Management with Long-Term Memory Notes By Emre Okcular
https://developers.openai.com/cookbook/examples/agents_sdk/context_personalization

Inside the LangChain x Manus Webinar on Context Engineering By Duarte Caldas Cardoso
https://medium.com/@caldasdcardoso/inside-the-langchain-x-manus-webinar-on-context-engineering-69166ee404db

Context Engineering in 29 Minutes: Complete Cours By Marina Wyss - AI & Machine Learning
https://www.youtube.com/watch?v=-h9VVJIqtvA

Context Engineering Clearly Explained By Tina Huang
https://www.youtube.com/watch?v=jLuwLJBQkIs

Context Engineering Explained (5 Practical Tips) By Shaw Talebi
https://www.youtube.com/watch?v=zKHSpwayPBU

Context Engineering Explained: How to Build Reliable AI Agents By VectorLab
https://www.youtube.com/watch?v=oDUnErN6z3w

Context Engineering Our Way to Long-Horizon Agents: LangChain’s Harrison Chase By Sequoia Capital
https://www.youtube.com/watch?v=vtugjs2chdA