You’re building an AI system designed to handle high volumes of tasks, from customer service to logistics. How should your agents work together? Should they act independently, pulling data and making decisions on their own, or should they communicate and collaborate with each other to get things done?
In fact, how AI agents “talk” to each other, or to data, can completely change how your system operates. But which approach works best for your business?
Take a moment to consider this:
In industries like finance, where data-driven decisions are key, is it better to let agents pull information and act independently?
Or, in complex workflows, is it more efficient for agents to collaborate and pass tasks between one another?
In the next sections, we’ll assess the core differences between Agent-to-Agent and Agent-to-Data communication, and how each approach can impact your AI system’s efficiency and scalability.
The Need for Standardized Communication in AI
As AI systems grow more complex, the need for standardized communication protocols becomes critical. Without them, AI systems may struggle to integrate, leading to inefficiencies that standardized solutions can address.
Additionally, robust security measures are essential to ensure that these protocols maintain data integrity and comply with privacy standards.
Challenges of Unstandardized Communication
- Data Incompatibility: Different AI systems may use different formats, making data exchange difficult.
- Complex Integrations: Connecting AI systems with diverse frameworks and configurations can cause delays and errors.
- Workflow Disruptions: Lack of standard communication can interrupt key business processes.
- Performance Testing: It is crucial to test configurations to evaluate the performance and scaling of agents within data connection environments. This includes determining the maximum parallelism and efficiency of agents when ingesting data, as well as monitoring for failures during the testing process.
For example, a customer service AI might struggle to pull up the latest product information if its system isn’t compatible with the inventory system.
Benefits of Standardized Protocols
By adopting standardized protocols, AI systems can:
- Facilitate Real-time Data Exchange: Ensures accurate, immediate access to information across systems.
- Streamline Integrations: Simplifies connecting AI systems without extensive customization.
- Enhance Scalability: New AI systems can be easily integrated with minimal adjustments.
For instance, adding a new AI chatbot to a customer service platform can be done with minimal effort if standardized communication protocols are in place.
Agent to Agent vs Agent to Data: A Quick Look
Before we understand the 3 protocols for standardized communication, quickly have a look at what exactly is agent to agent and agent to data.
Agent-to-Agent: An agent communicates or collaborates with other agents to complete a multi-step task. It’s about delegation, coordination, and passing context between agents.
However, traditional agent-based systems often require individual single agents for different system areas, which can complicate the setup and maintenance process.
Agent-to-Data: An agent connects directly to external data sources like APIs, databases, or documents. It retrieves, understands, and acts on data without needing other agents.
But which approach works best for your business, and how do they perform in real-world scenarios?
| Feature | Agent-to-Agent | Agent-to-Data |
|---|---|---|
| What it does | Talks to other agents | Connects directly to data |
| Use case | Multi-agent task coordination | Data lookup, retrieval, reasoning |
| Example | One agent delegates to another | Agent queries a database or API |
| Dependency | Relies on multiple agents working together | Needs data source access (e.g. API, DB) |
| Key challenge | Coordination and context sharing | Authentication, data parsing |
| Common in | Workflow automation, assistant chaining | Real-time dashboards, RAG, reporting |
The Three Protocols Shaping the Future
Over the past year, three agent protocols have emerged, each solving different parts of the communication puzzle and deploying innovative solutions.
These protocols help create comprehensive monitoring solutions by integrating different technologies for efficient data collection, analysis, and real-time monitoring.
1. MCP (Model Context Protocol) – From Anthropic
Focus: Agent-to-data
MCP is designed to help LLM-based agents connect with APIs and databases in a standardized way. It lets developers define external tools and send structured requests to those tools through a common interface, ensuring data can be securely accessed and updated.
Think of it as a wrapper that simplifies calling APIs via LLMs — with schemas, authorization, and tool descriptions baked in.
MCP isn’t trying to build an agent society. It’s focused on one thing: letting agents fetch or update external data securely and consistently.
To establish secure connections for data ingestion, it is crucial to specify a designated port along with the URL or IP. This requires administrative approval to ensure security in data transfer.
2. NANDA (Networked Agents for Decentralized AI) – From MIT
Focus: Agent-to-agent (decentralized)
NANDA picks up where MCP leaves off. It takes that structured access to tools and stretches it into a fully decentralized network of agents that can discover, communicate with, and verify each other without central control.
Multi-agent systems offer significant advantages in decentralized networks, such as improved task specificity, independent agent updates, and greater scalability.
However, they also present challenges like the complexity of integrating multiple agents and maintaining communication, which require careful planning and targeted agent responsibilities for enhanced performance.
It’s ambitious. NANDA wants to be the protocol backbone for the “Internet of AI Agents” , agents that don’t just use tools but collaborate across organizations and domains, handling specialized tasks with shared governance, verifiability, and traceability at the core.
3. A2A (Agent-to-Agent) – From Google
Focus: Agent-to-agent (enterprise-grade)
While NANDA builds for openness and decentralization, Google’s A2A takes a more enterprise-friendly route. It’s a spec for how agents can talk — securely, multimodally, and in real time. Agents using A2A can discover each other, send structured tasks, and respond with images, files, or JSON — not just text.
Where MCP is about calling APIs, and NANDA is about decentralized networks, A2A is about coordination at scale, for enterprises, research orgs, or teams building collaborative AI systems, enhancing their ability to manage complex workflows.
Evaluating the performance of these enterprise-grade AI agents is crucial. Performance metrics influence the reliability and success of AI systems, ensuring they achieve specific tasks effectively. Comprehensive evaluations and optimizations can significantly enhance agent performance.
What’s Actually Different?
Let’s simplify the split to determine which protocol best suits your needs:
Each protocol helps in identifying relevant data by integrating external knowledge and data, which enhances the accuracy and contextual understanding necessary for complex queries and workflows.
| MCP | NANDA | A2A | |
|---|---|---|---|
| Main Use Case | Agent ↔️ Tool/Data | Agent ↔️ Agent (peer-to-peer) | Agent ↔️ Agent (cross-org) |
| Real-time Support | Minimal | Emerging | Strong (SSE, async) |
| Governance | Centralized, app-led | Decentralized, multi-agent | Enterprise-first |
| Communication | Request/response | Encrypted, signed messaging | Multimodal messaging |
| Trust Model | User-mediated auth | Verifiable, cryptographic | OAuth2, mutual TLS |
| Agent Discovery | Manual/static | Decentralized registry | Agent cards + endpoints |
| Format | JSON-RPC | Built on MCP | JSON-RPC + web protocols |
Real-World Examples of Protocols in Action
Let’s see how MCP, A2A, and NANDA are already making an impact across different sectors, particularly in monitoring and data management.
These protocols help in improving service delivery for customers by leveraging advanced technologies to enhance customer experiences, track user engagement, and refine services based on feedback.
1. MCP in Data-Driven Industries: Finance and Healthcare
| Industry | Use Case | How MCP is Applied |
|---|---|---|
| Finance | Real-time financial insights | MCP connects agents to financial APIs for quick market data and revenue data analysis. Efficient processing systems ensure accurate data retrieval and improve performance. |
| Healthcare | Patient data management | MCP pulls patient data from EHRs and suggests treatments based on guidelines. |
2. A2A in Enterprise Environments: Large-Scale, Multi-Agent Workflows
| Industry | Use Case | How A2A is Applied |
|---|---|---|
| Supply Chain | Inventory and demand management | A2A enables agents to sync inventory with real-time demand. |
| Customer Service | Workflow coordination | A2A agents share customer data across teams for seamless support, streamlining the customer service process. Categorizing these agents into specific domains, such as marketing or supply, allows for more effective querying and analysis, enhancing overall decision-making processes. |
3. NANDA in Open-Source and Research-Driven Projects: Decentralized Systems
| Sector | Use Case | How NANDA is Applied |
|---|---|---|
| Smart Cities | Traffic and waste management | NANDA enables peer-to-peer communication between agents to optimize city operations. |
| Research | Cross-domain collaboration | NANDA allows agents to share and verify research data securely, creating a clear path for cross-domain collaboration. Incorporating server information into monitoring strategies is crucial for facilitating swift troubleshooting and data analysis, enhancing overall infrastructure management by accessing server performance data alongside other system components. |
What to Watch
If you’re a developer, expect to start picking a protocol depending on what you’re building and how users will interact with it:
- Use MCP to connect agents with tools.
- Use A2A if you want structured, multi-agent workflows.
- Use NANDA if you’re building for open, cross-domain collaboration.
Advanced AI models, such as the Claude 3.5 Sonnet, enhance the functionalities of Cortex Agents by showcasing their capability to perform complex tasks like planning and orchestrating AI-driven workflows, as well as utilizing multimodal inputs to enrich data processing and analysis in enterprise applications.
If you’re a platform builder, the real question is: Will you support one of these specs, or try to invent your own?
And if you’re a researcher or startup founder, this is a rare moment where standards are still fluid. The choices made now could shape how agents interact for the next decade.
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