Tech heavyweights align on agentic AI standards, promising more choice for CIOs

Tech industry heavyweights including Anthropic, AWS, Google, Microsoft, and IBM are beginning to align around shared standards for AI agents, a shift that could give CIOs more flexibility and reduce dependence on any single provider’s platform.

The Agentic AI Foundation (AAIF), announced on Tuesday, aims to develop common protocols for how agents access data and interact with business systems, reflecting growing concern that today’s mix of proprietary tools will hold back broader adoption.

Many early deployments rely on custom connectors or one vendor’s agent framework, making it difficult to integrate other tools as projects scale. A recent Futurum Group report suggests that the agent landscape is fragmented and inconsistent, warning that enterprises will face higher costs and governance risks without open specifications.

AAIF’s goal is to make it easier for agents to work together by agreeing on how they authenticate, share context, and take actions across systems.

Anthropic has contributed its widely adopted Model Context Protocol (MCP) as the core starting point, with Block’s goose and OpenAI’s AGENTS.md also joining the initial set of projects, giving the group established building blocks rather than a standard starting from scratch.

Rising risks drive standards

Enterprises are running into unexpected forms of lock-in and integration complexity as they experiment with agentic AI, exposing architectural risks. Analysts say the underlying problem is that agent behavior itself can create hidden dependencies.

“With agentic platforms, the dependency is now coded into behavior,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “What appears modular on the surface often turns out to be tightly wound when organizations try to migrate or diversify.”

Tulika Sheel, senior vice president at Kadence International, agreed, adding that enterprises adopting agentic AI today risk becoming tied to a single vendor’s proprietary protocols and infrastructure, limiting flexibility and driving up switching costs. She said the formation of AAIF “makes it easier for enterprises to adopt agentic AI with confidence, giving them more control over their AI choices.”

How shared standards can reshape architectures

For CIOs, the real question is whether vendors can agree on practical interfaces and safety rules that work across platforms. Analysts say this will determine whether AAIF becomes a meaningful foundation for enterprise agent deployments or ends up as just another standards effort with limited impact.

“Open foundation models are used for nearly 70% of generative AI use cases today, and over 80% of enterprises say open source is extremely or very important in their generative AI application stack, especially in the development and fine-tuning layers,” said Sharath Srinivasamurthy, research vice president at IDC. “Hence, enterprises are already designing their architecture keeping open environments in mind.”

Shared protocols could accelerate that shift. According to Lian Jye Su, chief analyst at Omdia, common standards for agent interoperability have the potential to reshape how AI architectures are designed and deployed.

“Firstly, agentic AI applications can shift from rigid, vendor-specific silos to modular, composable systems with plug-and-play capability,” Su said. “Second, enterprises can enjoy seamless portability, shifting their workloads easily from one environment to another without a strong tie-in.”

Su added that clearer standards could also improve governance and orchestration. Transparent oversight mechanisms, combined with consistent integration rules, would allow enterprises to coordinate multi-agent workflows more efficiently. Seamless orchestration, he said, is essential for generating accurate and trustworthy outputs at scale.

Will vendors stay aligned?

Even with momentum building, analysts caution that the harder part may be sustaining cross-vendor alignment once implementations begin.

Gogia said the real test of AAIF will not be technical but behavioral, noting that vendors often align on paper long before they do so in practice. The difference now, he added, is the sheer complexity of agentic AI systems.

“Agentic AI is not just infrastructure,” Gogia said. “It’s behavioral autonomy encoded in software. When agents act unpredictably, or when standards drift from implementation, the consequences are not limited to system bugs. They extend into legal exposure, operational failures, and reputational damage.”

Su agreed that alignment is possible but not guaranteed. “Aligning major vendors around shared governance, APIs, and safety protocols for agents is realistic but challenging,” Su said, citing issues like rising expectations and regulatory pressure.

Sheel said early indicators of progress will include wider production use of MCP and AGENTS.md, cross-vendor governance guidelines, and tooling for auditability and inter-agent communication that works consistently across platforms: “We’ll know it’s working when enterprises can use these tools and safety controls at scale, not just in proofs of concept.”