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
AGENTS
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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?
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Coding with o3 model to avoid AI slowdown
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If you feel like AI is slowing down, try coding with o3 – best model only a year ago
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Governance gap in multi-owner agent economy discussed by Patrick Hussey
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Sunday edition of Superintelligence – our second "Intelligence from the Community" piece is live. This week: Patrick Hussey on the governance gap in the multi-owner agent economy.
Agents are already destroying production systems, ignoring stop commands, and sustaining collusive -
Hermes Agent Self-Evolving Skills and Memory Architecture
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self-evolving skills in Hermes agent. i found this to be the most powerful feature in Hermes. the agent doesn't just solve problems, it remembers how it solved them. memory handles facts, and skills handle procedures. the difference matters because knowing that a Kubernetes
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The Shift Toward AI-Powered Digital Workers as a Service
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The Next Big AI Shift Is Selling Digital Workers as a Service AI is moving beyond software tools — businesses may soon subscribe to intelligent digital workers that can perform complex tasks autonomously. Read more https://
bernardmarr.com/the-next-big-a
i-shift-is-selling-digital-workers-as-a-service/
… #AI #FutureOfWork -
StarVLA: Modular Vision-Language Robot Codebase
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What if building a robot that sees, understands, and acts was as easy as snapping Lego together? Enter StarVLA: a modular codebase that lets you swap vision-language or world-model backbones and action heads independently. It matches or surpasses prior methods on benchmarks
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Causal vs Observational Agency in Agents
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Causal vs observational agency. Agent actions (a) should be treated as interventions, not as evidence for hypotheses (p). Actions by other agents or tool outputs are evidence (o).
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Checklist: How to Build AI Agents
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How to build AI Agents • Define scope
• Structure I/O
• Add tools
• Enable reasoning
• Orchestrate agents
• Add memory Smart ≠ enough
Structure + context = performance Via Giuliano Liguori (
@ingliguori
) #AI #AIAgents #BuildInPublic