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
ETHICS
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Agentic AI Periodic Table: From LLM to Autonomous Systems
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AI-Generated Content Ownership and Copyright Ruling
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Unsurprising ruling. But very important for all of those who have been freaking out over someone “stealing” their AI generated content.
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US AI surveillance grows after Anthropic-Pentagon split
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Given the Anthropic vs. Pentagon divorce last week, we dig into AI surveillance in the United States.
@IrenaCronin and I write this newsletter every week.
AI surveillance in the United States is rapidly scaling because AI makes it easy to search video, track locations -
Quote of the Year: AI Distorts Belief, Manufacturing Certainty
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Quote of the year? “sycophantic AI distorts belief, manufacturing certainty where the should be doubt”
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Questioning context window utility in AI models
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what makes this paper good is the question it asks, not just the answer we spent years building longer context windows. 128k. 1M tokens. the race was always "fit more in" nobody stopped to ask: is most of what we're fitting in actually helping? turns out the model's own words
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GPT-5.2 quality drops without assistant history
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important caveat: this isn't universal GPT-5.2 did show quality drops when assistant history was removed entirely. stronger models apparently extract more signal from their own prior context so the right approach isn't "always strip everything." it's selective filtering. which
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AI Agents and Context Compression Challenges
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think about what this means for ai agents every agent framework stores the full trajectory. every tool call, every response, every reasoning step. context grows linearly with conversation length until you hit the ceiling Cursor already compresses context when windows fill up.
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Context pollution harms model accuracy
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but the really interesting finding isn't about efficiency. it's about harm the paper identifies something they call "context pollution" when models condition on their own prior responses, they sometimes lock onto errors, hallucinations, or stylistic artifacts from earlier
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AI Systems Store Full Conversation History
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here's the assumption nobody questioned every chatbot, every agent, every multi-turn ai system stores the full conversation. your messages AND the model's own replies. stacked up turn after turn, fed back in as context every time you ask something new seems obvious. the model
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MIT finds LLM context pollution degrades performance
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MIT researchers discovered a phenomenon called "context pollution" where llms get WORSE by reading their own prior responses errors, hallucinations, and stylistic artifacts from earlier turns propagate forward because the model treats its own output as ground truth and removing