It's so important to look at the actual prompts sent to the model (and the received chunks) to see if it makes sense at all. A lot of RAG pipelines retrieve "relevant" chunks that are actually useless for the specific question. The routing agent is where most of the value is IMO.
AGENTS
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Human Approval Portal for Autonomous Agents
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Building a 'Human-in-the-Loop' Approval Gate for Autonomous Agents machinelearningmastery.com/b… [Translated from EN to English]
→ View original post on X — @craigbrownphd, 2026-04-05 12:44 UTC
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Agentic AI Periodic Table: Memory, Planning, Tools, Safety
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Agentic AI now has its own “Periodic Table” 🧪🤖 From: LLM, RAG, RL to PLAN, MAS, LTM to SAFE, HUMAN oversight to HR, MKT, LEGAL use cases Autonomous AI = memory + planning + tools + safety + collaboration. It’s a system, not a prompt. Credit: Prem Natarajan #AgenticAI #AIStack #LLM
→ View original post on X — @ingliguori, 2026-04-05 12:17 UTC
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First-Mover Advantage in Agent Strategy Market Opportunity
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The companies building agent strategies now will have first-mover advantages in the largest market opportunity ever.
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Harness Engineering: Building Better AI Agent Systems
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I let Claude Code loop for 45 minutes while I was at the gym. Came back. It told me the feature was done. It wasn't. It hadn't even run the tests. Not because the model is dumb. Because I wrapped it in nothing but a loop and a dream. That's harness engineering in one sentence. And no, it's not prompt engineering with a fancier name. The model is the engine. Context is the fuel. The harness is the rest of the car. Steering. Brakes. Lane boundaries. Warning lights. Tools, permissions, tests, retries, guardrails. Engine + fuel but not strong parts in it = dangerous car. So I stopped tuning the engine and started building the car around it. In every skill file (Claude Code, Claude Co-work), I added one last step. After each interaction, the agent reflects on what I liked, what I edited, what failed. Then it updates its own skill to be better next time. Token usage dropped (a lot). Output quality went up. Compounding improvement with zero extra effort from me. LangChain did something similar at a bigger scale. Changed only the harness on a coding agent. Same model. Went from outside the top 30 to top 5 on a benchmark. Same engine, completely different results, just because the car around it was better. Next time your agent breaks, don't blame the model. Fix the car. P.S. Do your agents learn from their mistakes, or do they keep making the same ones?
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Virtual employees target 50 trillion in knowledge worker value
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Virtual employees targeting $50 trillion in knowledge worker value. Partner content with Siemens.
— Lucian Fogoros (@fogoros) 5 avril 2026
This dwarfs every other technology disruption in economic scale.
The next two years will separate the prepared from the disrupted. . #sie_HM. pic.twitter.com/LlltgZYGzWVirtual employees targeting $50 trillion in knowledge worker value. Partner content with Siemens. This dwarfs every other technology disruption in economic scale. The next two years will separate the prepared from the disrupted. . #sie_HM.
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AI Communities Interconnected Across X Platform Networks
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I mapped out ask these communities on X: https://
x.com/scobleizer/lis
ts
… The AI ones are intertwined in all the others. -
MacBook Air M5 Sufficient for Coding Agents Work
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I spent 3 hours this morning working with coding agents on MacBook 16" M5 Max in LOW POWER mode! 😱 I noticed 0 difference! This means a MacBook Air M5 is more than enough for this. BTW Apple Silicon is unbeatable! 🤷🏻♂️
→ View original post on X — @clementdelangue, 2026-04-05 08:53 UTC
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Gemma-4-E4B: Local AI Agents Identify Sea Animals with Vision
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Watch Gemma-4-E4B casually identify sea animals by classifying images in a single agentic session using its vision capabilities. (impressive for a 4B model 🚀)
— Victor M (@victormustar) 5 avril 2026
I'm convinced: the agents of tomorrow are local, free, fast, and run on every computer! pic.twitter.com/mHPMiPPNSZWatch Gemma-4-E4B casually identify sea animals by classifying images in a single agentic session using its vision capabilities. (impressive for a 4B model 🚀) I'm convinced: the agents of tomorrow are local, free, fast, and run on every computer!
→ View original post on X — @deeplearn007, 2026-04-05 08:41 UTC
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AI Tool Discovers Best Content from AI Community
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I built AI to find the good stuff in the AI community: https://
alignednews.ai