Introduction: Evolving AI Workflows with A2A and MCP

Generative AI-powered products — like workflow automation tools and AI copilots — are evolving from single monolithic bots into multi-agent systems that collaborate on complex tasks. Two emerging architectures are making this possible: Agent-to-Agent (A2A) and Model Context Protocol (MCP). In simple terms, A2A is an open protocol for AI agents to talk to each other, while MCP is an open standard for connecting AI models to the tools and data they need. Together, these architectures promise to simplify development and scale AI workflows to enterprise levels. Before diving in, let's briefly define each:

• Agent-to-Agent (A2A) Protocol: A specification (open-sourced by Google in 2025) that standardises how autonomous AI agents discover, communicate, and coordinate with one another. It provides common methods (HTTP + JSON-RPC, server-sent events, etc.) for agents to exchange messages and tasks securely across different frameworks or vendors. Think of A2A as the "communication bus" that lets multiple AI agents collaborate in a workflow.
• Model Context Protocol (MCP): An open standard (introduced by Anthropic) that acts like the "USB-C port for AI applications" — a universal connector between AI models and external tools or data sources​. MCP defines how an AI agent (or large language model) can securely interface with databases, APIs, knowledge bases, and other software, injecting those capabilities or context into the model's runtime​. In essence, MCP lets an AI agent gain tools and information on-the-fly, without hard-coding every integration.

In the rest of this post, we'll explore the development challenges these architectures address, how A2A enables modular, reusable agent collaboration, and how MCP coordinates multiple models and tools with reliability and traceability. We'll also illustrate how A2A and MCP work together to power scalable AI copilots and workflows across many use cases.

Challenges in Building Scalable AI Workflows and Copilots

Building a robust AI workflow or copilot that scales is non-trivial. Developers often face several challenges:

• Integration Complexity: Without a standard, connecting each AI model to each tool or data source becomes a combinatorial nightmare. Every new tool may require custom code or prompts for each model, leading to an "M×N" integration problem​. This fragmentation makes it hard to add features or swap models.
• Multi-Agent Orchestration: As tasks grow complex, a single model is rarely enough. You might need one agent for retrieving data, another for reasoning or validation, etc. However, without a structured communication model between agents, the system can become "fragile, opaque, and difficult to scale"​. Ad-hoc agent communication (e.g. via bespoke APIs or shared memory) tends to break down as you add more agents or increase concurrency.
• Reuse and Modularity: In traditional setups, AI capabilities are often siloed. If you build a customer-support bot and a document-analysis bot, each might duplicate similar logic (e.g. a database lookup or an email sender) because there's no easy way for them to share agents or tools. This lack of modularity slows development and makes scaling to new use cases costly.
• Reliability and Traceability: Orchestrating AI decisions and external actions needs oversight. Without a clear framework, tracking which agent did what, or debugging failures, is hard. Logging and debugging across several AI components can turn into a maze of disparate logs. Ensuring security (e.g. an agent only accesses permitted tools) is another concern without standardised protocols.
• Performance Bottlenecks: Naively scaling a multi-agent system can lead to inefficiencies. For example, if every agent must individually integrate with every tool (without a hub like MCP), you duplicate work and connections. Conversely, if one big agent does everything, it may become a bottleneck. Developers struggle to balance workload across agents and ensure the system remains responsive as it scales to more tasks or users.

In summary, without A2A and MCP, developers either build monolithic AI systems that are hard to maintain, or brittle multi-agent hacks that are hard to scale. Now, let's see how A2A and MCP address these issues.

A2A: Agent-to-Agent Collaboration Made Scalable

Agent-to-Agent (A2A) is all about horizontal integration: it enables multiple AI agents to work together on a task by communicating in a standardised way​. Rather than one giant model trying to do everything, you can have specialised agents (for reasoning, retrieving info, executing actions, etc.) that coordinate their efforts. Here's how A2A helps developers:

• Standard Communication Protocol: A2A defines a common language for agents, using web-friendly standards (HTTP(S) + JSON-RPC 2.0) for requests and Server-Sent Events (SSE) or push notifications for asynchronous updates​. This means an agent developed in one framework can call another agent written in a different language or hosted elsewhere, as long as both follow A2A. The protocol handles the formatting of messages, task status updates, error codes, etc., so you don't have to reinvent those wheels.
• Agent Discovery via Agent Cards: To avoid hard-coding endpoints, A2A introduces Agent Cards — basically a manifest each agent hosts (e.g. at a URL) describing its name, capabilities ("skills"), API endpoint, and authentication requirements​. This is like service discovery: an agent can query a directory or known URLs to find another agent that can, say, "summarise text" or "translate French". For developers, this fosters a plug-and-play approach to composing agents. You can publish new agents and have existing ones discover and use them without manual wiring.
• Task-Based Collaboration: In A2A, interactions are organised around tasks. One agent (the "client") sends a task request to another agent (the "server") to perform some action. The protocol specifies a clear lifecycle for tasks — e.g. submitted, in-progress, completed, failed — making complex workflows easier to manage​. Agents exchange messages containing rich data (text, JSON payloads, even binary artifacts) as part of these tasks, so they can share results or ask for more info. This structured task handling relieves developers from writing a lot of boilerplate coordination logic; you get a built-in way to track multi-step workflows across agents.
• Modularity and Reuse: Each agent in an A2A system can be a self-contained microservice (with its own internal model, database, etc.) that exposes certain skills. Need a new capability? Spin up a new agent for it, and register its card. Because A2A agents don't reveal how they work internally (they are black boxes behind an API), you're free to implement or update each one independently — use different LLMs, fine-tuned models, or prompt chains as appropriate. This encapsulation means developers can reuse agents across different products. For example, the same "PDF summariser" agent might be used by a legal copilot and a customer support workflow, without each team duplicating that logic.
• Scalability and Performance: With A2A, workloads can be distributed. Multiple agents can run in parallel, handling different subtasks concurrently — much like microservices architecture​. If a particular agent becomes a bottleneck (e.g. an OCR agent when many documents come in), you can scale it out (run more instances) without affecting other components, as long as they can find an available instance via the agent discovery mechanism. A2A's design also supports long-running tasks through async patterns (the agent can keep the client posted via SSE or send a callback when done)​. This allows agents to tackle heavy jobs (say, an agent doing an hour-long data migration) without blocking others, improving overall throughput.
• Security and Governance: Designed with enterprise use in mind, A2A integrates authentication and authorisation into the protocol (agents can require auth tokens, validate permissions). All agent-to-agent calls can be logged and audited in structured form. From a developer perspective, this is a boon for traceability: you can trace a user request as it hops between agents, because each task and message is well-defined and can be recorded. This level of traceability and control is hard to achieve with ad-hoc integrations.

In short, A2A turns a collection of AI agents into a cohesive team. It fosters modularity (build small, focused agents), reusability (use them in many workflows), and scalability (run more or less of each as needed, handle tasks in parallel). However, each of those agents still needs to actually do its job, often by interfacing with external data or services. This is where MCP comes in.

MCP: Tool and Context Integration for AI Agents

If A2A is the horizontal layer connecting agents to each other, MCP (Model Context Protocol) is the vertical layer connecting an agent to its operating environment. MCP provides a standardised way for AI models to access tools, databases, and other context reliably and securely — essentially acting as a Model Coordination Platform for tool use. Key aspects of MCP include:

• Standardized Tool API: MCP turns disparate tools into a common format. An MCP server is essentially an adapter that exposes some capability (say, "database queries" or "send an email" or "search documents") over a JSON-RPC API​. An AI agent, acting as an MCP client, can connect to any MCP server and invoke its tools with a simple RPC call, rather than custom-coding HTTP requests or prompt engineering for each tool. For example, with MCP an agent can list available tools and invoke them by name with arguments, as shown below:

# Example: Listing and calling tools via MCP (simplified)
mcp_server_url = "https://mcp-server.company.com/jsonrpc"

# Discover what tools are offered by the server
discover_payload = {"jsonrpc":"2.0", "id":1, "method":"tools/list", "params":{}}
tools_catalog = requests.post(mcp_server_url, json=discover_payload).json()
print("Tools available:", tools_catalog)

# Call a specific tool (e.g., get recent GitHub commits) by name
call_payload = {
  "jsonrpc":"2.0", "id":2, "method":"tools/call",
  "params": { "name": "github_recent_commits", "arguments": {"repo": "myorg/repo"} }
}
result = requests.post(mcp_server_url, json=call_payload).json()
print("Tool result:", result)

Pseudocode: In this snippet (based on Anthropic's MCP spec), the agent discovers tools and then calls a github_recent_commits tool via the MCP server​. Under the hood, the MCP server knows how to talk to GitHub's API. The agent itself remains tool-agnostic, just calling tools/call with a JSON payload. This illustrates how MCP provides plug-and-play tool integration, saving developers from writing custom integration code for each new external service.

• Dynamic Context Injection: One major benefit of MCP is the ability to provide runtime context to the model in a structured way. Rather than stuffing a document's text into a prompt, an MCP client can ask a tool for the relevant data when needed. For instance, an agent could use MCP to fetch a customer's record from a database or pull the latest policy document from SharePoint during an AI chat session. This means developers can easily give the model up-to-date information or expanded capabilities on demand, without retraining or prompt hacking. Anthropic's design allows tools, documents, functions — essentially any external context — to be "injected" into the model's reasoning process via standard calls​.
• Secure Two-Way Connectivity: MCP is built with security and enterprise needs in mind. Connections between the AI (client) and tools (server) are two-way and secure​. The AI can request data or actions, and tools can return structured results. Authentication can be handled at the MCP layer, ensuring the model only accesses what it's allowed to. All interactions happen through a controlled interface (the MCP server), which can sanitize inputs, enforce policies, and log all requests. This is crucial for traceability: companies can audit what queries were made by the AI, what tool responses came back, etc. MCP's use of JSON-RPC yields explicit logs of every tool call​, aiding debugging and compliance.
• Ecosystem and Reusability: By standardising the interface, MCP enables an ecosystem of pre-built connectors. Early adopters have already created MCP servers for services like Google Drive, Slack, GitHub, databases, etc. For developers, this means you can reuse community-built MCP integrations or quickly build your own, then use them across many AI applications. It's analogous to device drivers in an OS: as long as a "driver" (MCP server) exists for a tool, any AI agent can plug into it. This drastically lowers the effort to scale an AI copilot to support new systems — no need to write custom code for each team's particular CRM or file system, if an MCP server is available.
• Central Orchestration and Memory: While MCP is primarily about connecting to tools, its structured approach naturally allows a form of centralised orchestration. In many MCP-based designs, there is an orchestrator or main agent that maintains a shared context or memory of the task at hand​. Every step (tool used, result obtained) can be recorded in this context, which can then inform subsequent model prompts or decisions. This is a contrast to pure A2A systems where each agent only knows its local state. The benefit is improved overall reasoning and consistency — the orchestrator can see the "big picture" and decide the next action (with an LLM's planning abilities). In essence, MCP's structured context passing can serve as the "brain" coordinating the tools, leading to more reliable outcomes (at the cost of a bit more initial design work to set up the schemas and flows​).

To sum up, MCP gives each agent a rich toolbox and memory to work with. It standardises external integrations, ensuring reliability and security, and provides the scaffolding for traceable, context-rich AI workflows. But MCP alone doesn't handle multiple agents working together — that's why we combine it with A2A.

Combining A2A and MCP for Powerful AI Systems

A2A and MCP address different layers of the stack, and together they enable scalable multi-agent copilot architectures. Google and Anthropic explicitly position them as complementary: "MCP handles vertical integration — how agents access tools and context. A2A manages horizontal integration — how agents communicate with each other."​ In practical terms, we use MCP to empower each individual agent, and A2A to let those agents coordinate as a team. This combination yields an architecture that is both modular and orchestrated:

• Horizontal + Vertical Integration: With A2A connecting agents and MCP connecting tools, we get the best of both worlds​. Agents can delegate subtasks to other agents (via A2A) and leverage external data or functions (via MCP) as needed. For example, one agent can act as the "planner" and use A2A to call a "worker" agent. Each of those agents may use MCP to perform its piece of the job. The system's complexity is manageable because each link (agent↔agent or agent↔tool) follows a known protocol. This layered design also improves reliability: if one agent fails or is too busy, another with similar capabilities could be swapped in (since they speak the same A2A protocol and advertise similar skills). Likewise, if one data source is down, the MCP layer can potentially reroute to a backup, without the agent logic changing.
• Traceability and Governance: In a combined A2A+MCP architecture, every significant action is logged in a structured way. A2A task exchanges can be logged by an orchestrator or the agents themselves. MCP tool calls are logged by the MCP servers. This means a developer or ops team can trace a user's request as it flows through various agents and tools — crucial for debugging and for compliance in sensitive domains. If an error occurs, standardized error messages propagate via A2A tasks or MCP responses, making it easier to pinpoint which component failed. Importantly, this setup also eases auditing: e.g., you can answer "which external data did the AI access when generating that report?" by checking the MCP call logs​. Such traceability is a strong selling point for deploying AI copilots in enterprise environments.
• Performance and Scaling: By decoupling concerns, A2A+MCP architectures allow each part to scale independently. Suppose you have a copilot serving many users — you might run multiple instances of each agent type behind a load balancer. Thanks to A2A, the "client" agents will dynamically discover an available "server" agent instance to handle each task. And thanks to MCP, each agent instance efficiently taps into the shared resources (tools/data) it needs rather than carrying that load itself. Work can be parallelised: one agent can fetch data while another processes a previous result. Moreover, the development team structure can mirror the architecture — different teams can own different agents or MCP connectors, updating them without affecting the others as long as they adhere to the protocol contracts.

To concretise how A2A and MCP operate together, let's walk through an example use case.

Example Use Case: Automated Report Generation (Multi-Agent Workflow)

Consider a workflow automation scenario in a company: a manager asks an AI assistant, "What were our sales figures last quarter, and can you email me a summary with top client highlights?" This task can be broken into sub-tasks and handled by a coalition of agents, as follows​:

1. Orchestration: A Manager Agent (the orchestrator) receives the request. This agent doesn't do the heavy lifting itself; it plans the steps and coordinates others via A2A.
2. Data Retrieval: The Manager Agent sends an A2A task to the Database Agent requesting last quarter's sales data​. The Database Agent is specialised in data fetching; it uses MCP under the hood (e.g., connecting to a SQL database through an MCP server) to run the query​. Once the data is retrieved, the Database Agent returns the results to the Manager Agent through the A2A channel.
3. Report Generation: Next, the Manager Agent delegates the reporting task. It sends the sales data (perhaps sanitised or summarised) to a Report Generator Agent via A2A. The Report Generator Agent might leverage an MCP-accessible tool (say a charting library or a document template service) to create a nicely formatted PDF report from the data​. It then responds with the report file (as an artifact) to the Manager Agent.
4. Delivery: Finally, the Manager Agent uses A2A to ask an Email Agent to send out the report​. The Email Agent in turn uses MCP to interface with an email server or API (it has a tool for sending emails). Once the email is sent, it confirms completion back to the Manager Agent.
5. Completion: The Manager Agent confirms to the human manager that the task is done, perhaps with a summary message.

This architecture, illustrated by the steps above, showcases A2A and MCP in tandem: MCP serves as the backbone for each agent's individual tool usage, while A2A handles the inter-agent communication to weave those pieces together into one workflow​. From a developer's perspective, each agent can be developed and tested in isolation (e.g. ensure Database Agent correctly fetches data via its MCP interface, ensure Report Agent correctly generates a report given data). A2A then cleanly stitches these components. If a company needs to scale this to more managers or different reports, they can deploy more instances of these agents, or add new agents (for new data sources or new delivery methods) without redesigning the whole system.

Other Examples at a Glance

The multi-agent pattern above can be adapted to many AI copilot scenarios. Here are a few brief examples of how A2A and MCP enable scalable solutions:

• Document Processing Pipeline: Imagine a copilot that ingests documents and answers questions about them. You could have an OCR Agent (to extract text from scans) using an MCP tool for a vision API, a QA Agent (LLM that finds answers) using an MCP connector to a vector database of embeddings, and a Summary Agent for generating summaries. An orchestrator agent coordinates them via A2A: first OCR the document, then index or query it, then summarize or answer the user. Each agent is reusable — the OCR Agent could be the same one used in other workflows, and the QA Agent might be a general Q&A module pointed at different corpora via MCP. As new document types or ML models emerge, you can slot them in as new agents or tools.
• Customer Support Copilot: In a customer support scenario, an AI assistant might need to pull information from various systems. Using MCP, an agent can access the ticket database, customer purchase history, or knowledge base articles securely. Using A2A, you might divide responsibilities between agents: e.g., a Retrieval Agent that gathers relevant info (by querying knowledge base via MCP), a Conversation Agent that uses an LLM to draft a response, and a Approval Agent that applies business rules or even asks a human supervisor if needed. These agents coordinate to ensure the final answer is accurate and compliant. Thanks to the protocols, this system can scale across many support teams — each team could deploy its own set of agents configured to their domain, all following the same A2A/MCP standards (ensuring consistency and easier maintenance).
• AI Coding Copilot (Development Tools): AI copilots for coding often need to use many tools: reading documentation, accessing a code repository, running test cases, etc. MCP provides a way to hook into IDEs, version control, and testing frameworks. One can design multiple agents: e.g., Documentation Agent (uses MCP to fetch API docs or Stack Overflow answers), Code Gen Agent (an LLM that writes code with the context provided), Test Agent (runs code in a sandbox via MCP connectors). Using A2A, these agents collaborate — the Code Gen Agent might call the Test Agent to verify its output, or the Orchestrator agent calls the Doc Agent and passes relevant info to the Code Gen Agent. This modular approach means each capability (doc retrieval, code execution) is encapsulated, making the copilot more reliable and easier to update (swap out how documentation is retrieved without touching the code generation logic, for example).

These examples highlight a common theme: developers can mix and match agents and tools like building blocks. A2A and MCP serve as the standard interfaces between these blocks, so you can assemble different workflows relatively easily. This is a huge improvement over monolithic AI systems where every new feature might require training a bigger model or engineering a single complex prompt. Instead, you compose smaller pieces, which is more tractable and scalable.

Scaling Copilots Across Teams and Organizations

One of the biggest advantages of adopting A2A and MCP architectures is the ease of scaling solutions across many customers or internal teams. Because these protocols enforce clear contracts and modularisation, organisations can avoid reinventing the wheel for each use case:

• Reuse Across Domains: If an enterprise has multiple copilot applications (say one for finance and one for HR), they can share common agents or MCP connectors. For instance, a well-tested "calendar scheduling" agent (that uses MCP to interface with Calendar APIs) could be reused in both a sales assistant and an HR assistant. A2A makes this possible by allowing the HR orchestrator agent to simply call the calendar agent as a service. Without A2A, teams might have built or prompted an LLM for scheduling separately in each app. Thus, A2A/MCP cut down duplicate development and propagate best-in-class capabilities across the organization.
• Team Autonomy with Governance: Different teams can develop agents in parallel — perhaps the IT team builds an "IT Support Agent" and the legal team builds a "Contract Analysis Agent". Thanks to the standardised protocols, these agents could later interoperate (if needed) or at least co-exist on the same platform. Each team can use the MCP to plug their agent into the data sources they control, and use A2A to expose some skills to others. Meanwhile, the organization's central platform team can monitor all agent interactions uniformly (since A2A and MCP interactions are logged and governed). This balances autonomy with central governance. It's akin to how microservice architectures let teams own their service, while the platform provides API gateways, monitoring, and security — here A2A/MCP provide the common fabric for AI services.
• Multi-Tenancy and Customisation: For AI products offered to many customers (SaaS style), A2A and MCP aid in scaling out. You might run a separate instance of certain agents per customer (for data isolation), but still maintain one codebase. Because communication is standardized, a central orchestrator could even route tasks to the correct customer-specific agent. MCP servers can be deployed per client environment to connect to their databases, etc., without changing the agent logic that calls them. This means your AI copilot framework scales not only in volume but also across varied environments with minimal friction. Each customer's unique tool stack can be integrated via implementing a few MCP connectors rather than rewriting the AI's core reasoning.
• Improved Reliability and Maintainability: Scaling isn't just about throughput — it's also about keeping the system reliable as it grows. A2A's decoupling and MCP's structured interfacing both help in isolating issues. If a particular tool integration fails frequently, you can update that MCP server without touching the agent. If a new better model comes along for a task, you can spin up a new agent with it and gradually shift A2A traffic there. This component-wise evolution makes it feasible to scale the quality of the system over time (continuous improvement) without big bang rewrites. And if regulations or policies change (say, an agent now needs to log certain decisions for compliance), you can introduce that in a centralised way (perhaps in the orchestrator or as a monitoring agent) thanks to the uniform protocols, ensuring traceability and compliance at scale.

In summary, A2A and MCP architectures make it dramatically easier to scale AI copilots across an enterprise or user base because they enforce a clean separation of concerns. Agents can be added or updated like Lego bricks, tools can be plugged in or swapped through a standard port, and the whole system can be scaled out horizontally. This lets AI engineering teams focus on building new capabilities rather than writing glue code for each expansion.

Conclusion

The emergence of A2A and MCP marks a pivotal shift in how we develop generative AI products. Much like web developers benefited from standard protocols (HTTP, REST) and modular services, AI developers now have a pathway to build interoperable, scalable agent ecosystems instead of isolated bots. A2A empowers agents to collaborate in a modular, microservice-like fashion, and MCP equips those agents with the tools and data access they need in a consistent way. By simplifying integration and coordination, these architectures let us concentrate on higher-level logic and user experience, confident that the underlying agents can talk to each other and to external systems reliably.

For AI engineers and product developers, adopting A2A and MCP can accelerate development and reduce maintenance headaches. Need to add a new feature? Develop a new agent or connector and plug it in. Need to scale to more users? Deploy more instances and let the protocols handle the coordination. The result is AI copilots and workflows that are easier to build, easier to scale, and easier to trust. As the industry coalesces around these standards (with broad support from companies like Google, Anthropic, Salesforce, etc.​), we can expect a flourishing ecosystem of agents and tools — a toolkit for the next generation of AI-powered products.

In this new paradigm, building a generative AI workflow is less about wrestling with one giant model and more about orchestrating an intelligent ensemble. A2A and MCP provide the blueprint to do just that, unlocking a future where AI agents seamlessly collaborate to tackle tasks of any complexity, at any scale.