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A new 'no-magic' guide offers a manual blueprint for building agentic AI frameworks, focusing on transparency over abstraction, though its long-term scalability remains unproven.

CodeRabbit's new 'loop engineering' framework offers a strategy for designing autonomous AI systems that recover from errors without needing constant human intervention.

A new study reveals that Transformer-based AI models struggle with selective attention, failing tests that humans pass easily. Experts are now questioning the limits of current LLM architecture.

New technical documentation explores using MongoDB and LangGraph to give AI agents long-term memory, addressing the limitations of ephemeral session-based operations.