As enterprises integrate AI agents into their workflows, a key architectural decision emerges: should you connect a single intelligent agent to many systems using the Model Context Protocol (MCP), or deploy multiple specialized agents—each connected to a single source—that coordinate using Agent-to-Agent (A2A) protocols?
This choice shapes how AI systems reason, interact, and scale across complex environments. MCP excels at enabling one agent to maintain context and act across diverse systems. A2A shines when domain-specific expertise and distributed coordination are essential. Understanding when to use each—and how they can work together—is critical for building scalable, intelligent enterprise AI solutions.
The single agent + MCP approach
In the MCP architecture, a single agent is equipped to interact with multiple back-end systems via a standardized interface. The Model Context Protocol acts as a middleware abstraction layer that mediates between the agent’s reasoning engine and the APIs, databases, or SaaS applications it must interact with. Think of MCP as translating natural language intent into structured tool invocations (e.g., SQL queries, API calls, file reads/writes) and relaying outputs back in a context-aware format.
This model emphasizes centralized intelligence with decentralized access. The agent retains all necessary context and reasoning history while dynamically fetching or pushing data via MCP servers. Because the agent always works with real-time data from live systems—rather than static copies—this model is particularly effective for:
- Monitoring and alerting systems
- Unified dashboards
- Operational assistants (e.g., “What’s the current sales pipeline status across all CRMs?”)
- Automation agents that span multiple business functions
Advantages:
- Simplified user experience: Users interact with a single agent capable of multitasking across various platforms
- Centralized reasoning: All logic, decision trees, and memory are held in one place, simplifying auditability and control
- Consistent context maintenance: The agent can correlate events across domains (e.g., ticket trends in Jira and related customer complaints in Salesforce)
- Lower inter-agent latency: No coordination is required with peers; everything routes through one central mind
Challenges:
- Agent complexity: The agent must reason across multiple domains (e.g., marketing, finance, HR) and maintain nuanced understanding of each.
- Scaling limits: The agent becomes a bottleneck under heavy load or large domain sprawl
- Failure sensitivity: If the agent or its hosting environment fails, the entire system may become unresponsive
This model maps well to centralized control-plane architectures and benefits from strong observability. As WorkOS describes it, MCP is the protocol for “vertical” integration, allowing AI agents to plug directly into existing application stacks in structured ways (WorkOS, 2024).
The multi-agent + A2A approach
In contrast to the MCP server model, the A2A approach favors a decentralized architecture: many lightweight, domain-specialized agents collaborate with each other using shared semantics and coordination protocols. Each agent owns a narrow slice of functionality—e.g., a calendar agent, a billing agent, a DevOps agent—and speaks A2A to publish intent, respond to queries, or participate in workflows.
Rather than connecting directly to tools, some of these agents are tool-facing (connected via MCP or other integrations), while others may act purely as workflow orchestrators. This mirrors microservice-style architectures: each agent is modular, independently deployable, and responsible for a bounded domain.
That said, it’s important to acknowledge that A2A protocols are still early in their lifecycle. While frameworks like AgentOS, LangGraph, and ACP are emerging, there is no universal standard yet, and many implementations remain experimental. This immaturity presents challenges but also represents an opportunity: the ability to shape open protocols, conventions, and coordination primitives for future agentic infrastructure.
A2A agents typically communicate via:
- Agent Cards: Standardized metadata to advertise their capabilities, schemas, and communication preferences
- Contextual messages: Which may or may not persist across sessions, depending on memory architecture
- Protocols like ACP: Used to negotiate capabilities and compose services dynamically (WorkOS, 2024)
Use cases include:
- Customer support systems where chat, knowledge retrieval, and ticket escalation are handled by separate agents
- Engineering copilots where debugging, logging, and code editing are segmented
- Long-running workflows (e.g., compliance reviews) that span hours or days and involve asynchronous coordination
Advantages:
- Domain specialization: Agents can be fine-tuned for specific tools or workflows, increasing accuracy and reliability
- Fault isolation: One failing agent doesn’t bring down the entire system
- Scalability: Teams can iterate on individual agents without disturbing the broader ecosystem
- Asynchronous execution: Tasks can be paused, deferred, or split across agents with natural transitions
Challenges:
- Inter-agent complexity: Workflows require careful message passing, dependency resolution, and error handling
- Context fragmentation: Agents may lose shared understanding or require rehydration of state mid-process
- Security and governance: Auditing and permissioning must span multiple agents and possibly multiple organizations
- Latency accumulation: Chained reasoning across agents can introduce non-trivial delays
As the WorkOS article notes, A2A represents “horizontal” integration—allowing independent agent services to interoperate, especially when no single agent has the full memory, tools, or domain authority to complete the task alone (WorkOS, 2024).
Building for the best of both worlds
In practice, MCP and A2A complement each other rather than compete. MCP provides vertical integration (application-to-model), while A2A provides horizontal integration (agent-to-agent) (WorkOS, 2024). The most sophisticated enterprise implementations will likely use both protocols:
- MCP for tool and data integration: Connecting agents to their specialized data sources and capabilities
- A2A for agent coordination: Enabling complex workflows that require multiple specialized agents
Google provides an example of a car repair shop use case to demonstrate how A2A and MCP could work together: MCP is the protocol to connect these agents with their structured tools (e.g., raise platform by 2 meters, turn wrench 4 mm to the right). A2A is the protocol that enables end-users or other agents to work with the shop employees (WorkOS, 2024).
As of now, MCP is further along in enterprise adoption—thanks to its structured approach and alignment with existing API infrastructure—whereas A2A is still evolving, with active exploration underway around standards for agent discovery, memory coordination, and trust boundaries.
The role of CData MCP Servers
In this hybrid architecture, CData’s MCP Servers serve as the foundational connectivity layer between AI agents and live enterprise systems. Each MCP Server functions as a highly-performant, low-latency gateway that allows agents to interact with real-time data from CRMs, ERPs, databases, cloud apps, and more—without needing to replicate or warehouse that data.
By abstracting complex APIs, authentication layers, and query translations, CData MCP Servers give agents a live, tool-native interface into the systems they need to operate. This allows each agent—whether general-purpose or domain-specific—to:
- Retrieve structured data (e.g., rows from Salesforce, tickets from Jira, events from ServiceNow)
- Invoke native actions (e.g., create, update, delete records)
- Do so in a standardized, language-model-friendly format
Crucially, CData MCP Servers enable composability. Agents connected to different MCP endpoints can be orchestrated via A2A protocols to collaborate across domains—e.g., one agent querying order data in SAP, while another handles fulfillment via a WMS API. CData’s unified protocol abstraction ensures that all these interactions are live, secure, and consistent.
In short, MCP Servers from CData are not just integration tools—they are the enablers of vertical agent intelligence. When paired with A2A for horizontal coordination, they unlock a powerful and flexible foundation for modern enterprise AI systems.
Conclusion
Choosing between MCP and A2A—or adopting a hybrid approach—depends on the specific needs and context of your enterprise AI implementation. MCP offers streamlined integration for centralized agents, while A2A provides flexibility and specialization through distributed agents. Importantly, while MCP protocols are already mature and deployable today, A2A is an emerging frontier—full of promise but still evolving in standardization and adoption. By understanding these trade-offs and planning accordingly, organizations can build robust, scalable AI systems that are both effective and adaptable.
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