Outcome Coordination for AI Agents

Abstract

Outcome Coordination for AI Agents presents an architectural model for governing AI-agent execution within individual systems while coordinating outcomes across organizational and technical trust boundaries. The paper shifts the agent-facing abstraction from low-level tool calls to explicit objectives, offers, commitments, evidence and settlement. Agents interpret intent, evaluate trade-offs and apply policy, while participating systems retain control over fulfillment and remain accountable for the outcomes they commit to deliver. It examines authority boundaries, execution governance, provider accountability, evidence-backed completion, failure handling and distributed trust. Lattice Capability Runtime and Covenant Layer Protocol are used as reference implementations for structured execution within systems and commitment-based coordination between them.