The silent AI revolution: how MCP and A2A will change your business (and your work)

De stille AI-revolutie: hoe MCP en A2A je bedrijf (en je werk) gaan veranderen

While everyone is busy experimenting with AI chatbots, something much bigger is happening behind the scenes: a global network of AI agents that are independently teaming up. And the two secret ingredients that make all this possible? MCP (Model Context Protocol) and A2A (Agent-to-Agent Protocol).

Sound technical? It’s not as daunting as it seems, but the impact is huge. This is what the AI of tomorrow enables. Think: AIs that distribute tasks, manage systems, negotiate with each other, and make your work smarter.

We’ll explain what MCP and A2A do, but more importantly: how you can start engaging with them today.

In short:

  • Think of MCP as the universal socket where AIs can plug in
  • A2A is the language that AI agents use to communicate with each other. So, no single AI tool needs to know everything, it just calls in a specialist agent instead
  • Together, MCP and A2A enable autonomous collaboration between AI systems
  • We’ll show you 10 use cases that can be applied across any industry and organization
  • Start experimenting now and build your own mini-agents.

Why this is needed: AIs that don't speak each other's language

AI is still too much of an island. Your chatbot understands your customer, but doesn’t know what’s in the CRM. Your planner understands your calendar, but not your project progress. Each model has its own language and isn’t really collaborating. So you have smart systems, but no smart system.

Imagine a super talented team where the designer only speaks Swahili, the marketer speaks only Icelandic, and the programmer communicates only in code. The knowledge is there, but communication breaks down. This is exactly the issue AIs are facing now, and it limits everything you can do with them.

That changes with MCP and A2A. They are the bridge between models, tools, and agents. Two protocols that enable AI systems to:

  • Communicate reliably with external tools (MCP)
  • And collaborate autonomously with other AIs (A2A)

MCP: the universal socket for AIs

MCP (Model Context Protocol) ensures that AIs can securely and systematically manage external tools. It facilitates communication between an AI model and an external tool. Think of:

  • An AI that automatically retrieves data from your accounting software
  • An AI that generates tickets in your CRM
  • An AI that manages a dashboard without complicated integrations

Why this is significant: With MCP, an AI system no longer needs to be specifically trained for one tool. Once your systems are MCP-ready, they can work with any AI that understands this protocol.

Think of it like USB: in the past, each device had its own connection, but now you can plug everything into one port. MCP is that universal port, but for AIs.


A2A: the collaboration language for AI agents

A2A (Agent-to-Agent Protocol) enables AIs to communicate with each other independently, exchange information, and tackle complex tasks together. Your AI doesn’t need to do everything by itself – it just engages the best specialist-agent as needed. Think of:

  • An AI responsible for content creation that automatically calls in a data analysis agent
  • An AI that forwards a customer query to another AI with domain knowledge
  • AIs that handle an audit, planning, or support process together without human coordination

Important because: You don’t need to build an all-rounder AI. Let specialized agents emerge – just like a team of experts – and use A2A as their common language.


Practical examples for your organization

What once seemed impossible is now feasible. Thanks to MCP and A2A, you can have AI agents work together with your systems and with each other. And surprisingly, it works really well without rocket science.

These 10 use cases demonstrate how you can take steps towards a smarter organization today using existing AI models, APIs, and tools.

1. Intelligent onboarding buddy for HR

Let’s say a new colleague starts. The HR onboarding agent is automatically activated, and:

  • Through MCP, it automatically creates accounts (HR tools, Slack, email)
  • Orders hardware like a laptop, mouse, and charger through the IT portal
  • Reserves a workspace
  • Monitors the activity of the new colleague and notices that there's a lot of searching in the knowledge base for ‘submitting claims’
  • Via A2A, it engages an internal knowledge base agent to create a personalized explanation video about the claims process

Result: A super smooth and personalized onboarding experience. The new colleague feels supported right away, and the HR team saves hours on repetitive tasks.


2. Hyper-personalized B2B sales agent

Your sales team wants to find and approach the 10 most promising leads in the manufacturing industry. Through MCP, a sales agent plugs into the CRM system, LinkedIn Sales Navigator, and news feeds. Here, it identifies companies that fit the customer profile and specifically detects 'triggers' like a recent investment round or the announcement of a new production line.

Once a promising lead is found, it activates an email personalization agent via A2A. The task: "Write a unique, hyper-personalized email that plays into [trigger] and the role of [contact person]."

Result: Your sales team starts the day with 10 perfectly qualified leads and a ready-to-go, powerful opening email that yields a much higher response rate.


3. Self-optimizing marketing campaign

Your marketing department launches a new online advertising campaign. The campaign agent monitors performance in real-time. Through MCP, it has direct access to the APIs of Google Ads, LinkedIn Ads, and other advertising platforms.

The agent notices that the click-through rate on LinkedIn is lagging while an ad on a specific tech blog is performing exceptionally well. The cost per click on LinkedIn is unnecessarily increasing as a result. Via A2A:

  • It asks another agent to generate new ad variants for LinkedIn
  • It instructs a budget agent to shift budget from LinkedIn to the tech blog
  • It sends you an update: “Budget moved, new test started”

Result: The campaign is optimized continuously rather than weekly. The marketing budget is maximized, and the ROI of the campaign increases without human intervention.


4. Proactive project manager

Your team is working on a complex project with tight deadlines. A project agent has access to the schedule (like Jira or Asana) through MCP, notices that a task is falling behind, and:

  • Analyzes dependencies
  • Calculates which milestones are at risk
  • Engages an agenda agent via A2A to schedule an urgent meeting
  • Sends the team a Slack message with context and suggestions

Result: Problems are identified and addressed before management needs to step in. Projects run smoother and with significantly less stress.


5. Proactive travel assistant

A customer books a flight to Barcelona through your website. A travel agent processes the booking. Immediately after confirmation, it scans public data APIs for news and events related to the destination and travel dates via MCP. It:

  • Finds an announcement of a public transport strike on the arrival day and classifies this as a high risk for customer experience
  • Engages a local transport agent via A2A
  • Arranges transport to the hotel

Result: The traveler lands and receives a reassuring message: "Due to a public transport strike, a taxi is waiting for you. Driver Juan is at the arrival hall." This turns a potential disaster into a positive brand experience.


6. Intelligent financial assistant

A customer is using her bank's mobile banking app. A bank agent continuously monitors the customer's transactions and account balance via MCP, of course with consent. It notices:

  • Low balance in the checking account
  • Premium is due tomorrow and will fail due to insufficient funds, leading to higher costs

Via A2A, it engages a cash flow agent to calculate the minimum transfer amount and propose a micro-transfer.

Result: The customer receives a push notification: "Hi! Your insurance premium is due tomorrow. To avoid overdraft fees, I suggest transferring €34.50 from your savings account. Is that okay?" The customer avoids fees and maintains control.


7. Proactive care coordinator

A patient with heart issues wears a smartwatch, scale, and blood pressure monitor. The AI agent:

  • Collects this biometric data via MCP
  • Recognizes a concerning pattern (weight + heart rate abnormal)
  • Engages via A2A:
    • The agenda agent of the cardiologist (online consultation within 24 hours)
    • The general practitioner agent for a heads-up with data overview

Result: A potentially serious medical issue is detected days earlier than in a regular check-up. Early intervention is made possible, significantly improving health outcomes for the patient.


8. Smart product maintenance assistant

A customer buys a high-end espresso machine from your webshop. The customer service agent:

  • Registers the purchase date via MCP
  • Notices that the first descaling is due after 6 months
  • Sees that the customer hasn’t purchased descaling tablets
  • Engages a marketing agent via A2A: "Send [customer name] an email with a short instructional video on how to descale [product name] and add a 10% discount code for the corresponding descaling tablets."

Result: The customer receives proactive, helpful service that extends the life of their product. The webshop generates a relevant cross-sell and builds a long-term customer relationship.


9. Predictive maintenance platform

A factory operates 24/7 with hundreds of machines equipped with IoT sensors. A maintenance agent monitors a constant stream of data (vibrations, temperature, pressure) from all machines on the production floor via MCP. When abnormal behavior is detected:

  • Its predictive model links this to a 95% probability of a failure within 72 hours
  • Engages via A2A:
    • The procurement agent to check the stock of the spare part in the ERP system and order if necessary
    • The planning agent to schedule a maintenance technician within 3 days
    • The production agent to adjust the planning

Result: A critical machine failure is completely prevented. The company saves tens of thousands of euros in emergency repairs and lost production hours.


10. Automated financial auditor

An accounting firm needs to verify the quarterly numbers of a large company. An audit agent gains read access to the accounting system (SAP, Oracle), bank transactions, and the company's expense systems via MCP with the appropriate authorizations. It detects a series of 27 invoices from a new supplier without a valid trade register number that follow each other suspiciously quickly.

Via A2A:

  • It asks a compliance agent to conduct background research on the supplier
  • Has a data visualization agent visualize the cash flow
  • Marks suspicious transactions for human review

Result: Potential fraud, which would be nearly impossible for a human auditor to detect, is uncovered in minutes. The audit process is faster, more thorough, and more reliable.


What does this mean for your organization?

This isn’t distant future talk. MCP and A2A are the foundation for how AI will truly impact your business. Not with a big bang, but with a silent revolution – system by system, task by task.

Companies that are already thinking of AI as a team member are building a structural advantage that’s hard to catch up with.

Here’s how to prepare for the next AI step:

  • Think in skills, not in systems: Treat your internal systems as a collection of 'skills' an AI could use. Can you make a report, analysis, or action available via an API? Then an AI can use it too.
  • Find your 'agent potential': Which tasks are repetitive, predictable, and data-driven? Think of answering customer inquiries, generating reports, or distributing budgets. Perfect fodder for agents.
  • Start small, learn fast: Don't wait. Start building simple internal agents with existing AI models today. An internal HR buddy, a content checker, or a basic customer data scan. The lessons you learn today about defining tasks and structuring data will be invaluable tomorrow.

The revolution of AI agents won’t come as a 'big bang'. It will be a quiet, steady integration of smart, collaborating systems that make our businesses more efficient, strengthen our customer relationships, and expand our capabilities beyond what we can currently imagine. The question isn't if your company will become part of this ecosystem, but how quickly.