How Agentic Protocols Are Transforming AI Development

Artificial intelligence is on the brink of its next great leap. After years dominated by generative AI, 2025 is shaping up to be the year of agentic AI. Unlike their generative predecessors, which only respond to prompts and create new content, agentic AI systems do much more. They are built not just to interact with humans, but to actively perceive their surroundings, make independent decisions, and carry out tasks to achieve specific goals. This change has the potential to make a significant impact. While generative AI has mostly focused on delivering information or producing content, Agentic AI is about taking action and enabling collaboration. These systems are built to work together and solve complex, real-world problems as a team, not in isolation. However, unlocking the full potential of agentic AI requires a fundamental shift in how AI systems interact with each other, with data, and with the world around them. This is where agentic protocols are reshaping the field. They are transforming isolated, single-purpose tools into powerful, collaborative networks that can achieve much more than any individual AI on its own.

The Problem with Isolated AI Systems

Despite rapid advancements in the field, most AI solutions today still operate in isolation. Traditionally, organizations have built separate AI systems for specific needs, such as customer support, fraud detection, or inventory management. These systems cannot easily communicate with one another. They use different data formats, have unique ways of exchanging information, and follow their own operational protocols. While this approach may be workable for simple applications, it leads to fragmentation, duplicated effort, and wasted resources. Organizations often end up maintaining multiple overlapping systems, each requiring its own infrastructure and expertise, which increases complexity and operational costs.

The main challenge is integration. When organizations want their different AI systems to work together, they face significant technical barriers. For example, a chatbot handling customer questions can’t easily share information with the logistics platform that tracks orders. Fraud detection tools also operate separately from risk assessment tools. Because these systems don’t speak the same language, connecting them usually requires expensive and fragile custom solutions. This not only reduces efficiency but also makes it hard for organizations to make the most of their data.

Recent trends have made these challenges more apparent. Studies show that 96% of organizations plan to increase their use of AI agents in the coming year, with most considering it vital to staying competitive. However, these AI agents require seamless coordination, data sharing, and interoperability to work. If nothing changes, integrating these disconnected tools could soon become both a costly and unsustainable burden.

The Rise of Agentic Protocols

To tackle the growing challenges of AI silos, the AI community is developing agentic protocols. These are standard rules and interfaces that let AI systems communicate and work together. The underlying idea is to build the foundation for what many now call the “Internet of Agents” where agents can find each other, share information, and collaborate. It is similar to how the early internet helped computers around the world connect and interact.

• Model Context Protocol (MCP)

The Model Context Protocol, launched by Anthropic in late 2024, is quickly becoming the standard for connecting AI systems to external tools and data sources. Many call it the USB-C port for AI because, like a universal connector, it replaces a tangle of incompatible systems with one simple standard.

Previously, connecting an AI system to a new tool or database meant writing custom code for each connection. MCP solves this problem. Now, AI applications can use a single, standardized protocol to access many different data sources, APIs, and services without requiring any special integrations. Major tech companies like AWS, IBM, and Cloudflare have already adopted MCP, with OpenAI and Google DeepMind following their lead. Early implementations show that organizations using MCP report significant improvements in automation and productivity, sometimes boosting workflow efficiency by more than 50%

• Agent2Agent (A2A) Protocol

While MCP focuses on connecting AI systems to external tools, Google’s Agent2Agent (A2A) protocol allows AI agents to communicate directly with each other. Introduced in April 2025, A2A allows autonomous AI agents to discover each other, negotiate their roles, and collaborate on complex tasks. Importantly, they can do all this without exposing their proprietary algorithms or sensitive inner workings. This is especially valuable in business environments, where organizations often use AI solutions from different vendors and need to protect intellectual property.

A2A introduces the concept of “Agent Cards.” These are like digital business cards that describe an agent’s capabilities and how to connect with it. These cards summarize each agent’s skills and provide details for making connections. The protocol also includes a structured system to manage the entire process of collaborative work, from assignment to completion. With built-in support for different communication formats, the protocol offers a high degree of flexibility. The Linux Foundation’s decision to adopt A2A as an open, vendor-neutral standard ensures that it will remain widely accessible and continue to drive industry innovation.

• Agent Communication Protocol (ACP)

IBM’s Agent Communication Protocol (ACP) takes a different approach by focusing on simplicity and easy adoption. Developed as part of the BeeAI platform and now managed by the Linux Foundation, ACP uses familiar web technologies like REST APIs and JSON. This makes it straightforward for developers to use, even if they don’t have deep AI expertise.

ACP supports both real-time (synchronous) and delayed (asynchronous) communication between agents. It also includes built-in discovery features, allowing agents to find and connect with each other, even in environments with limited or no internet access. This practical, low-requirement makes ACP an attractive choice for organizations that want to enable agent collaboration without a complicated setup.

Benefits of Standardized AI Communication

The adoption of agentic protocols is a major milestone in AI development. By moving from isolated tools to collaborative agent ecosystems, organizations can achieve benefits beyond technical improvements.

• Enhanced Interoperability

Agentic protocols act like universal translators for AI. Organizations no longer need to spend time and resources building unique integrations for every system. Instead, AI agents from different vendors, and even those built with different programming languages, can now communicate easily through shared standards. This level of interoperability allows organizations to combine the best solutions available, creating a more flexible and adaptable AI environment.

• Increased Efficiency and Automation

Standardized protocols can significantly reduce manual work and duplication of effort. When AI agents can coordinate effortlessly, many routine tasks that once needed human oversight are now handled automatically. Early adopters have seen up to 40% improvements in operational efficiency as workflows become smoother and less dependent on people for coordination.

• Improved Security and Governance

Standardization also brings many security advantages. With unified protocols, it’s easier to apply consistent authentication, authorization, and audit rules across an entire ecosystem of AI tools. This makes it simpler for organizations to meet compliance and regulatory standards. When AI agents follow the same security rules, it doesn’t matter what technology they are built on.

• Accelerated Innovation

Perhaps the most exciting benefit is the boost to innovation. Without the burden of constantly solving integration problems, developers are free to focus on building new and valuable features. This building block approach, where each agent, protocol, or tool can be reused and extended, encourages experimentation and speeds up AI progress across industries.

Overcoming Implementation Challenges

Despite these advantages, building a fully collaborative AI ecosystem is not without its challenges. Security and trust are major concerns. Organizations need to ensure that data stays private, systems remain reliable, and all compliance requirements are met before they deploy interconnected AI agents. There’s also a shortage of skilled professionals. The field of agentic AI is advancing rapidly, but there aren’t enough experts to meet the growing demand. On top of that, many companies still use older legacy systems that lack the modern APIs or infrastructure needed for smooth integration with AI agents.

The Bottom Line

To achieve the vision of agentic AI, it is crucial to improve the integration and communication between AI systems and other tools and technologies. Protocols such as MCP, A2A, and ACP are emerging as key enablers for AI collaboration. By standardizing communication, these protocols help create more interoperable, efficient, and secure AI ecosystems. The transition from isolated, single-purpose AI tools to interconnected agents will lead to enhanced automation, lower operational costs, and accelerated innovation.