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
AGENTS
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Business Value of Agentic AI: Three Key Pillars
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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
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Glean Prompt Library: AI Agents for Enterprise Search
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Glean Prompt Library! @glean Glean’s platform seamlessly integrates with a wide array of enterprise systems such as email, intranet, cloud storage, and database by providing a powerful, unified search interface with AI Agents that effortlessly retrieves information from all
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DeepMind LLMs Lean Proof-Search Agents Solve Open Problems
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This new DeepMind research turns LLMs into Lean proof-search agents, so every step must compile and the final proof is mechanically verified. Under this setup, they solved 9 open Erdős problems, proved 44 OEIS conjectures, and helped advance actual research in optimization,
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Agent Behavior: Each Line Must Change Agent Actions
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The fix is making every line answer "what does the agent do differently because of this?" If a line doesn't change behavior, it's decoration.