Tines proposes identity-based definition to distinguish true AI agents from assistants

Tines proposes a new identity-based definition for AI agents, anchored in legal concepts of agency and expressed through audit logs. The company’s unique framework helps distinguish true AI agents from assistants by focusing on whether the AI operates under its own identity and can take independent actions, rather than simply extending human capabilities.

The big picture: Tines has developed a “litmus test” for defining AI agents that focuses on whether the system performs actions under its own identity in audit logs.

• This identity-based approach draws inspiration from legal principles of agency, where both principals and agents have distinct identities and responsibilities.
• The definition provides practical clarity in a space where “we’ll know it when we see it” has been the prevailing but imprecise standard.

Key details: According to Tines, a true AI agent operates independently under its own identity, rather than as an extension of a human user.

• The agent’s name appears in audit logs when it takes actions, creating a clear record of responsibility.
• Systems like copilots or in-product assistants fail this test because they operate under the human user’s identity rather than their own.

Why this matters: This definition creates meaningful distinctions between AI assistants and true agents in enterprise environments.

• Organizations need clear accountability frameworks as AI systems gain more autonomy within business operations.
• The identity-based approach helps determine appropriate levels of trust, responsibility, and supervision for different AI systems.

Reading between the lines: Tines acknowledges that many valuable AI systems, including their own Workbench product, don’t qualify as agents under this definition.

• This stance suggests the company values clarity of roles over marketing hype that might label every AI tool as an “agent.”
• By positioning agents as something beyond assistants, Tines signals its interest in developing true agency capabilities while maintaining a clear distinction between different types of AI systems.