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AI Agent Architecture August 2026 Version 1.2

Outcome Coordination for AI Agents: Governing Execution Across Trust Boundaries

Outcome coordination is an architectural model in which AI agents express objectives and participating systems coordinate through explicit offers, commitments, evidence and settlement. Execution remains governed within each system, while cross-system obligations are represented as verifiable commitments rather than opaque chains of tool calls.

Abstract

AI agents are often placed inside the execution loop of enterprise and consumer software. The model selects tools, reconstructs parameters, carries intermediate state through context, responds to failures, and eventually causes an authenticated side effect. Each operation can be valid while the combined effect is outside the task the user intended.

This paper presents Outcome Coordination as an architectural response: organizing agent systems around explicit outcomes, bounded authority, decision points, evidence and responsibility for fulfillment. It separates work performed inside one organizational trust boundary from coordination that crosses between organizations, and shows how Lattice and Covenant Layer represent the two scopes.

What's inside this paper
1

What is outcome coordination?

A model in which agents express objectives while participating systems coordinate through explicit offers, commitments, evidence and settlement.

2

Why direct tool orchestration becomes difficult across trust boundaries

Individually permitted operations can still combine into an effect the user never intended once work spans multiple systems and organizations.

3

Execution governance within a system

How a capability runtime keeps sequencing, state, credentials, failure policy and audit inside reviewed code, returning a projection the model can reason over.

4

Commitments between systems

How objectives, offers and authenticated acceptance turn cross-boundary work into verifiable commitments rather than opaque tool chains.

5

Objectives, offers, commitments, evidence and settlement

The public coordination objects and lifecycle that make authority, terms, acceptance, evidence and settlement explicit and inspectable.

6

Relationship to Lattice and Covenant Layer

Lattice governs execution inside one trust boundary; Covenant Layer coordinates commitments across boundaries. The two layers can be deployed independently or together.

7

Architecture diagrams

Reference diagrams for direct-operation agents, the Outcome Coordination layers, Lattice execution flow, the Covenant lifecycle and the combined architecture.

8

Security and failure considerations

Intent integrity, delegated authority, capability and provider execution, evidence and settlement, privacy, and dispute governance as assurance boundaries.

9

Limitations and open questions

Open research on deriving the coordinate, revision and reauthorization, outcome equivalence, projection completeness, authority provenance and broker incentives.

How to cite this paper

Abbasi, H. (2026). Outcome Coordination for AI Agents: Governing Execution Within Systems and Commitments Across Trust Boundaries. White Paper, Version 1.2, August 2026.

References
  1. Abbasi, H. "The Future of Agents Is Outcome Coordination." Level Up Coding, 11 March 2026.
  2. Abbasi, H. "The Future of Agents Is Outcome Coordination, Part II." 13 March 2026.
  3. Abbasi, H. "The Missing Runtime Between AI Agents and Enterprise Backends, Part 2 of 2." 20 May 2026.
  4. Abbasi, H. Lattice: The capability runtime for outcome-based execution. GitHub repository.
  5. Abbasi, H. Covenant Layer: Open protocol and framework for outcome-based coordination. GitHub repository.
  6. Liu, N. F. et al. "Lost in the Middle: How Language Models Use Long Contexts." 2023.
  7. Fourney, A. et al. "Tool-space interference in the MCP era." Microsoft Research, 11 September 2025.
Version history

Version 1.2August 2026. First public release of the combined Lattice and Covenant Layer architecture.