What is the difference between build-time interoperability and run-time interoperability for AI agents?

Why This Matters for Security Teams

Build-time interoperability answers a procurement and engineering question: can teams swap model providers, orchestration libraries, or agent frameworks without rewriting everything? Run-time interoperability answers a security and operations question: can those agents still discover tools, exchange tasks, and share policy enforcement in a controlled way while the environment changes? That distinction matters because AI agents are not static services. They are goal-driven workloads that may chain tools, call other agents, and change behaviour based on context.

Security teams often assume that framework choice is the main interoperability problem. In practice, the harder issue is whether governance, identity, and authorisation stay consistent once agents begin operating across boundaries. Guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework both point toward runtime controls, not just design-time portability. NHIMG research on the OWASP NHI Top 10 also highlights how agentic systems fail when identity and tool access are treated as one-time integration work. In practice, many security teams encounter interoperability as an incident response problem only after an agent has already made an unexpected tool call or crossed an approval boundary.

How It Works in Practice

Build-time interoperability is mostly about software compatibility. Teams want the freedom to change orchestration layers, swap LLM providers, or move from one agent framework to another without redesigning every workflow. That usually depends on abstraction layers, common message formats, and portable tool definitions. It reduces vendor lock-in and makes migration feasible.

Run-time interoperability is different. It is the live ability of an agent to authenticate, discover, request, and use tools or peer agents through shared protocols while governance remains consistent. For that to work, the agent needs workload identity, not just an API key copied into a config file. Best practice is evolving toward cryptographic identity for the workload, short-lived credentials, and policy evaluation at request time rather than at deployment time. Standards and implementation guidance increasingly point to protocol-driven exchange, such as SPIFFE for workload identity and policy engines that can evaluate context at runtime.

• Build-time portability lowers rewrite cost when frameworks change.
• Run-time interoperability keeps tool access, agent-to-agent calls, and approvals governed under one policy model.
• JIT credentials and ephemeral secrets limit how much damage a compromised agent can do.
• Uniform policy enforcement prevents one agent from becoming a bypass path for another.

NHIMG’s reporting on Analysis of Claude Code Security and CoPhish OAuth Token Theft via Copilot Studio shows why this distinction matters: if runtime trust is weak, an agent can be integrated successfully and still be dangerous operationally. These controls tend to break down when teams rely on static service accounts for autonomous agents because behaviour, tool choice, and access paths change after deployment.

Common Variations and Edge Cases

Tighter runtime controls often increase integration overhead, requiring organisations to balance agility against governance. That tradeoff is real, especially in multi-agent environments where every extra policy check, token exchange, or approval step can affect latency and developer experience.

There is no universal standard for runtime agent interoperability yet. Current guidance suggests treating shared protocols, identity, and policy as separate layers: protocols move requests, identity proves who the agent is, and policy decides whether the action is allowed. That separation matters in mixed estates where one team uses a proprietary orchestration stack and another uses a standards-based agent protocol. The build-time layer can remain flexible, but the run-time layer should be normalised around consistent authorisation and logging.

Two edge cases matter most. First, agent-to-agent delegation can appear interoperable while actually bypassing local approval logic if the receiving agent trusts the caller too broadly. Second, some legacy environments cannot support short-lived credentials or dynamic policy checks, so teams fall back to long-lived secrets. That is a compatibility compromise, not a security ideal. NHIMG’s State of Secrets in AppSec research shows how persistent secret sprawl undermines control, and the CSA MAESTRO agentic AI threat modeling framework reinforces the need to model these handoffs explicitly. In practice, runtime interoperability fails fastest in environments that mix legacy secrets, ad hoc agent delegation, and toolchains that cannot enforce policy uniformly.

Related resources from NHI Mgmt Group

• What is the difference between continuous authorization and login-time authentication for AI agents?
• What is the difference between managed identities and hardcoded secrets for AI agents?
• What is the difference between workload identity and API keys for AI agents?
• What is the difference between logging actions and logging intent for AI agents?