Scaling Agentic AI - Impact of Model Context (MCP) and Agentic (A2A) protocols

Introduction

In the ever-evolving landscape of technology and artificial intelligence (AI), the development of agentic solutions — systems that can act autonomously based on environmental stimuli — is becoming increasingly critical. Two essential methodologies that have emerged in this domain are the Model Context Protocol (MCP) from Anthropic and the Agent-to-Agent (A2A) protocol from Google. These methodologies facilitate the creation of more robust, adaptable and efficient agentic systems.

Benefits of using Model Context Protocol and A2A in designing agentic solutions

Complementary tools for AI integration

MCP Protocol: This protocol addresses the integration of AI agents with external tools, systems and data sources. It provides a standardized, model-agnostic interface for AI assistants to access resources, execute functions and handle contextual prompts. MCP lowers the barrier to creating AI applications that are deeply integrated with real-world data and specific functionalities.

A2A Protocol: Focuses on agent-to-agent interoperability, enabling AI agents to communicate, coordinate and collaborate across diverse platforms. This initiative aims to dismantle silos that often hinder enterprise automation. A2A paves the way for automating complex, multi-step processes by allowing specialized AI agents to work in concert, creating more efficient and scalable AI solutions.

In summary, MCP and A2A protocols are foundational to agentic AI because they enable seamless interoperability, contextual awareness, adaptive problem-solving and efficient collaboration among AI agents. These protocols allow AI systems to operate autonomously, securely and effectively in complex environments, driving innovation and operational efficiency 

Understanding Anthropic's Model Context Protocol (MCP)

Model Context Protocol (MCP) is an open standard that enables AI models to connect with their data sources and allows multiple LLMs to extract context using a universal adapter approach. This protocol is useful for agentic AI-based systems that require access to diverse platforms and various data sources.

Dynamic adaptability

One of MCP's primary benefits is its ability to provide dynamic adaptability to agentic systems. By using contextual models, agents can adjust their behavior in real time to suit the changing conditions of their environment. This adaptability is crucial in scenarios where static programming may fail to anticipate all possible variables.

Enhanced decision making

MCP empowers agents to make more nuanced and informed decisions. By considering a wide range of contextual factors, agents can evaluate the potential outcomes of their actions more effectively. This leads to better performance in terms of providing greater context and speed in a structured fashion with a higher degree of autonomy.

Scalability

The use of MCP allows for greater scalability in system design. As new contextual factors emerge, they can be integrated into the existing models without the need for extensive reprogramming. This means that the agentic solutions remain relevant and effective over time.

Context management

The MCP architecture efficiently manages persistent context in demanding environments through real-time synchronization, priority queuing for essential versus non-essential sessions, parallel query routing and distributed caching to minimize duplication of work.

Exploring Google's Agent-to-Agent (A2A) 

Agent-to-Agent (A2A) communication is a methodology that facilitates direct interaction between autonomous agents. This approach is a significant advancement in enabling agentic AI systems to operate at a higher level of scalability. By allowing agents to share information directly, A2A communication enhances the reasoning and decision-making processes within these systems, leading to seamless and efficient collaborative efforts.

Improved coordination

A2A communication enhances coordination among agents, allowing them to work together seamlessly towards a common goal. This is particularly useful in complex environments where multiple agents need to collaborate to achieve optimal results. The ability to share real-time information and updates enables agents to synchronize their actions, leading to more coherent strategies and outcomes.

Distributed problem solving

With A2A communication, the problem-solving process is distributed across multiple agents. Each agent can tackle specific aspects of a problem, and through communication, they can share their findings and solutions. This distributed approach leads to more efficient and effective problem-solving, as agents can leverage collective intelligence and diverse perspectives.

Redundancy and reliability

A2A communication introduces a level of redundancy and reliability into the system. If one agent encounters an issue or fails, other agents can step in to take over its responsibilities. This redundancy ensures that the overall system remains resilient and continues to function effectively, mitigating potential disruptions and maintaining operational stability.

Context-aware collaboration

Agents using MCP can share contextual information through A2A communication, leading to more effective collaboration. This context-aware interaction allows agents to align their actions based on a shared understanding of the environment, leading to more coherent and efficient outcomes. This context-aware collaboration is critical in dynamic environments where conditions are constantly changing, and adaptive responses are required for success.

In summary, the integration of A2A is crucial for the success of Agentic AI. It facilitates seamless reasoning, enhances contextual awareness, streamlines decision-making, and promotes efficient collaboration among AI agents. The A2A protocol enables AI systems to operate autonomously, securely and effectively in complex environments, thereby driving innovation and improving operational efficiency.

Synergy between MCP and A2A

When combined, MCP and A2A create a powerful synergy that significantly enhances the capabilities of agentic solutions. The contextual awareness provided by MCP, coupled with the collaborative potential of A2A, results in systems that are not only highly autonomous but also capable of complex, coordinated actions.

Context-aware collaboration

Agents using MCP can share contextual information through A2A communication, leading to more effective collaboration. This context-aware interaction allows agents to align their actions based on a shared understanding of the environment, leading to more coherent and efficient outcomes.

Adaptive problem solving

The combination of MCP and A2A enables adaptive problem-solving. Agents can dynamically adjust their strategies based on real-time contextual data and communicate these adjustments to other agents. This allows the system to respond to unforeseen challenges and evolve its problem-solving approach continuously.

Summary

• Holistic integration: Combining A2A and MCP can lead to a robust AI ecosystem where agents not only collaborate effectively but also have access to the tools and data they need.
• Vendor neutrality: Both protocols are open standards, reducing dependency on specific vendors and promoting flexibility in technology choices.
• Scalability: The modular nature of these protocols supports scalable AI deployments, accommodating growth and evolving business needs.
• Security and compliance: Built-in security features align with enterprise requirements, aiding in compliance and risk management.

Reference architectures 

A recent blog on WWT provides an in-depth look at MCP and highlights its advantages. Standardizing MCP server connections to information repositories helps reduce the need to develop duplicate information collections, thus enhancing the overall architecture of information search. 

Additionally, the Google-developed Agent-to-Agent Protocol improves agent applications in terms of scalability and action-oriented results.

Hybrid architecture using Google A2A Protocol

Reference Link Tech 1: Agentic MCP and A2A Architecture: A Comprehensive Guide | by Anil Jain | AI / ML Architect | Data Architect | Apr, 2025 | Medium

Reference Link Tech 2: Model Context Protocol Deep Dive - https://www.wwt.com/blog/model-context-protocol-mcp-a-deep-dive 

Agentic MCP (Model Context Protocol) is a protocol that helps AI models, like chatbots, connect to external systems such as databases or business tools. It's like giving the AI a set of tools to fetch information or perform tasks during a conversation, making it smarter and more helpful.

The A2A (Agent-to-Agent) Protocol lets different AI agents talk to each other. Imagine a team where each agent has a job, like one handles customer queries and another manages tickets—they can work together smoothly using A2A.

MCP + Agent-to-Agent Protocol architecture example

Reference: https://google.github.io/A2A/#/

Comparison of A2A vs MCP Protocol architectures

https://www.toolworthy.ai/blog/mcp-vs-a2a-protocol-comparison

Workforce skills and technology stack requirements

To successfully implement Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols within agentic AI frameworks, certain upskilling and a robust technology stack understanding are essential.

Workforce skill upgrades

Professionals involved in the implementation of MCP and A2A protocols need to be proficient in several areas:

• Understanding protocol architectures: In-depth knowledge of MCP and A2A architectures, including their design principles, communication methods and protocols.
• AI and machine learning: Strong foundation in AI and machine learning algorithms, particularly those used for autonomous decision-making and contextual awareness.
• Software development: Expertise in software development, with a focus on programming languages such as Python, Java, R, Scala, C# and C++, which are commonly used for AI development.
• Networking and security: Knowledge of network protocols, cybersecurity principles and practices to ensure secure and reliable agent communication.
• Project management: Skills in project management to oversee the implementation process, coordinate between different teams and ensure timely delivery.
• Domain-specific knowledge: Familiarity with the specific domain applications of MCP and A2A, such as smart cities, autonomous vehicles and healthcare.

Technology stack

A robust technology stack is necessary to support MCP and A2A implementations:

• Agentic AI platforms: LangGraph, LangChain, Microsoft AutoGen, CrewAI or Google ADK
• Database implementations with Vector Implementations: Supabase, Pinecone, FAISS or PostgreSQL
• Large language model implementations including Claude, Gemini v2.5, DeepSeek-V3 or OpenAI v4o or similar
• Data management tools: Tools such as Hadoop or Apache Spark are used to handle large datasets and ensure efficient data processing.
• AI frameworks: Frameworks like TensorFlow, PyTorch or Keras for building and training AI models.
• Communication protocols: Implementation of communication protocols like MQTT or HTTP for agent interaction.
• Cloud services: Utilization of cloud services such as AWS, Google Cloud or Azure for scalable AI deployments.
• Security solutions: Integration of security solutions like firewalls, encryption tools, and intrusion detection systems to safeguard agent communications.
• Monitoring and analysis tools: Tools like ELK Stack, Grafana, or Prometheus for monitoring system performance and analyzing agent interactions. This assists with debugging the Agent-to-Agent transactions and helps fine-tune the responses and reduce hallucinations.

Adoption and use cases 

Model Context Protocol industry adoption

Structured framework: MCP is designed to manage and exchange contextual data between clients and large language models (LLMs). It provides a structured framework for handling context, including conversation history, tool calls, agent states and other information needed for coherent and effective AI interactions.

Consistency and scalability: MCP addresses the challenge of maintaining and structuring context consistently, reliably and scalably.

Industry category and use cases

Healthcare

The Healthcare Model Context Protocol (HMCP) is designed to integrate healthcare AI agents with data, tools, FHIR APIs and workflows within a secure, compliant and standards-based framework. It ensures AI agents handle sensitive patient data securely, adhere to compliance regulations and maintain consistent interoperability across diverse clinical workflows.

Use cases:

• Ambient clinical documentation: AI agents support real-time clinical documentation to reduce clinician burnout from Electronic Health Record systems. Current AI technologies capture voice conversations and virtual meetings in the clinical space. Agentic AI with MCP protocols will integrate physician notes with audio transcripts and assist in codifying for reimbursement, medication and lab orders with additional context to allow for fewer error opportunities.
• Decision support: AI-driven decision support systems for healthcare providers. Current EHRs are further augmented with Agent-based decision support systems to allow for additional precision in the specific diagnostics, medication and other interventions to assist with patient care.
• Patient data analysis: To achieve better outcomes, it is essential to analyze patient data securely and in compliance with regulations. Ensuring the secure handling of patient information is crucial, with de-identification or encryption playing a vital role, particularly for general research purposes. Clinical decision-making through A2A protocols can be significantly enhanced by conducting similarity searches, identifying outliers, and analyzing trends based on longitudinal history. This includes grouping patients by age, events, procedures, genetic predisposition and demographics (SDOH). By utilizing MCP and A2A protocols, standardization can be driven, providing faster insights and thereby improving the decision-making process.

Global finance

MCP is emerging as a critical element for next-generation AI applications in the finance sector. It facilitates how AI models exchange information and context, ensuring interoperability for various AI tools, agents, and systems.

Use cases:

• Fraud detection: AI models analyze transaction data to detect fraudulent activities. This is executed by looking at the usage and transaction patterns to eliminate any fraud occurrence.
• Customer service automation: Utilize AI-driven chatbots and virtual assistants to address customer inquiries efficiently. This approach facilitates prompt responses to customers seeking information from designated financial transaction databases and reports, leveraging MCP's capability to interface with multiple information repositories.
• Financial analysis: AI models offer valuable insights and predictions based on financial data. These models can significantly assist financial advisors by providing access to various recommendations, drawing from information on past loans, stock prices, mortgage rates, real estate risk assessments, and other relevant financial parameters.

Telecommunications

MCP enables seamless communication between AI models and external tools, enhancing decision-making and scalability in telecommunications.

Use cases:

• Network management: AI models optimize network performance and manage resources. They help implement real-time trust assessment, Quality of Service(QoS) negotiations and edge capabilities.
• Customer interaction: AI-driven virtual assistants are improving customer service, including rapid assessment of problem areas in the network, knowledge base responses from past incidents or from engineering design databases.
• Data analysis: AI models analyze large volumes of data for insights and optimization. Agents can provide additional observability interventions to solve network bottlenecks or provide heatmaps that allow more insights into vulnerabilities.

These projects demonstrate MCP's versatility and potential for enhancing AI integration, improving decision-making, and ensuring compliance across various sectors.

Agent-to-Agent Protocol industry adoption

Runtime Discovery: A2A enables runtime discovery of agent capabilities, providing endpoint URLs, authentication methods and task submission details.

Complementary Nature: A2A is designed to complement MCP by enabling inter-agent communication and coordination, allowing AI agents to work together effectively.

Industry and use cases:

Healthcare

The Agent2Agent (A2A) Protocol enables independent AI agents to communicate, making it ideal for healthcare projects that need coordination among diagnostic, treatment planning and patient monitoring agents. This protocol allows AI agents from different frameworks to collaborate seamlessly, supporting comprehensive healthcare solutions. Agentic approaches also act on data with follow-up action plans, aiding care coordination.

Global finance

The A2A protocol connects agents for customer support, inventory management and finance, enabling automated processes across different departments. This protocol improves enterprise agent integration by offering a method to leverage agent capabilities within the technological ecosystem. Additionally, agents help coordinate with fraud detection, contract language support and identity management, thereby maintaining the financial integrity of applications.

Telecommunications

The A2A protocol allows AI agents to communicate, securely exchange information, and coordinate actions across platforms. It is ideal for traffic management, energy optimization and public services, enhancing city operations. Agents can improve human operations by managing network routing, assessing telemetry, isolating network segments and reprogramming equipment for optimized performance.

These projects demonstrate the versatility and potential of the A2A protocol in enhancing AI integration, improving decision-making, and ensuring compliance across various sectors.

Metrics on cost savings and productivity gains

Cost savings:

• Significant ROI: Studies indicate that companies investing in AI are realizing significant returns, with an average ROI of $3.7 for every $1 invested. Notably, 5 percent of organizations worldwide are achieving an even higher average ROI of $10 for every $1 invested.  Reference Link: https://techcommunity.microsoft.com/blog/machinelearningblog/a-framework-for-calculating-roi-for-agentic-ai-apps/4369169
• Procurement savings ROI: Procurement is poised to capitalize on AI, which can streamline manual work in key processes by up to 30 percent and reduce overall costs by roughly 15 percent to 45 percent. Reference Link: From Buzz to Bottom Line - Cost Savings Using GenAI | BCG

Productivity gains:

• Task automation: Gartner predicts a 25 percent reduction in customer service costs due to AI-driven automation. This reduction is largely attributed to the decreased need for human intervention in routine inquiries and transactions, as AI can handle these efficiently. Additionally, a report from Accenture highlights that AI can reduce error rates in customer service operations, further driving down costs and enhancing service quality. Reference Link: https://digitaldefynd.com/IQ/agentic-ai-statistics/
• Operational coordination: AI agents streamline operations across various departments, ensuring tasks like patient scheduling, follow-ups and emergency response are handled efficiently. Accenture estimates that agentic AI could save the healthcare industry approximately $50 billion annually, particularly in patient care administration. These savings stem from AI's ability to streamline research and development processes and enhance precision in patient treatment plans. A report from the American Hospital Association supports this, showing that AI applications in healthcare lead to a 20 percent reduction in diagnostic errors and a 15 percent decrease in treatment costs. Reference Link: Top 30 Agentic AI Facts & Statistics [2025] - DigitalDefynd

These metrics highlight the transformative impact of Agentic AI with MCP and A2A protocols, driving improvements in cost savings and productivity across various enterprise applications.

Challenges in adoption

Complexity of integration: Implementing MCP and A2A protocols involves integrating AI agents with existing systems and workflows, which can be complex and time-consuming. Adequate time and effort need to be placed into architectural approaches, especially where latency impact, high message volumes and state management could significantly impact user experience.

Reference Link: Scaling MCP in Distributed Systems: Architectural Patterns for High Performance | Markaicode

Standardization: Developing and adopting standardized protocols for agent communication and context management is crucial but challenging. Different organizations may have varying requirements and constraints. This is especially important when you have time-sensitive actions that need to be taken by agents. Standards allow structured approaches that lead to improved adoption. There are quite a few data standards that are applied in industry verticals, including examples in healthcare (DICOM, HL7 and FHIR), banking (BDM, IBAN and LEI), supply chain (GS1, RFID and SSCC) and cybersecurity (HIPAA, NIST, SOC2 and HITRUST).

Performance optimization: Ensuring that AI agents operate efficiently and effectively in real-time environments requires continuous performance monitoring and optimization. Approaches including context pruning, quantization-based context models, context compression and asynchronous message handling are key elements of architectural patterns, combined with high-performance computing (HPC), will be key in driving performance optimization in large-scale environments.

Adaptability: AI agents need to adapt to changing environments and requirements. MCP and A2A protocols must support dynamic adaptability to ensure that agents can respond to new challenges and opportunities.

Conclusion

The benefits of using Model Context Protocol and Agent-to-Agent communication in designing agentic solutions are profound. These methodologies provide dynamic adaptability, enhanced decision-making, improved coordination and distributed problem-solving capabilities. By leveraging the synergy between MCP and A2A, we can create advanced agentic systems that operate autonomously and collaboratively to address complex challenges effectively.

As technology continues to evolve, the integration of MCP and A2A will play an increasingly vital role in the development of robust, efficient, and intelligent agentic solutions, driving innovation and improving outcomes across various sectors.