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joy7758/README.md

Bin Zhang

Independent researcher exploring governance infrastructure for AI agents.

My work focuses on how autonomous AI systems can become:

  • Identifiable (persona layer)
  • Observable (execution trace)
  • Auditable (evidence records)
  • Governable (runtime control)

AI Agent Governance Stack

Minimal research architecture for auditable AI agents.

flowchart TD

A[Persona Layer<br>POP] --> B[Agent Runtime]

B --> C[Execution Trace]

C --> D[Audit Evidence<br>ARO]

D --> E[Governance Controls<br>Token Governor]
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Core Projects

Persona Object Protocol (POP)

Portable persona layer for AI agents.

https://github.com/joy7758/persona-object-protocol


ARO Audit

Evidence generation and audit records for agent execution.

https://github.com/joy7758/aro-audit


Token Governor

Runtime token cost and policy control for AI agents.

https://github.com/joy7758/token-governor


Verifiable Agent Demo

Minimal end-to-end governance pipeline demo.

https://github.com/joy7758/verifiable-agent-demo


Research Direction

Current focus:

  • AI Agent Observability
  • Agent Governance Architecture
  • Verifiable Agent Execution
  • Digital Object Infrastructure (FDO)

Contact

GitHub discussions and issues welcome.

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  1. persona-object-protocol persona-object-protocol Public

    A lightweight public draft protocol for portable persona objects in multimodal AI systems.

    Python 1

  2. aro-audit aro-audit Public

    A lightweight audit and provenance layer for AI execution integrity.

    Python 1

  3. token-governor token-governor Public

    天将|LLM 智能体运行时资源控制引擎:Token 预算控制、成本优化、稳定性 Guard、多工具 Agent 调度

    Python 1

  4. verifiable-agent-demo verifiable-agent-demo Public

    Python