The silent AI revolution: how MCP and A2A are going to change your business (and your job)

The silent AI revolution: how MCP and A2A are going to change your business (and your job)

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

Sounds technical? Not really, but the impact is enormous. This is what the AI of tomorrow will enable. Think: AIs that autonomously delegate tasks, drive 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 preparing for it today.

In short:

  • Think of MCP as the universal power strip where AIs can plug in
  • A2A is the language that AI agents use to communicate with each other. This way, there’s no need for a single AI tool to know everything; it simply calls on a specialist agent
  • Together, MCP and A2A enable autonomous cooperation between AI systems
  • We’ll show you 10 use cases that can be applied in any industry and organization
  • Start experimenting now and building your own mini-agents.

Why this is necessary: 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 knows your schedule but not your project progress. Each model speaks its own language, without truly collaborating. So you have smart systems, but no smart system.

Think of it as a super talented team where the designer only speaks Swahili, the marketer only speaks Icelandic, and the programmer only speaks code. The knowledge is there, but communication gets lost in translation. As a result, effective collaboration breaks down. That's exactly what AIs are struggling with now, and it limits everything you can do.

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

  • reliably communicate with external tools (MCP)
  • and autonomously collaborate with other AIs (A2A)

MCP: the universal power strip for AIs

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

  • An AI that automatically pulls data from your accounting software
  • An AI that creates tickets in your CRM
  • An AI that drives 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: back in the day, every device had its own connector; now you plug everything into one port. MCP is that universal port for AI.


A2A: the collaboration language for AI agents

A2A (Agent-to-Agent Protocol) enables AIs to communicate autonomously, exchange information, and perform complex tasks together. Your AI doesn’t have to do everything on its own – it just calls in the best specialist agent. Think of:

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

Important because: You don’t have to build a jack-of-all-trades 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 easily achievable. Thanks to MCP and A2A, you can have AI agents collaborate with your systems and with each other. And it works surprisingly well, without any rocket science.

These 10 use cases show how you can already take steps toward a smarter organization using existing AI models, APIs, and tools.

1. Intelligent onboarding buddy for HR

Imagine a new colleague starts. The HR onboarding agent gets automatically activated, and:

  • Creates accounts automatically via MCP (HR tooling, 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 sees that there's a lot of searches in the knowledge base for 'submitting expense reports'
  • Via A2A, engages an internal knowledge base agent to create a personalized explanation video about the expense reporting 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 identify and approach the 10 most promising leads in the manufacturing industry. Via MCP, a sales agent plugs into the CRM system, LinkedIn Sales Navigator, and news feeds. Here, it finds companies that fit the customer profile and specifically detects 'triggers' like a recent funding 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 addresses [trigger] and the role of [contact person]."

Result: Your sales team starts the day with 10 perfectly qualified leads and a ready-made, compelling opening email that generates a much higher response rate.


3. The self-optimizing marketing campaign

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

The agent sees that the click-through rate on LinkedIn lags, while an ad on a specific tech blog performs extremely well. As a result, the cost-per-click on LinkedIn is unreasonably high. 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 shifted, new test started"

Result: The campaign is not optimized weekly, but continuously. The marketing budget is maximally utilized, and the ROI of the campaign rises 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 via MCP (like Jira or Asana), sees that a task is falling behind, and:

  • Analyzes the dependencies
  • Calculates which milestones are at risk
  • Via A2A, engages an agenda agent to schedule an urgent meeting
  • Sends a Slack message to the team with context and a proposal

Result: Problems are identified and addressed before management needs to intervene. Projects run smoothly and with significantly less stress.


5. Proactive travel assistant

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

  • Finds an announcement of a public transport strike on the arrival day and classifies it as a high risk for the customer experience
  • Via A2A, engages a local transport agent
  • Arranges transportation to the hotel

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


6. Intelligent financial assistant

A customer uses her bank's mobile banking app. A bank agent continuously monitors the customer's transactions and account balance via MCP, of course with permission. It sees:

  • Low balance in the checking account
  • Premium will be charged the day after tomorrow, which due to low balance will fail, 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 will be charged the day after tomorrow. To avoid fees, I suggest transferring €34.50 from your savings account. Agreed?" The customer avoids costs 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
  • Detects a concerning pattern (weight + heart rate abnormal)
  • Via A2A:
    • Engages the cardiologist's agenda agent (online consultation within 24 hours)
    • Engages the general practitioner agent for a heads-up with data overview

Result: A potentially serious medical issue is identified days earlier than during a regular check-up. Early intervention becomes possible, drastically improving health outcomes for the patient.


8. Smart product maintenance assistant

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

  • Records the purchase date via MCP
  • Sees that the first descaling is needed after 6 months
  • Notices that the customer hasn’t purchased descaling tablets
  • Via A2A engages the marketing agent: "Send [customer name] an email with a short video instruction for descaling [product name] and include a 10% discount code for the associated descaling tablets."

Result: The customer receives proactive, useful 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 continuously monitors a steady stream of data (vibrations, temperature, pressure) from all machines on the production floor. As soon as there is abnormal behavior:

  • It connects its predictive model to a 95% chance of failure within 72 hours
  • Via A2A:
    • Engages the purchasing agent to check the inventory of the spare part in the ERP system and orders if necessary
    • Engages the planning agent to schedule a maintenance technician within 3 days
    • Engages the production agent to adjust the schedule

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


10. Automated financial auditor

An accounting firm needs to audit the quarterly figures of a large company. An audit agent gains access via MCP with proper authorization to read the accounting system (SAP, Oracle), bank transactions, and the company's expense systems. It detects a series of 27 invoices from a new supplier without a valid Chamber of Commerce number that abnormally follow each other.

Via A2A:

  • It asks a compliance agent to conduct background research on the supplier
  • Lets a data visualization agent create a flowchart of the finances
  • Marks suspicious transactions for human review

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


What does this mean for your organization?

This is not far-off science fiction. MCP and A2A form the foundation of how AI will truly impact your business. Not with a big bang, but with a quiet revolution – system by system, task by task.

Companies that are already thinking about AI as a team member are building a structural advantage that will be hard to catch up to.

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

  • Think in skills, not systems: View your internal systems as a collection of 'skills' that an AI could utilize. Can you make a report, analysis, or action available via an API? Then an AI can use it in the future.
  • Find your 'agent potential': Which tasks are repetitive, predictable, and data-driven? Think about answering customer inquiries, generating reports, or allocating budgets. Perfect fodder for agents.
  • Start small, learn fast: Don’t wait. Begin building simple internal agents today using current AI models. An internal HR buddy, a content checker, or a simple customer data scan. The lessons you learn today about defining tasks and structuring data will be worth their weight in gold tomorrow.

The revolution of AI agents will not come as a 'big bang'. It will be a quiet, steady integration of smart, collaborating systems that make our businesses more efficient, our customer relationships stronger, and our possibilities greater than we can currently imagine. The question is not whether your business will become part of this ecosystem, but how quickly.

From concept to action with Sterc ONE

Already thinking about how agents or AI could support your organization? Then Sterc ONE is a great place to start. It brings your data, knowledge, tools, and AI together in one central platform. This gives you the space to:

  • connect your own AI agents to the systems you already use
  • store data and knowledge in one place so agents can work with it
  • connect everything securely via APIs — fully MCP-ready
  • experiment with agent behavior in a safe, controlled environment

It’s the easiest way to start small while thinking big — from a single assistant to a fully connected network of AI agents.

Curious? Take a look at Sterc ONE and see what’s already possible.

Onward to Sterc ONE!