[logseq-plugin-git:commit] 2026-02-10T07:25:47.227Z

pages/KaraKeep-Highlights.md

@@ -1,5 +1,5 @@
 icon:: ✏️
-generation_time:: [[2026-02-04]] T18:00:32Z
+generation_time:: [[2026-02-10]] T07:00:32Z
 
 - # **TODOs and IDEAs**
 	- {{query (and [[TODO]] [[KaraKeep-Highlights]] (or [[Ideas]] [[KaraKeep-Highlights]]))}}
@@ -64,6 +64,28 @@ generation_time:: [[2026-02-04]] T18:00:32Z
 	  background-color:: green
 		- > **Note:** Il Coraggio di vivere
 
+- ## **LangChain Unveils Four Multi-Agent Architecture Patterns for AI Development**
+  source:: https://blockchain.news/news/langchain-multi-agent-architecture-patterns-guide
+  url:: https://karakeep.diruscio.org/dashboard/preview/fiaojtcvh7y42zbs9f5qt7ul
+  tags:: [[Artificial Intelligence]] [[Developer-Tools]] [[Machine Learning]] [[Multi-Agent Systems]] [[Software-Architecture]]
+	- detailed framework for building multi-agent AI systems, arriving as the AI infrastructure space heats up with competing approaches from Google and Microsoft in recent weeks
+	  background-color:: green
+	- Context management becomes the first bottleneck. Specialized knowledge for multiple capabilities simply won't fit in a single prompt. The second constraint is organizational—different teams need to develop and maintain capabilities independently, and monolithic agent prompts become unmanageable across team boundaries
+	  background-color:: green
+	- Anthropic's research validates the approach. Their multi-agent system using Claude Opus 4 as lead agent with Claude Sonnet 4 subagents outperformed single-agent Claude Opus 4 by 90.2% on internal research evaluations. The key advantage: parallel reasoning across separate context windows.
+	  background-color:: green
+		- > **Note:** This is important to support the seminar at Milano Bicocca
+	- The Four Patterns
+	  background-color:: blue
+		- > **Note:** #IMPORTANT #MOSAICO
+	- A supervisor agent calls specialized subagents as tools, maintaining conversation context while subagents remain stateless
+	  background-color:: blue
+	- Routers classify input and dispatch to specialized agents in parallel, synthesizing results. Enterprise knowledge bases querying multiple sources simultaneously benefit here. Stateless by design, which means consistent per-request performance but repeated routing overhead for conversations
+	  background-color:: green
+	- LangChain's Deep Agents
+	  background-color:: blue
+		- > **Note:** TODO Da vedere per #MOSAICO
+
 - ## **LangChain and LangGraph Agent Frameworks Reach v1.0 Milestones**
   source:: https://blog.langchain.com/langchain-langgraph-1dot0/
   url:: https://karakeep.diruscio.org/dashboard/preview/gfuwsnyaj3k8sglws9kncv27