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    DAIR.AI highlights a framework for deciding how much authority to give AI agents

    DAIR.AI says the review distinguishes a model’s competence from its supporting software and the authority it actually gets in deployment.

    DA
    1 Source, 23d ago, first seen 23d ago

    TLDR

    According to DAIR.AI’s summary, the papers examined document expanded interfaces for taking action far more convincingly than robust completion, recovery, authorization or independent verification. The review keeps the model, its supporting software and its environment distinct when attributing results. DAIR.AI also says MCP and Agent2Agent improve systems’ ability to work together without establishing that delegation is trustworthy, while multi-agent setups bring specialization alongside cost and correlated failures.

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    1 Source, first seen 23d ago

    Combined views

    7.7K

    1 Source, first seen 23d ago

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    14 comments
    41 saves
    6 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    1 Source

    @dair_ai// From Language Models to World-Acting Systems // A critical review of agentic AI, and a framework that is genuinely useful for deciding how much authority to hand an agent. Here is how it works. The review separates three things the field routinely treats as one. Model competence, harness integration, and the authority a deployment actually grants are pulled apart and assessed separately. Evidence gets organized along delegated authority, temporal persistence and environmental coupling, and the model, the harness and the environment stay distinct when a result is attributed. The finding across the papers examined is that expansion of action interfaces is documented far more convincingly than robust completion, recovery, authorization or independent verification. MCP and Agent2Agent improve interoperability without establishing that delegation is trustworthy. Multi-agent organization buys specialization along with cost and correlated failure. Paper: https://academy.dair.ai/papers/from-language-models-to-world-acting-systems-progress-and-limits-of-agentic-ai-a-2609.04894

    1 Source

    @dair_ai// From Language Models to World-Acting Systems // A critical review of agentic AI, and a framework that is genuinely useful for deciding how much authority to hand an agent. Here is how it works. The review separates three things the field routinely treats as one. Model competence, harness integration, and the authority a deployment actually grants are pulled apart and assessed separately. Evidence gets organized along delegated authority, temporal persistence and environmental coupling, and the model, the harness and the environment stay distinct when a result is attributed. The finding across the papers examined is that expansion of action interfaces is documented far more convincingly than robust completion, recovery, authorization or independent verification. MCP and Agent2Agent improve interoperability without establishing that delegation is trustworthy. Multi-agent organization buys specialization along with cost and correlated failure. Paper: https://academy.dair.ai/papers/from-language-models-to-world-acting-systems-progress-and-limits-of-agentic-ai-a-2609.04894