Agentic AI is rising. The evolution toward pervasive AI agents paves the way for an “internet of agents” where AI entities seamlessly interact and coordinate tasks across diverse ecosystems. Just as standardized protocols fueled the internet’s exponential growth, a shared framework is crucial to actualizing a globally interconnected agentic workforce.

Today’s AI agents, however, remain trapped in walled gardens. Agents built on a common platform share similar architectures and common orchestration, data and memory structures, tool calling, and execution mechanisms. Outside platform boundaries, interoperability standards and frameworks aimed at enabling agents developed on different platforms or by different vendors to work together simply do not exist yet. Without these, AI agents risk becoming isolated silos of automation, hindering the emergence of a truly connected AI ecology.

Nine Dimensions Of Agentic Interoperability

Interoperability among AI agents is a complex, multilayered challenge. For seamless cross-platform functionality, agents need standardized frameworks addressing not just technical compatibility but also security, governance, adaptability, and intent. Here are a few key dimensions:

  1. Tool use and integration. Ensuring interoperability at this level allows agents to seamlessly access databases, automation platforms, and enterprise applications without vendor lock-in or brittle, hard-coded logic. Anthropic’s Model Context Protocol and IBM’s Agent Communication Protocol are examples of such a standard.
  2. Interagent communication and coordination. AI agents, to collaborate effectively, need a structured way to exchange messages, delegate tasks, and resolve dependencies. Establishing a shared communication standard would allow agents from different frameworks to work together without friction, enabling cross-platform orchestration for everything from business process automation to multiagent scientific discovery. There are several initial efforts toward this end: AGNTCY, an industry-standard agent interoperability language backed by Cisco, LangChain, LlamaIndex, and others; the Open Voice Interoperability Initiative; and more diffuse collaborations, such as that announced by Qualtrics with LangChain.
  3. Identity and trust. Agents must verify the authenticity of entities that they interact with, determine permissions, and enforce security constraints. A standardized approach to authentication, authorization, and trust scoring would enable agents to evaluate new interactions dynamically. This would create an AI-native equivalent of Zero Trust security, allowing agents to autonomously assess whether another entity is legitimate before engaging in transactions or sharing sensitive information. While there are a few early attempts at evolving standards for data provenance for AI or for decentralized trust on the internet at large, nothing currently exists that’s specific to AI agents. Many emerging agentic platforms are either extending their retrieval-augmented generation governance systems, attempting to repurpose existing governance tools for agents, or have this as a roadmap item to tackle within the next year.
  4. Memory. Agentic networks must have a shared way to remember past interactions, retain relevant knowledge, and apply context across multiple exchanges. A standardized model for memory persistence and retrieval would allow agents to maintain a continuous understanding of their tasks and relationships. Currently, each major agentic AI development platform implements its own memory components.
  5. Knowledge sharing and reasoning. A common protocol around how agents share, verify, and refine knowledge would enable collaborative intelligence where multiple agents contribute specialized expertise to solve complex problems. This would also contribute to improving reliability and trust between both different system components as well as system components and humans.
  6. Marketplaces and transactions. As AI agents become more autonomous, they will need mechanisms for negotiating payments, purchasing services, and compensating other agents for computational and economic work. A shared financial protocol would enable seamless transactions while ensuring that they are secure, verifiable, and fraud-resistant. Stripe’s “agent toolkit” is directionally indicative but still far from a standard.
  7. Governance. AI agents might operate across different regulatory frameworks and ethical guidelines, but there is no standardized way for them to interpret and enforce these constraints consistently. Interoperability in governance will enable agents to dynamically recognize, apply, and comply with shared policy standards, ensuring that cross-platform interactions remain legally and ethically aligned.
  8. Discovery. In an open ecosystem, agents must be able to find and identify each other dynamically, rather than relying on hard-coded connections. Current agent systems lack a universal way to advertise their capabilities, verify their credentials, and negotiate interactions in real time. This makes it difficult to create scalable agent networks that function like the internet, where new services can be discovered and integrated without manual intervention. A standardized registry and discovery protocol would allow agents to locate compatible peers, assess their trustworthiness, and initiate collaborations autonomously.
  9. Error handling and conflict resolution. AI agents operate on significantly higher cognitive levels than deterministic tools do. A standardized way for them to detect, report, and respond to failures would be a key aspect of failure management, enabling agents to communicate, escalate, negotiate, and resolve conflicts and errors across diverse agent ecosystems. This is particularly important in larger-scale heterogeneous systems where individual AI agents may be used as part of larger workflows and each of those larger workflows may have different levels of accuracy required or different regulatory requirements associated with each.

Achieving seamless interoperability across these foundational dimensions is essential to unlocking the full transformative potential of agentic AI. We stand at the nascent frontier of this evolution, with a handful of early interoperability standards emerging in patches and with varying degrees of maturity. There is still a long way to go.