AI Agent Interoperability and the Governance Implications of Connected Autonomous Systems
AI token costs are rising fast. Learn what's driving hidden spend in enterprise AI and how to take control before the bill arrives.
Download The eBook Now: AI Agent Interoperability and the Governance Implications of Connected Autonomous Systems
The single-agent era is already behind us. Now comes the hard part.
Enterprises that deployed AI early are now running systems where agents hand off work to other agents, share context across tasks, and chain decisions across functions and organizational boundaries — often with no human in the loop. The governance frameworks built for single-agent deployments were never designed for this.
This technical-executive guide for CIOs, CISOs, and platform leaders breaks down exactly what’s changing, where the risk lives, and how to govern connected autonomous systems before an incident forces the conversation.
Key Takeaways:
- Your old governance playbook has a gap: Single-agent frameworks don’t translate to multi-agent environments — and most enterprises haven’t closed that gap yet.
- Permission inheritance is the most common failure mode: Downstream agents can silently inherit upstream authorization, creating privilege escalation no human consciously approved.
- Accountability diffuses across chains: When something goes wrong, the responsible action may be steps removed from the original authorization — with no one clearly accountable.
- Five concrete controls you can implement now: Agent identity and credentialing, inter-agent authorization, context boundary enforcement, audit trail architecture, and guidance on MCP and A2A standards.
Download the eBook to learn more.