Context

In practice, we found that AI agents cannot be treated as interchangeable tools once they are placed inside real organizational workflows.

Without explicit structure:

  • actions are executed without durable records,
  • responsibility becomes ambiguous,
  • handover between agents and humans breaks down,
  • failure modes lack clear ownership.

Under these conditions, AI systems may appear productive, yet remain unreliable.

The Structural Problem

The issue is not model capability.

The issue is the absence of a governance interface between AI agents and human organizations.

When agents act without defined boundaries:

  • no one can determine accountability,
  • humans cannot resume work coherently,
  • escalation becomes improvisational rather than procedural.

This makes sustained collaboration impossible.

The AI Employee Agreement

To address this, we designed an AI Task Collaboration Framework that functions analogously to an employee agreement.

The framework defines:

  1. Action traceability
    Every agent action is logged through operational tools (e.g., Notion, Linear), enabling audit and reconstruction.

  2. Task boundary layers
    Tasks are explicitly classified into those eligible for autonomous execution and those requiring human review.

  3. Escalation paths
    Tasks exceeding agent capability are routed to predefined human roles, rather than failing silently.

  4. Versioned authority
    When a model or agent version changes, its task permissions are re-evaluated rather than implicitly inherited.

Four Governing Questions

From an implementation perspective, the framework exists to answer four questions:

  • What is the agent allowed to do?
  • What is it explicitly not allowed to do?
  • Who is accountable if it fails?
  • How can humans reclaim control without friction?

Without clear answers to these questions, AI remains operationally fragile.

Implication

Only through this form of agreement can AI agents function as governable members of the workforce, rather than as useful but unreliable execution plugins.

This framing treats AI collaboration as an organizational design problem, not a tooling problem.