hands on workshop: context engineering for multi agent systems [D]

This r/MachineLearning discussion thread centers on a hands-on workshop covering context engineering — the process of designing and optimizing the information an AI agent sends to and receives from a large language model — specifically applied to multi-agent systems. The workshop covers topics such as defining and building AI agents, the core agent loop, function calling, orchestrating context between agents, and the critical importance of minimizing context. Context engineering is framed as the discipline of providing the right information and tools in the right format, encompassing techniques like context offloading, reduction, retrieval, and isolation to improve reliability and performance in agentic pipelines.

Related

• Frameworks For Supporting LLM/Agentic Benchmarking [P]
• This is the right frame. We’re currently designing our agent memory platform, and the hardest part isn’t storage — it’s deciding what to rem…
• @hwchase17 middleware was the right abstraction for it too. way more adoptable than asking everyone to restructure their agent setup
• This post completely misses the point of @sarahwooders 's original article. The whole point is that memory == context engineering, so it can…

Source: r/MachineLearning | 2026-04-13