UC San Diego researchers say multi-agent AI has a memory problem. The fix? Computer architecture: caching, consistency, hierarchy. Old problems, new systems.
#AI #AgentSystems
resultsense.com/i/2026-03-19-multi-agent...
A bigger context window does not guarantee better retrieval. Needle-in-a-haystack shows many LLMs miss facts buried in the middle of the context, and agent systems can favour tools listed first over better later options. Better structure beats bigger prompts.
#LLMs #AIEngineering #AgentSystems
“AI sees itself” is not an architecture.
Mirror recognition, if you want to talk about it seriously, should be modeled as a bounded, governed event under reflection — not as consciousness fanfiction.
New paper:
zenodo.org/records/1901...
Demo Monday.
#AI #AISafety #AgentSystems
Agents are ditching RAG for pure vector search—because memory frameworks already store embeddings. Find out why similarity search is becoming the go‑to retrieval infra for LLM agents. #VectorSearch #AIMemory #AgentSystems
🔗 aidailypost.com/news/agents-...
Детальна розбірна архітектура Emergent AI 2026: як агенти приймають рішення та взаємодіють у системі. Технічний гайд для розробників і AI-ентузіастів.
🔗 webscraft.org/blog/yak-pra...
#EmergentAI #AgentSystems #AI
Scaling agent systems to solve complex tasks can lead to inefficiencies. We introduce asymptotic analysis with #LLM primitives (#AALPs) to model and optimize computational cost as systems grow.
Read our paper: ow.ly/ALHq50W2YaM
Dive into our blog: ow.ly/YuF950W2YaL
#AgentSystems
From tool calling to multi-agent orchestration—LLMs now interact via the Model Context Protocol (MCP) for scalable, modular workflows. Agents coordinate tasks through JSON-RPC, powering the next generation of AI systems.
#LLM #MCP #AgentSystems #AI #OpenAI #Anthropic
