Introduction
When you think about achieving a business goal in an enterprise, it’s rarely the work of one person. There’s usually a team each member with a specific responsibility. One handles planning, another manages deployment, and someone else oversees support or communication.Together, they deliver success whether it’s resolving a ServiceNow ticket or deploying code to production.
Now, imagine bringing that same teamwork concept to the world of Agentic AI. That’s exactly what AWS Strands Multi-Agent Patterns enable a framework where multiple specialized AI agents collaborate like a well-orchestrated enterprise team, each playing its role with focus, context, and precision.
From Enterprise Teams to Agentic Collaboration
Just like humans in a project team, AI agents in a Strands-based system work together toward a shared goal. Each agent has a clearly defined purpose one might retrieve information, another might analyze or reason over it, and another might summarize or execute the final action.
This mirrors the S—Single Responsibility Principle from the SOLID design patterns: every agent does one job and does it well. And just as professionals depend on tools to get their work done, agents rely on APIs, databases, and search systems to gather the context they need. That’s where the Model Context Protocol (MCP) comes into play.
MCP: The Language of Tools in AWS Strands
MCP (Model Context Protocol) is the backbone that enables AWS Strands Multi-Agent Patterns to talk to the outside world.
It defines how agents discover, connect, and exchange context with external tools in a consistent, standardized way avoiding the chaos of custom integration code.
Key features of MCP include:
Capability Discovery–Agents can ask a tool what operations or data it supports.
Context Sharing–Tools can provide structured context back to the agent for reasoning.
Streaming Responses – Agents can consume partial outputs in real time for faster insights.
Flexible Connectivity–MCP supports both STDIO and Streamable HTTP, making it usable across local environments or distributed systems.
In short, MCP provides a common language between agents and their tools ensuring communication is predictable, modular, and scalable.
A2A: Agent-to-Agent Invocation-The Heart of Collaboration
In enterprise teams, one specialist often delegates a subtask to another the planner calls the analyst, who calls the translator, and so on.
In AWS Strands Multi-Agent Patterns, this same dynamic is made possible through A2A (Agent-to-Agent Invocation).
With A2A, an agent can invoke another agent as if it were a callable tool.
For example:
A Planner Agent might call a Retriever Agent to collect domain-specific data.
A Summarizer Agent might invoke a Translator Agent to localize content.
This pattern promotes modularity and reuse each agent is independently designed, deployed, and maintained, but can still collaborate as part of a larger workflow. It’s like a team of microservices, but with reasoning power.
ACP: The Industry’s Broader Communication Protocol
While AWS Strands focuses on MCP and A2A internally, the wider AI ecosystem has also been developing a complementary initiative — the Agent Communication Protocol (ACP).
ACP is a REST-based standard that enables inter-agent communication across different platforms and vendors.
It’s designed for universal interoperability, supporting rich metadata, asynchronous messaging, and content negotiation across frameworks.
In other words, while A2A is the local teamwork protocol within Strands, ACP is the global handshake that connects agents beyond AWS boundaries.
A2A + ACP: The Path Toward Unified Agent Communication
Here’s where the ecosystem gets exciting.
Both the ACP and A2A working groups have announced plans to merge efforts and co-develop a unified standard — blending the strengths of both approaches.
The idea is to take the REST-based robustness of ACP and combine it with the lightweight, JSON-RPC-based simplicity of A2A.
The result: a single, flexible communication layer that allows any compliant agent to discover, message, and collaborate with others regardless of the platform they were built on.
This upcoming ACP + A2A merged framework will enable:
Cross-platform discovery – Agents can find and describe each other’s capabilities dynamically.
Secure, structured messaging – Using consistent schemas and metadata across systems.
Asynchronous delegation – One agent can assign work and receive updates in real time.
Seamless interoperability – AWS Strands agents will be able to communicate with ACP-compliant agents from other ecosystems without adapters.
In essence, this evolution moves us closer to a universal agent communication layer — much like how HTTP unified the web.
From Software Factory to Agent Factory
For decades, enterprises have perfected the Software Factory model — where teams build applications through well-defined pipelines, reusable components, and CI/CD automation. That model gave us consistency, scalability, and reliability in delivering code.
Now, with the rise of AWS Strands Multi-Agent Patterns, we’re entering the next chapter: the Agent Factory era.
In an Agent Factory:
We don’t just deploy applications we deploy intelligent agents with purpose and context.
Each agent can be configured, reused, or combined with others to form an adaptive, goal-driven system.
Just like software modules in a CI/CD pipeline, agents can be assembled dynamically to respond to business goals, user intents, or live data streams.
The same principles that powered the Software Factory — modularity, automation, and collaboration — now power Agentic AI ecosystems.
Only this time, instead of compiling code, we’re orchestrating intelligence.
The Big Picture: From Single Agents to Enterprise-Scale Intelligence
With MCP handling the agent-to-tool communication, and A2A / ACP **enabling **agent-to-agent collaboration, AWS Strands Multi-Agent Patterns provide a blueprint for building intelligent, enterprise-grade AI ecosystems.
Instead of building one giant monolithic “super agent,” organizations can design teams of smaller, specialized agents each with clear roles, reusable logic, and standardized interfaces.
Just as enterprise teams thrive through collaboration and clear communication, agent ecosystems will thrive through protocols like MCP, A2A, and ACP. This is how the next generation of AI systems will scale — not by adding size, but by adding structure and synergy.
Multi-Agent Hiring process Architecture
Multi-Agent Hiring Architecture - Human Explanation
Imagine you're an HR manager who needs to hire someone, but instead of doing all the work yourself, you have a team of AI assistants that work together like a well-oiled machine. Here's how it works: You start by telling your Supervisor Agent (the team leader) what kind of person you need to hire. The Supervisor then coordinates everything by assigning specific tasks to different specialist agents the Screening Agent reviews all the job applications and filters out the best candidates, the Data Agent gathers detailed information about each candidate from databases and LinkedIn, and the Interview Agent conducts video interviews using Microsoft Teams. As these agents work, they constantly share information with each other using the A2A protocol (like team members talking to each other), while also using company tools like Outlook for emails and Calendar for scheduling through the MCP protocol (like using office equipment). The Analysis Agent then evaluates all the collected data to compare candidates, and finally the Decision Agent makes the final hiring choice. If they decide to hire someone, they send an offer letter via email; if not, they send a polite rejection. The whole process is automated, fast, and ensures every candidate gets fair treatment while you, the HR manager, just need to give the initial instructions and wait for the final result.
If you want to know more about A2A, please refer my blog
Final Thoughts
The enterprise world runs on collaboration humans, tools, and processes working together in harmony. Now, with AWS Strands Multi-Agent Patterns, we can replicate that same model in the digital realm.
Agents that plan, retrieve, reason, summarize, and execute — all communicating through open protocols mark the beginning of truly composable intelligence And as ACP and A2A converge, the future promises a seamless, cross-platform agent ecosystem where collaboration is not just possible, but effortless.
Welcome to the Agent Factory Era where enterprise intelligence is built, deployed, and scaled through teams of AI agents, just as the Software Factory once did for applications.
In Part-II we will deep dive into multi-agent patterns Swarm , Graph , Workflow and Agents as tools
Thanks
Sreeni Ramadorai
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