Leading tech companies are establishing open standards for agentic AI
When you’ve got five of the largest players in tech stepping up to the same table, you pay attention. Anthropic, AWS, Google, Microsoft, and IBM have launched the Agentic AI Foundation (AAIF). Their goal is simple and smart: build open and shared standards for agentic AI, intelligent systems that can independently make decisions, take actions, and interact with business environments.
Right now, this space is dominated by proprietary tools and vendor-specific frameworks. That slows things down when you want to move fast, scale fast, and integrate across platforms. Custom connectors and narrowly defined interfaces make AI deployment more complicated than it needs to be. AAIF isn’t just another standards body, it’s setting out to fix this by aligning on how agents interact with your systems, authenticate across services, and share critical context.
What’s practical here is this: instead of starting from nothing, AAIF builds on work that’s already proven effective. Anthropic put up its well-used Model Context Protocol (MCP) as a foundation. Block’s goose and OpenAI’s AGENTS.md are also part of the mix. These aren’t experiments, these are real tools already solving problems. By combining them, the group’s setting up an operating baseline that teams can apply without reinventing the wheel every time.
For CIOs and CTOs, this alignment brings significant upside, interoperability, lower integration costs, and freedom from single-vendor dependence. It’s about strategic control: being able to choose the best tools from different vendors and expect them to work together, consistently.
Tulika Sheel, SVP at Kadence International, made the point clear, AAIF creates the conditions for enterprises to adopt agentic AI with more confidence and less lock-in. That’s the kind of move that de-risks the roadmap while keeping your AI stack flexible. Companies that want to scale AI capabilities without bloating complexity need to follow this space closely.
Fragmentation and proprietary architectures pose integration and vendor lock-in risks
Here’s the current problem. Most of the agentic AI systems out there aren’t built to play well together. You’re seeing vendors push their own frameworks, custom APIs, one-off connectors, private protocols. On paper, it might look modular. In real-world deployments, it rarely is.
This leads to one critical failure: integration doesn’t scale. The second you try to expand across tools or switch providers, costs and headaches rise. Governance gets messy. Maintenance becomes expensive. You wind up stuck, relying on the same vendor, not because it’s the best solution, but because changing course is too risky or too complex.
Experts are flagging this issue as an urgent one. A recent analysis by Futurum Group called the agentic AI ecosystem “fragmented and inconsistent,” warning that without standardized protocols, organizations will face higher operating costs and greater governance exposure. That should matter to every exec managing digital transformation or large-scale AI rollouts.
The core of the challenge? Behavior-coded lock-in. AI agents don’t just read data, they act. And when those actions are pre-coded within a specific vendor’s tech stack, you end up boxed in. Sanchit Vir Gogia, Chief Analyst at Greyhound Research, described it as “dependency now coded into behavior.” Once you’re embedded in one system, changing course without breaking something becomes extremely difficult.
The takeaway is clear: this space needs open standards that are actually implementable. Otherwise, businesses will keep building fragile architectures that can’t adapt or scale. C-suite leaders need to recognize these dependencies early, because once they’re in place, they’re expensive and operationally dangerous to undo.
Open standards can foster modular, composable AI architectures
Enterprise software doesn’t need to be rigid. That’s the point of what’s happening in agentic AI right now. With shared protocols on the table, there’s finally a path to break away from narrow, closed-off systems and build something modular, something you can deploy flexibly, upgrade without disruption, and connect across environments without endless custom work.
The direction this is heading is toward plug-and-play capability. That means agentic AI systems that can be introduced into your workflow or removed just as easily, without pulling apart your architecture. Enterprises gain the ability to scale operational intelligence without bottlenecks tied to vendor-specific idiosyncrasies.
It’s not only about convenience. Lack of interoperability kills velocity. Without it, every deployment becomes a bespoke project, and that impacts delivery timelines, team productivity, and long-term viability. Shared standards change that equation. They make interoperability real, make migration simpler, and support balanced governance across complex AI systems.
Lian Jye Su, Chief Analyst at Omdia, said it clearly, these common frameworks don’t just enable portability. They reshape how AI is fundamentally structured and deployed. His point on orchestration is critical: shared specs allow for smoother coordination between multiple agents. That level of structured oversight greatly increases your chance of generating outputs that are accurate, aligned with policy, and scalable.
For CIOs and transformation leaders, this opens a clear path to scalable AI adoption, systems that are flexible enough to evolve with your business and clear enough in structure to remain governable. It lowers risk, reduces complexity, and boosts future adaptability.
Increasing adoption of open foundation models bolsters the case for shared standards
The numbers don’t lie. Most enterprises are already betting heavily on open-source infrastructure for AI. And it’s not a casual decision, it’s rooted in the reality of how flexible and efficient modern architectures need to be.
According to Sharath Srinivasamurthy, Research Vice President at IDC, open foundation models are used in nearly 70% of generative AI applications today. More importantly, over 80% of enterprises say that open source is “extremely” or “very” important across development and fine-tuning stages. That’s not fringe, it’s mainstream behavior across sectors that prioritize scale and pace.
This adoption also aligns with the broader push for accountability and independence. Closed systems don’t offer the same level of insight or customizability. When workflows are built on open components, you’re not locked into anyone’s roadmap or constrained by someone else’s limitations.
Enterprises that anchor their AI strategies in open environments are better positioned to drive innovation faster, deploy with fewer constraints, and adapt to changes, technical, regulatory, or operational. It also puts integrated standards, like those being built under AAIF, within reach. If your stack already values transparency and modularity, integrating shared protocols is more straightforward. That means time-to-value improves, governance becomes easier to maintain, and vendor flexibility becomes a built-in advantage.
For any C-suite leader, this isn’t about chasing trends. It’s about staying competitive in a domain that rewards speed and rewards control. If your AI development relies on closed, rigid systems, you’ll eventually hit a wall. Open foundation models provide the groundwork to keep moving forward without compromise.
Sustaining cross-vendor alignment remains a critical challenge
Bringing major tech vendors together under one initiative is progress, but keeping them aligned as real-world deployments scale is the hard part. Agentic AI isn’t just infrastructure. It’s a dynamic layer of autonomy, behavior, and learning that interacts with sensitive data and enterprise workflows at scale. That makes alignment not just technical, but operational and legal.
What works in the lab doesn’t always hold under production pressure. Shared standards like those developed by the Agentic AI Foundation (AAIF) need to function consistently across environments, teams, and implementations. A mismatch between theory and practice here doesn’t just create bugs, it creates real risk. That includes compliance failures, output instability, and brand liability.
Sanchit Vir Gogia, Chief Analyst at Greyhound Research, puts a fine point on it: “Agentic AI is not just infrastructure. It’s behavioral autonomy encoded in software.” He highlights that when implementations diverge from the agreed standard, the results won’t just break systems, they can generate operational failures or even open up legal exposure. That’s not abstract risk. These systems make decisions, trigger actions, and interact with regulated data. You need them aligned or you compromise control.
Lian Jye Su, Chief Analyst at Omdia, also notes that alignment is “realistic but challenging.” Regulatory pressure adds urgency, but managing expectations and ongoing convergence across APIs, protocols, safety standards, and governance models is no small task.
According to Tulika Sheel, SVP at Kadence International, there are signals decision-makers should watch. Widespread application of protocols like MCP and AGENTS.md, increased cross-vendor tooling for auditability, and consistent inter-agent communication architectures, these will be early proofs that alignment is moving in the right direction. If these tools stay confined to proofs-of-concept and never scale, the promise of AAIF remains unfulfilled.
For executive leaders, the message is clear: don’t just assess whether a standard exists, assess whether it’s being implemented through working software, transparent integrations, and measurable risk controls. Standards talk is common. Responsible implementation is what actually drives impact.
Key takeaways for leaders
- Major vendors embrace AI standards: Leaders from Anthropic, AWS, Google, Microsoft, and IBM are backing shared agentic AI protocols to enable cross-platform tools and reduce vendor dependency. Executives should track AAIF progress to ensure their AI investments remain interoperable and strategically agile.
- Proprietary AI tools increase long-term risk: Fragmented systems and vendor-specific protocols create hidden dependencies that raise costs and limit flexibility. CIOs should prioritize solutions built on open standards to minimize future migration challenges.
- Modular AI architectures improve scalability: Shared protocols support plug-and-play agentic AI, enabling enterprises to expand and adapt systems without reintegration overhead. Leaders should seek AI tools with clearly defined, standards-based interfaces to maintain architectural control.
- Open-source adoption is accelerating enterprise AI: Most businesses now favor open foundation models for generative AI development, confirming the strategic shift toward openness. Decision-makers should align infrastructure planning with open-source trends to maintain compatibility and speed.
- Cross-vendor alignment will test implementation depth: Sustained cooperation among tech giants is needed for standards like AAIF to deliver real operational value. Executives should monitor implementation benchmarks, such as usage of MCP, AGENTS.md, and audit tools, to assess long-term ecosystem viability.
