Building AI Agents : A2A, MCP, Scala & Apache Spark

Introduction

In today’s AI-driven world, building smart applications isn’t just about training a powerful model—it's about orchestrating agents, streamlining model interactions, and scaling data processing efficiently.

Two protocols making this possible are:

A2A (Agent-to-Agent): a standardized protocol for autonomous agent coordination

MCP (Model Context Protocol): a structured way for AI models to interact with tools, memory, and systems

When paired with Scala and Apache Spark, these protocols unlock a powerful pattern: scalable, intelligent agent-based systems that can process and act on large volumes of data—in real time.

What Are A2A and MCP? 🔗

Agent-to-Agent (A2A) Protocol 🤝

A2A allows autonomous agents to communicate, discover each other’s capabilities, and collaborate to solve complex tasks. Think of a travel planning system:

✈️ Flight Agent: books flights based on price trends, time constraints, and frequent flyer data

🏨 Lodging Agent: recommends hotels using budget, reviews, and travel history

🎟️ Activity Agent: suggests local events or tours personalized to user preferences and seasonality

Instead of building a tightly-coupled system, A2A lets these agents self-register, discover, and invoke each other’s capabilities dynamically via standardized contracts.

🧠 Model Context Protocol (MCP)
MCP, introduced by Anthropic, defines how LLMs (Large Language Models) can interact with tools, memory, documents, and APIs in a consistent way.

With MCP:

Tools are structured like RESTful resources

Models use verbs like get, set, list, subscribe

Context is shared efficiently and securely

Workflows are modular and model-driven

This makes MCP ideal for building LLM-centric workflows with dynamic access to real-world systems and knowledge.

🔁 A2A + MCP + Spark = Scalable, Intelligent Orchestration
What happens when you combine them?

Component	Role
A2A	Modular orchestration of intelligent agents (flight, hotel, activity, anomaly detection, etc.)
MCP	Standardized LLM-tool interaction for tasks like retrieving documents, triggering workflows
Apache Spark	High-throughput data processing, real-time analytics, streaming insights
Scala	The glue—concise, typed, and scalable code for orchestration and data transformation

Together, they offer:

⚙️ Distributed agent-based AI orchestration

🔄 Model-based decision-making with contextual tools

⚡ Scalable batch/streaming data analysis for agent inputs

🔐 Strongly typed, production-grade system via Scala

🛠️ Practical Implementation in Scala
A2A Agent (Scala + Spring Boot)

@Agent(
  groupName = "TravelAI", 
  groupDescription = "Agents coordinating travel plans"
)
class FlightAgent {
  @Action(description = "Get best flight options based on price, duration, and timing")
  def findFlights(origin: String, destination: String, date: String): List[String] = {
    List("Flight A at 10:00 AM", "Flight B at 3:00 PM")
  }
}

MCP Resource Example

@RestController
@RequestMapping(Array("/v1/travel"))
class HotelResource {

  @GetMapping(Array("/hotels"))
  def getHotels(@RequestParam location: String): List[String] = {
    List("Grand Maple Inn", "Budget Suites", "Lakeview Resort")
  }

  @PostMapping(Array("/hotels/book"))
  def bookHotel(@RequestBody hotel: String): String = {
    s"Hotel $hotel successfully booked."
  }
}

Spark Agent for Processing Travel Trends

class TravelTrendsAgent {

  def analyzeTrends(spark: SparkSession, dataPath: String): String = {
    val df = spark.read.option("header", true).csv(dataPath)
    val popularDestinations = df.groupBy("destination").count().orderBy(desc("count")).limit(5)
    popularDestinations.show()
    "Trend analysis completed"
  }
}

This Spark agent can now be exposed via A2A and queried by an LLM via MCP.

⚙️ Architecture: End-to-End Pipeline

graph TD
    A[User Query] --> B[LLM via Claude, OpenAI, Grok]
    B --> C[MCP invokes TravelTool.get/hotels]
    C --> D[Agent triggers Spark-based analytics]
    D --> E[A2A orchestrates flight, hotel, event agents]
    E --> F[Response composed and enriched]
    F --> G[Response sent back to LLM]

⚡ Apache Spark Use Cases in the A2A/MCP World

Use Case	Spark Role
🛰️ Real-time route optimization	Streaming GPS data processing
📈 Travel trend analysis	Batch jobs over historic booking data
🛬 Flight pricing prediction	MLlib integration for price forecasting
🧹 Agent input cleaning	Data normalization & transformation
🎯 Personalization	Recommend hotels/events based on behavior embeddings

Spark serves as a data backbone to feed intelligent agents and models accurate, timely insights.

💎 Why Scala Is a Game-Changer
Scala stands at the intersection of functional programming, strong typing, and JVM performance, making it ideal for:

✅ Spark-native workflows

✅ Protocol-safe A2A/MCP interfaces

✅ Spring Boot integration for REST + AI tooling

✅ Composable agent logic using FP constructs

✅ Streaming pipelines for reactive agents

With Scala, you write expressive, efficient, and correct code—perfect for intelligent systems that must handle both data and decisions.

🧪 Example: Spark + A2A Agent Runner

object TrendAgentRunner {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder
      .appName("Trend Analyzer")
      .master("local[*]")
      .getOrCreate()

    val agent = new TravelTrendsAgent()
    agent.analyzeTrends(spark, "data/flights.csv")
  }
}

Wrap this inside an A2A action and it becomes a discoverable, callable data-driven agent.

✅ Summary

Technology	Responsibility
A2A	Agent discovery, orchestration, standardized collaboration
MCP	Model-to-system integration with structured context sharing
Spark	Real-time, large-scale data analysis and transformation
Scala	The unified programming model tying them all together