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Knowledge mapFrom AI to agents that actIndustry concepts and standards

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← AI and agents

Learning path

From AI to agents that act

Explore agent systems through identity, data and execution.

Start with model capability, then examine tools, identity, working material and execution. Connections lead to identity, personal data, applications and blockchain; they do not require every agent to operate on-chain.

Along this path

  1. AI, models and applications

    Separate a field, a model capability and a running application.

  2. How does an AI agent move from answers to actions?

    Understand goals, tools, feedback and stopping conditions.

  3. Are agent identity and permission the same?

    Separate persistent naming, proof of control and authority for an action.

  4. Who owns an agent’s memory?

    Distinguish personal records, task context and transient execution state.

  5. Why does an agent need an execution environment?

    A model proposes operations; the environment executes them and holds state.