2nd Edition, by @keithbourne v/ @PacktDataML "Unlocking Data with Generative AI and RAG — Learn AI Agent Fundamentals with RAG-powered Memory, Graph-based RAG, and Intelligent Recall" Get the book here: http://
amzn.to/49zsIkb
AGENTS
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New Book: Unlocking Data with Generative AI and RAG, 2nd Edition
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New book on designing multi-agent AI systems with MCP and A2A
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New release from @PacktDataML available at: http://
amzn.to/40Sp4O9 "Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Python-based Agentic AI Framework with tool use, memory, and multi-agent workflows" Table of Contents:
Introduction to Generative AI and AI -
AI Agents, MCPs, and Container Safety Mechanisms
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We use both together. In practice, for containers to be useful, you often have to punch some holes: GitHub, Anthropic API, kube, other MCPs. Auto mode makes interacting with these safer. It significantly reduces the risks of accidental data deletion, exfiltration, and prompt
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Business Value of Agentic AI: Three Key Pillars
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What's The #Business Value Of #AgenticAI? Three Key Pillars For Scaling #AIAgents
by Preetpal Singh @Forbes Learn more: https://
buff.ly/Ax4IOkO #LLM #GenerativeAI #ArtificialIntelligence #MachineLearning -
Boring context compaction beats multi-agent systems for most use cases
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I've built multi-agent systems for clients. I still think they're overrated for most use cases. What's underrated is boring. Context compaction. The reason your AI app feels slow and expensive isn't the model. It's everything you keep stuffing into the prompt before it even
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Agent Task Timing and Pricing Tier Strategy
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Most agent tasks don't need to be fast. They need to be done by morning. A slow tier fits that reality well.
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How AI Agents Manage Goals and Objectives
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How #AIAgents Manage Goals and Objectives
by @e_opore #AI #LLM #ArtificialIntelligence #MachineLearning #ML -
Agent Architectures: Thin Orchestrators Over Direct Work
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This matches what's happening in agent systems beyond research too. You would find similar patterns in Claude Code as well. I think the best agent architectures keep converging on thin orchestrators that delegate instead of doing the work themselves.
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Qwen 3.5 27B NVFP4 runs 5 agents full context under 20GB VRAM
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Qwen 3.5 27B in NVFP4 w/ full context taking less than 20GB VRAM You can basically run like 5 agents w/ full context on a single RTX PRO 6000 like this, and they'd be so fast Tell me I didn't tell you this was gonna happen
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GIPO: Gaussian Importance Sampling for AI Learning Without Forgetting
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What if your AI agent could learn faster from older data without forgetting or crashing? Researchers from Sany Group present GIPO (Gaussian Importance Sampling Policy Optimization). Instead of hard clipping, it uses a soft Gaussian trust weight to gently limit extreme updates