AI for Java Architects – Part 8

Posted on by Rahul Mittal

Multi-Agent Systems and Agentic AI Architectures

“A single agent can solve a task. A team of agents can solve a business problem.”

Introduction

In Part 7, we learned that an AI agent can:

  • Reason
  • Plan
  • Use tools
  • Maintain memory
  • Execute tasks

But enterprise systems rarely rely on a single component.

We don’t build one giant Java class.

We build:

  • Controllers
  • Services
  • Repositories
  • Message consumers
  • External integrations

Similarly, modern AI systems are evolving toward:

Multi-Agent Systems

Instead of one intelligent agent doing everything, multiple specialized agents collaborate.

Why One Agent Is Not Enough

Imagine asking:

Review my architecture, identify risks, analyze costs, evaluate security, and recommend improvements.

A single agent must become:

  • Architect
  • Security expert
  • Cloud expert
  • Performance expert
  • Financial analyst

This often leads to:

  • Long prompts
  • Reduced accuracy
  • Higher hallucinations
  • Poor specialization

Software Engineering Analogy

Monolithic agent:

One Agent
    ├── Security
    ├── Architecture
    ├── Cost
    ├── Compliance
    └── Operations

Microservice agents:

Architecture Agent
Security Agent
Cost Agent
Operations Agent
Compliance Agent

Sound familiar?

What is a Multi-Agent System?

Multiple AI agents collaborate to solve problems.

Each agent has:

  • Responsibilities
  • Tools
  • Memory
  • Goals

Example

User:

Review my AWS architecture.

Agents:

  1. Architecture Agent
  2. Security Agent
  3. Cost Agent
  4. Operations Agent

Final answer combines all results.

Architecture

                User
                  ↓
         Supervisor Agent
        /       |        \
       /        |         \
Architecture Security   Cost
   Agent      Agent    Agent
       \        |         /
        \       |        /
          Final Report

Why Multi-Agent Systems Work

Benefits:

✅ Separation of concerns

✅ Specialized knowledge

✅ Independent tools

✅ Better reasoning

✅ Parallel execution

✅ Easier maintenance

Agent Roles

AgentResponsibility
PlannerBreak tasks
ResearcherGather information
ReviewerValidate results
SummarizerProduce output
ExecutorRun actions

Example: Project Review

User:

Review our microservice architecture.

Planner:

  • Analyze architecture.
  • Check security.
  • Evaluate costs.

Architecture Agent:

  • Service boundaries
  • APIs

Security Agent:

  • Authentication
  • Authorization

Cost Agent:

  • Infrastructure spending

Supervisor:

  • Consolidate findings

Supervisor Pattern

Most common enterprise pattern.

User
  ↓
Supervisor
  ↓
Workers
  ↓
Results
  ↓
Supervisor
  ↓
Answer

Example

Supervisor:

Analyze application.

Delegates:

  • Security review.
  • Performance review.
  • Scalability review.

Advantages

  • Central control.
  • Easy monitoring.
  • Clear responsibilities.

Team Pattern

Agents collaborate equally.

Agent A ↔ Agent B
     ↕
Agent C ↔ Agent D

Example:

  • Research agent
  • Validation agent
  • Writing agent

Debate Pattern

Two agents disagree.

Example:

Agent 1:

Use Kafka.

Agent 2:

Use RabbitMQ.

Judge:

Select the best solution.

Hierarchical Pattern

CEO Agent
    ↓
Manager Agents
    ↓
Worker Agents

Large organizations may eventually adopt this approach.

Blackboard Pattern

Agents share information.

Shared Knowledge Base
        ↑
    Multiple Agents

Example:

  • Architecture findings
  • Security findings
  • Cost findings

Agent Communication

Agents exchange:

  • Messages
  • Context
  • Results

Example:

{
  "risk": "Database bottleneck",
  "severity": "HIGH"
}

State Management

Multi-agent systems maintain:

Goal

Current Tasks

Completed Tasks

Failures

Results

Example

Architecture Review

✓ Security Completed

✓ Cost Completed

✗ Performance Failed

Retry Required

Enterprise Example

Architecture Review Platform

Agents:

Architecture Agent

Reviews:

  • Services
  • APIs
  • Boundaries

Security Agent

Reviews:

  • Authentication
  • Secrets
  • Vulnerabilities

Cost Agent

Reviews:

  • AWS spend
  • Scaling costs

DevOps Agent

Reviews:

  • CI/CD
  • Monitoring
  • Deployment

Final Report

Supervisor combines:

  • Risks
  • Recommendations
  • Action items

Parallel Execution

Traditional:

Security

Cost

Performance

Sequential.

Multi-agent:

Security  ─┐
Cost      ─┼── Parallel
Performance┘

Faster execution.

Memory in Multi-Agent Systems

Each agent may have:

Local Memory

Its own context.

Shared Memory

Common knowledge.

Example:

Project:
Spring Boot

Cloud:
AWS

Messaging:
Kafka

All agents can access this.

Tool Specialization

Architecture Agent:

  • Vector database
  • Documentation

Security Agent:

  • Vulnerability scanner

Cost Agent:

  • AWS billing APIs

DevOps Agent:

  • Kubernetes APIs

Human-in-the-Loop

Not every decision should be autonomous.

Example:

Agent:
Ready to deploy.

User:
Approved.

Agent:
Proceed.

Important for:

  • Banking
  • Healthcare
  • Finance

Agent Failures

Problems:

  • Infinite loops.
  • Conflicting answers.
  • Wrong tools.
  • High costs.

Guardrails

Limits:

  • Maximum steps.
  • Maximum tokens.
  • Tool permissions.
  • Approval workflows.

LangGraph

One of the most important frameworks for agents.

Concept:

Node
   ↓
Decision
   ↓
Next Node

Example

User Question
      ↓
Retrieve Documents
      ↓
Analyze
      ↓
Need More Information?
      ↓
Yes → Search Again
No  → Respond

State Graph

START
   ↓
Planner
   ↓
Research
   ↓
Review
   ↓
END

Why Graphs?

Because AI workflows are not always linear.

Example:

Search

No Result

Search Again

Review

Respond

Agentic Workflows

Traditional software:

step1();
step2();
step3();

Agent workflow:

Observe

Think

Act

Evaluate

Repeat

Enterprise Use Cases

1. Architecture Copilot

Agents:

  • Architecture
  • Security
  • Performance

2. Production Support

Agents:

  • Logs
  • Metrics
  • Incidents

3. Banking

Agents:

  • Fraud
  • Compliance
  • Risk

4. HR Assistant

Agents:

  • Leave
  • Payroll
  • Benefits

Example: Incident Analysis

User:

Production failed.

Supervisor:

  • Logs Agent.
  • Metrics Agent.
  • Deployment Agent.

Final report:

  • Root cause.
  • Recommendations.

Java Analogy

Java ArchitectureMulti-Agent
MicroservicesAgents
OrchestratorSupervisor
REST CallsAgent Communication
Shared DBShared Memory
WorkflowsAgent Graphs
BPMAgent Plans

Cost Considerations

Multiple agents mean:

  • More LLM calls.
  • More tokens.
  • More latency.

Optimization becomes important.

Observability

Track:

  • Agent execution.
  • Tool calls.
  • Token usage.
  • Errors.
  • Response time.

Interview Questions

What is a multi-agent system?

Multiple AI agents collaborating.

Why use multiple agents?

Specialization and scalability.

What is the supervisor pattern?

One coordinator manages workers.

What is LangGraph?

A framework for agent workflows.

Why use human approval?

Risk reduction.

Hands-On Project

Enterprise Architecture Reviewer

Agents:

  • Architecture Agent
  • Security Agent
  • Cost Agent
  • DevOps Agent

Input:

  • HLD
  • LLD

Output:

  • Risks
  • Recommendations
  • Action plan

Future Enterprise Architecture

Imagine:

Developer
    ↓
Architecture Agent
    ↓
Security Agent
    ↓
Cost Agent
    ↓
Deployment Agent
    ↓
Production

This may become normal software delivery.

Key Takeaways

✔ Agents specialize.

✔ Supervisors coordinate.

✔ Teams collaborate.

✔ LangGraph manages workflows.

✔ Shared memory improves results.

✔ Guardrails are essential.

✔ Multi-agent systems resemble distributed systems.

What’s Next?

Part 9 — LangChain and LangGraph for Java Architects

Topics:

  • What is LangChain?
  • Chains.
  • Retrievers.
  • Memory.
  • Tools.
  • Agents.
  • LangGraph.
  • State management.
  • Workflow orchestration.

Because understanding these frameworks helps us build production-grade AI systems instead of isolated experiments.

“Monoliths became microservices. Chatbots are becoming multi-agent systems.”

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