What if your AI agent could decide how much brainpower to use for each task, saving you money and time? Researchers from Huawei, National University of Singapore, and USTC present QuantClaw, a plug-and-play plugin that dynamically assigns low precision for simple jobs and high
AGENTS
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Context Engineering for Multi-Agent Systems Architecture
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"Context Engineering for Multi-Agent Systems: Move beyond prompting to build a Context Engine, a transparent architecture of context and reasoning" — at https://
amzn.to/448dSiA v/ @PacktDataML 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷:
Develop memory models to retain short-term and -
Representing AI agents as state for improved persistence
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maybe you can’t just tack statefulness onto an agent, you have to figure out how to represent the agent as a state
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Anthropic releases engineering guide for building effective AI agents
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4/ Building Effective Agents – by @Anthropic Core principles for designing agents that are dependable and efficient. → https://
anthropic.com/engineering/bu
ilding-effective-agents
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OpenAI Releases Comprehensive Guide to Building AI Agents
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3/ A Practical Guide to Building Agents – by @OpenAI A comprehensive, step-by-step guide from OpenAI on creating and deploying AI agents. → https://
cdn.openai.com/business-guide
s-and-resources/a-practical-guide-to-building-agents.pdf
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Technical challenges in stateful AI agents and memory management
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yeah they do cool stuff around memory. starting to think stateful goes beyond that to tracking changes in the agents capability, etc which I’m not sure if many memory tools do
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Architectural challenges in building AI agent loops
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agree. kinda feels like we’re tacking on memory, tools, traces, logs into agent loops but there should be an elegant way to combine them, also relying on crons or heartbeat loops also feels hack
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Complexity of state tracking in AI agents
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one thing that makes agents more complex is that they generally add capability over time which should also be tracked as part of the state
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Skill0: RL recipe for skill internalization by Meituan team
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That's so cool! The same team at @Meituan_LongCat wrote Skill0, where they propose an RL recipe for skill internalization.
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Seeking elegant primitives for implementing stateful AI agents
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stateful agents, decision traces, context graphs… talked about a lot, but has anyone seen an elegant primitive around how to actually implement?