The 8-Layer Architecture of #AgenticAI
by @Python_Dv #AI #LLM #ArtificialIntelligence #MachineLearning #ML
AGENTS
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The 8-Layer Architecture of Agentic AI Explained
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Deep Agents: Optimizing Performance Across Model Families
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great deep dive into how we get deepagents to work well with different families of models
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Replit Agent Monitors Production Apps, Fixes Issues Automatically
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Building apps is easy- keeping them running isn’t
— Replit ⠕ (@Replit) 29 avril 2026
Introducing Replit Application Monitoring
Replit Agent now watches your app in production, investigates issues, and helps fix them- so you don’t have to pic.twitter.com/NP8blat8awBuilding apps is easy- keeping them running isn’t Introducing Replit Application Monitoring Replit Agent now watches your app in production, investigates issues, and helps fix them- so you don’t have to
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Replit Agent Diagnoses App Issues Automatically
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When users report issues with your app, simply paste their message into Replit and Agent will diagnose the problem and recommend solution.
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OpenAI DevDay Returns to San Francisco September 29
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OpenAI DevDay is back. San Francisco September 29
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LangChain 2nd Edition: Production LLM Applications and Advanced Agents
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The 2nd Edition of this book has arrived, with Agentic AI updates: "Generative AI with LangChain — Build Production-ready LLM Applications and Advanced Agents using Python and LangGraph" at https://
amzn.to/3JEeS6K v/ @PacktDataML 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷:
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Agentic AI Models Demonstrate Advanced Judgment Capabilities
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One reason I don’t think “judgment” is going to be a distinctly human role in working with AI is that the most recent agentic models have gotten quite good at some types of judgment. You can’t do the kind of high complexity, long-run tasks that current AIs can do without it.
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AGI ALPHA Completes First Real Loop with Evidence Compiler
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Today, AGI ALPHA ran the First Real (RSI) Loop: Nova‑Seed → MARK → mini Sovereign → 5 real jobs → Evidence Docket → reusable compiler → vNext treatment/control improvement. From architecture to replayable evidence. GitHub Actions: https://
github.com/MontrealAI/agi
alpha-first-real-loop/actions/runs/25118498108/job/73612279861
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Madrigal Pharma Multi-Agent Platform Powered by LangChain
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@MadrigalPharma
’s multi-agent research and intelligence platform for pharma is powered by LangChain + LangSmith. When users ask research questions, the orchestrator breaks them into sub-tasks. Multiple agents working in conjunction towards an overarching goal, with an -
Parallelism and Agentic Skills Accelerate Prototype to Production
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Going from prototype to production took weeks instead of months. Madrigal’s team is finding parallelism increases accuracy and scope while decreasing latency. With agentic skills, domain experts can work directly inside the feedback loops and address shortcomings. See