Let’s dive into Agentic AI, guided by the "Cognitive Architectures for Language Agents" (CoALA) paper. What defines an agentic system? How does it plan, leverage memory, and execute tasks? We explore semantic, episodic, and procedural memory, discuss decision-making loops, and examine how agents integrate with external APIs (think LangGraph). Learn how AI tackles complex automation — from code generation to playing Minecraft — and why designing robust action spaces is key to scaling systems. We also touch on challenges like memory updates and the ethics of agentic AI. Get actionable insight…
🔗 Links to the CoALA paper, LangGraph, and more in the description.
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Mentioned Materials:
• Cognitive Architectures for Language Agents (CoALA) - https://arxiv.org/abs/2309.02427
• Memory for agents - https://blog.langchain.dev/memory-for-agents/
• LangChain - https://python.langchain.com/docs/introduction/
• LangGraph - https://langchain-ai.github.io/langgraph/
Our solutions:
- https://askpythia.ai/ - LLM Hallucination Detection Tool
- https://www.wisecube.ai - Wisecube AI platform can analyze millions of biomedical publications, clinical trials, protein and chemical databases.
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