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LLM agents are AI systems that can act on their own to achieve specific goals. In practice, an agent can break a user’s request into steps, use a knowledge base or APIs to get data, and then compose the final answer. This makes agents much more capable than standalone chatbots — they can automate complex workflows (e.g. booking a trip, generating reports, writing code) by coordinating multiple actions.
A useful analogy is that an LLM agent is like a digital assistant with access to plugins: it can reason with its internal knowledge and act on the world via tools. For example, a planning agent might decide which actions are needed, a memory module could track what’s already been done or learned, and tools (like database queries or APIs) supply real-time data. This modularity lets agents tackle tasks too complex for a single LLM pass, making them valuable for developers automating workflows or building “super” copilots.
Agent-to-Agent (A2A) and Multi-Component Prompting (MCP) are two complementary frameworks for building such agents. Conceptually, MCP is like a universal connector (or “USB-C port”) between an agent and external tools. In contrast, A2A is like a network cable linking multiple agents so they can collaborate. It provides an open protocol for agent-to-agent communication: agents advertise capabilities, send tasks to one another, and share outputs through a common interface. Both of them aim to extend plain LLMs, but at different scales and layers.
Let’s understand how both of them work and compare them.
Understanding Model Context Protocol
MCP is an open standard protocol (launched by Anthropic) that lets LLM-based applications access external data and tools in a uniform way. It breaks this interaction into three roles: a host (the LLM application interface), a client (an embedded connector), and one or more servers (tool providers). For example, a host app (like a chat UI or IDE) contains an MCP client that maintains a connection to external MCP servers. Each server implements one or more tools (functions, APIs or resource streams). When the LLM needs to take an action — say, query a database or call Slack — the client forwards that request to the appropriate MCP server, which executes the action and returns results.
The core idea is to abstract away the M×N integration problem. Before MCP, developers had to write custom code for each model-to-API link. With MCP, tools self-describe their inputs and outputs, so any MCP-compatible model can use them without glue code. In practice, the agent (LLM) receives a list of available tools or prompts/templates that guide when to use them. It can then invoke tools in a structured workflow:
Understanding → Planning → Verification → Refinement → Action
This resembles a chain-of-thought pipeline: the LLM plans a strategy, checks its reasoning, and executes final steps via tools. For example, a travel-planning agent using MCP might parse “Plan a week in Japan” and identify needed tools (flights API, hotel search). It then queries those APIs via MCP servers, checks consistency (e.g. dates align), adjusts as needed, and finally outputs a booked itinerary. In code, this might look like adding a “Google Flights” MCP server and a “Hotel Finder” MCP server; the agent’s prompts would include their interfaces so it can call them when needed.
Image Source: Salesforce Devops
What Is Agent-to-Agent Protocol?
A2A is a new open protocol (introduced by Google in 2025) for allowing multiple AI agents to find each other, talk, and work together. In an A2A system, each agent is an independent service with its own capabilities. Agents expose a network endpoint implementing A2A and a public “Agent Card” (JSON metadata at /.well-known/agent.json) describing their name, skills, endpoint URL, and auth info. When one agent needs something, it can discover another by fetching its Agent Card, then send it a “task” request via HTTP/JSON-RPC. The protocols and libraries handle security, long-running jobs, and complex data formats under the hood. Key A2A concepts include:
- Agent Card: A small JSON file that advertises an agent’s abilities (e.g. “can schedule meetings” or “analyze finances”) and endpoint. Agents periodically publish or register these cards so others can find them (e.g. via a directory or just knowing a URL).
- Client and Server Agents: In A2A terminology, a client agent initiates a task (the requester), and a remote/server agent performs it. For example, a “hiring agent” (client) might delegate to a “resume-search agent” (remote).
- Task Lifecycle: Communication happens through tasks, which are JSON objects with unique IDs. The client sends a task (via POST /tasks/send or subscribe) containing the user’s request. The remote agent then updates the task status as it works (“working”, “input-required”, etc.) and finally returns an artifact (the result). Servers can stream updates using Server-Sent Events (SSE), or push updates back to the client via webhooks.
- Messages and Parts: Tasks contain messages (like chat turns). Each message can include multiple parts with different content types (text, image, JSON data, etc.), allowing rich collaboration. For instance, a data-analysis agent might send back a chart as an ImagePart and a data table as a DataPart. Both client and remote negotiate supported formats, so agents can adapt to various UIs.
- Capability Discovery & Delegation: Agents can announce or search capabilities. For example, an orchestrator agent can scan available Agent Cards to pick one specialized in “contract analysis” or “tweet sentiment”. The chosen agent is then sent the task to handle that subproblem.
In practice, A2A enables multi-agent teamwork. For example, imagine automating a corporate event planning: one agent manages the overall plan, while it delegates venue booking to a “Venue Agent”, catering to a “Food Agent”, marketing to a “Social Media Agent”, etc. They communicate securely over HTTP: the plan agent formulates tasks (e.g. “Find a keynote speaker”), then sends those to specialist agents via A2A. Each agent internally may use its own LLM and tools, but externally they follow the A2A spec, so any A2A-compliant client can talk to them. This design is inherently modular: agents act like microservices. If one agent is slow or down, the coordinator can route tasks to backups or wait (the protocol supports long-running tasks and retries). A2A is built on standard tech (HTTP+JSON-RPC, SSE) for easy integration and is “secure by default” with OAuth/OpenAPI-style auth. It handles tasks ranging from quick queries to extended workflows (with humans in loop) Notably, it does not assume agents share memory or internal context; each agent is autonomous. All coordination is explicit: the protocol manages who has the conversation and what messages are exchanged.
Comparison: A2A vs MCP
| Aspect | MCP | A2A |
|---|---|---|
| Architecture | Single-agent (LLM-centric) with tool servers. Client–server: Host+Client (in app) connect to one or more MCP Servers for tools | Multi-agent (peer) network. Client and Remote roles: agents advertise via Agent Cards, then communicate over HTTP/JSON |
| Core purpose | Give one agent structured access to external data and APIs. Acts as a “context layer” to enrich an LLM’s environment | Enable inter-agent collaboration. Lets agents discover each other, delegate tasks, and share outputs as if on a common team |
| Data flow | Agent uses tools, resources, and prompt templates. It reads a list of tool descriptions and calls them as needed. | Agents exchange tasks, messages, artifacts, and parts. Each task has a lifecycle; messages carry content (text/data) as needed |
| Modularity | Modular at the tool level: each MCP server is a separate service. But the LLM agent and its reasoning remain one process. | Modular at the agent level: each agent can be separately developed, scaled, or replaced without touching others. New agents with new skills can join the ecosystem. |
| Maintainability | Requires maintaining one main agent codebase and any number of tool-server endpoints. Changing a tool’s interface means updating its server (though the protocol remains standard). | Agents are independent microservices. You can update or roll out a new agent without redeploying others. However, the coordination logic (often a top-level agent or orchestrator) can become more complex. |
| Performance/Scalability | Typically low-latency for tool calls if servers are local or well-provisioned (uses direct HTTP or SSE). Complexity grows with number of tools, but integration is standardized. Can suffer if context windows overflow or too many simultaneous calls. | Overhead of HTTP network calls between agents. Best suited for larger-scale workflows: you can scale by running many agent instances in parallel. Can handle long-running tasks (hours/days) with streaming updates. Relies on asynchronous communication, so throughput depends on network and agent availability. |
| Capabilities | Excels at structured reasoning: agents can break tasks into predictable steps and verify each. Transparent execution (each step logged), easy to audit. Well-suited for coding assistants, data lookup, or any task needing precise tool use. | Excels at diverse expertise: teams of agents each specialized (finance, NLP, vision, etc.) collaborate. Ideal for complex multi-domain tasks (enterprise workflows, multi-step business processes) where no single agent has all knowledge. Supports rich media (text, audio, video) negotiation. |
| Notable examples | Used in Claude Desktop and tools like Zed or Cursor AI to link to GitHub, calendars, etc. Great for single-agent scenarios (e.g. “an AI copilot that connects to all my databases”). | Emerging in cross-organization setups (e.g. a resume-search agent working with interview scheduler agents), complex business processes, and projects like Google’s Agentspace. Many contributors (Atlassian, Cohere, LangChain, etc.) are building A2A-enabled agents. |
In practice, MCP and A2A complement each other rather than compete. Google explicitly calls A2A “complementary” to Anthropic’s MCP. For example, one agent could use MCP internally (chaining multiple tool calls) and then hand off results to another agent via A2A. Or a specialized A2A agent might itself be implemented using MCP to manage its internal tool usage. The table above highlights this: MCP is about tools and data, A2A about agent orchestration.
On security and complexity, both protocols introduce new concerns. MCP’s open tool calls must be carefully sandboxed (prompt injection or tool poisoning are risks). A2A opens many endpoints between agents, so authentication (OAuth, API keys, etc.) and authorization (RBAC, tokens) are critical. But both are built with enterprise use in mind: A2A uses HTTP auth or OAuth2.0 by default, and MCP has been updated to support OAuth2.1 for servers.
Wrapping Up
So, where does this leave us? We’ve understood how MCP and A2A complement each other — one focusing on tool and data integration, the other on agent orchestration. We’ve also seen that while they expand what agents can do, they introduce new layers of complexity and security to manage. Now, the real question is:
- How might you combine these frameworks in your own systems?
- Could your next agent chain tools with MCP and then collaborate across services with A2A?
- And are your security models ready for this new, more open agent ecosystem?
As developers and architects, we’re no longer just building chatbots — we’re designing agentic systems that can plan, act, and coordinate. The building blocks are here. The next move is yours.
Kanwal Mehreen Kanwal is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.