The Need for Relational Governance in Multi-Agent Systems

The Governance Gap in Multi-Agent Systems

Trust in fully autonomous AI agents for enterprise applications dropped from 43% in 2024 to 22% in 2025 despite technological improvements. The infrastructure is advancing rapidly with Google's Agent2Agent, Anthropic's Model Context Protocol becoming an industry standard, Visa processing agent-initiated transactions, and Singapore publishing the world's first dedicated governance framework for agentic AI in January 2026.

Current Governance Landscape

Existing frameworks address important risks but have limitations:

• Singapore's Model AI Governance Framework for Agentic AI focuses on four dimensions: bounding agent autonomy and action-space, increasing human accountability, and ensuring traceability
• Know Your Agent ecosystem includes Visa, Trulioo, Sumsub, and startups solving agent identity verification
• ISO 42001 provides a management system framework for documenting oversight
• OWASP Top 10 for LLM Applications identifies "Excessive Agency" as a critical vulnerability
• Three-tiered guardrail model includes foundational standards, contextual controls, and ethical guardrails

These frameworks govern agents as individuals with proper identity, permissions, and audit trails, but don't address relationships between agents working together.

Research Findings on Agent Interactions

Recent studies reveal critical gaps in current approaches:

• Salesforce's AI Research team built an "A2A semantic layer" for agent-to-agent negotiation and found that when two agents negotiate on behalf of competing interests, the dynamics are fundamentally different from human-agent conversations
• Models were trained as helpful conversational assistants, not to advocate, resist pressure, or make strategic tradeoffs in adversarial contexts
• Agent-to-agent interactions aren't scaled-up versions of human-agent conversations—they're entirely new dynamics requiring purpose-built solutions
• A large-scale AI negotiation competition with over 180,000 automated negotiations found warmth consistently outperformed dominance across all key performance metrics
• Warm agents asked more questions, expressed more gratitude, and reached more deals
• Dominant agents claimed more value in individual transactions but produced significantly more impasses
• Relationship-building through warmth in initial encounters compounds over time when agents can reference past interactions
• Relational memory and relational style matter for outcomes, not just permissions and audit trails

The current governance approach treats agents like individuals entering a building with verified credentials and access cards, but multi-agent systems function more like teams that need communication norms, mechanisms for resolving misunderstandings, and facilitation when coordination breaks down.