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Multi-agent architecture

Advanced⏱ 12 min read📅 Updated: 2026-02-17

🏗️ Introduction – Architecting Agent Systems

Article 04 la single vs multi-agent basics paathom. Now let's go production-grade! 🏢

Building a multi-agent system is like building a company:

👨‍💼 CEO (Orchestrator) – strategy and coordination
👩‍💻 Engineers (Worker agents) – specialized tasks
📊 Manager (Supervisor) – quality control
💬 HR (Router) – directing requests to right team

Multi-Agent Architecture = How you structure, connect, and manage multiple AI agents for complex, production-ready systems.

This article covers:

🏗️ Architecture patterns
🔄 State management
📊 Orchestration strategies
⚡ Performance optimization
🛡️ Production considerations

📐 Architecture Patterns

Pattern 1: Supervisor Architecture 👑

code

Supervisor Agent
├── Worker Agent A (Research)
├── Worker Agent B (Analysis)
└── Worker Agent C (Writing)

Supervisor decides which worker does what. Workers report back.

Pattern 2: Pipeline Architecture ⛓️

code

Agent A → Agent B → Agent C → Agent D → Output

Linear flow. Each agent transforms and passes forward.

Pattern 3: DAG (Directed Acyclic Graph) 🔀

code

        Agent A
       /       \
  Agent B     Agent C
       \       /
        Agent D

Parallel branches, merge points. Complex but powerful.

Pattern 4: Swarm Architecture 🐝

code

Agent A ←→ Agent B ←→ Agent C
    ↕           ↕
Agent D ←→ Agent E

Peer-to-peer, emergent behavior. Most flexible, hardest to control.

Pattern	Complexity	Control	Flexibility	Best For
Supervisor	Medium	High	Medium	Task delegation
Pipeline	Low	High	Low	Sequential processing
DAG	High	Medium	High	Complex workflows
Swarm	Very High	Low	Very High	Research, exploration

🏗️ Production Multi-Agent Architecture

🏗️ Architecture Diagram

```
┌──────────────────────────────────────────────────┐
│                  API GATEWAY                     │
│          Load Balancer │ Rate Limiter            │
└─────────────────────┬────────────────────────────┘
                      │
                      ▼
┌──────────────────────────────────────────────────┐
│            🧠 ORCHESTRATOR LAYER                 │
│  ┌──────────────┐  ┌─────────────────────────┐  │
│  │ Task Planner │  │ Agent Router/Dispatcher │  │
│  └──────────────┘  └─────────────────────────┘  │
│  ┌──────────────┐  ┌─────────────────────────┐  │
│  │ State Manager│  │ Error Handler           │  │
│  └──────────────┘  └─────────────────────────┘  │
└─────────────────────┬────────────────────────────┘
                      │
          ┌───────────┼───────────┐
          ▼           ▼           ▼
┌──────────────┐┌──────────┐┌──────────────┐
│ 🔍 Research  ││ ✏️ Writer ││ 📊 Analyst   │
│    Agent     ││   Agent  ││    Agent     │
│ ┌──────────┐ ││┌────────┐││┌────────────┐│
│ │ Web Tool │ │││ LLM    ││││ Data Tool  ││
│ │ DB Tool  │ │││ Editor ││││ Chart Tool ││
│ └──────────┘ ││└────────┘││└────────────┘│
└──────┬───────┘└────┬─────┘└──────┬───────┘
       │             │             │
       └─────────────┼─────────────┘
                     ▼
┌──────────────────────────────────────────────────┐
│              SHARED INFRASTRUCTURE               │
│  ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │
│  │ State DB │ │ Message  │ │ Monitoring &     │ │
│  │ (Redis)  │ │ Queue    │ │ Observability    │ │
│  └──────────┘ └──────────┘ └──────────────────┘ │
│  ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │
│  │ Vector   │ │ Cache    │ │ Audit Logs       │ │
│  │ Memory   │ │ Layer    │ │                  │ │
│  └──────────┘ └──────────┘ └──────────────────┘ │
└──────────────────────────────────────────────────┘
```

🔄 State Management

Multi-agent state management = Most critical challenge!

What is State?

Current task progress
Intermediate results from agents
Shared context and data
Error states and recovery points

State Management Approaches:

Approach	How	Pros	Cons
Centralized Store	Redis/DB shared by all	Consistent, easy	Single point of failure
Event Sourcing	Log all state changes	Full history, replay	Complex, storage heavy
Message Passing	State in messages	No shared store	Can lose state
Graph State	LangGraph-style	Built-in, typed	Framework-dependent

Recommended: Centralized + Event Sourcing Hybrid

code

State Structure:
{
  "task_id": "task-001",
  "status": "in_progress",
  "current_step": 3,
  "total_steps": 5,
  "agents": {
    "researcher": {"status": "completed", "output": "..."},
    "writer": {"status": "in_progress", "progress": 60},
    "editor": {"status": "waiting"}
  },
  "shared_context": {...},
  "errors": [],
  "started_at": "2026-02-17T10:00:00",
  "updated_at": "2026-02-17T10:05:30"
}

Every agent reads from and writes to this shared state! 📊

🎛️ Orchestration Deep Dive

Orchestrator = The brain of multi-agent systems

Orchestrator Responsibilities:

📋 Task Planning – Break goals into agent-specific tasks
🔀 Agent Routing – Send tasks to right agents
🔄 Flow Control – Sequential, parallel, conditional execution
👁️ Monitoring – Track agent progress and health
⚠️ Error Recovery – Handle agent failures gracefully
✅ Result Aggregation – Combine outputs from multiple agents

Orchestration Frameworks:

Framework	Approach	State Mgmt	Learning Curve
LangGraph	Graph-based	Built-in	Steep
CrewAI	Role-based	Automatic	Easy
AutoGen	Conversation	Message-based	Medium
Temporal	Workflow engine	Durable	Steep
Prefect	DAG pipeline	Built-in	Medium

LangGraph Example Flow:

code

graph = StateGraph(AgentState)
graph.add_node("researcher", research_agent)
graph.add_node("writer", writing_agent)
graph.add_node("reviewer", review_agent)

graph.add_edge("researcher", "writer")
graph.add_edge("writer", "reviewer")
graph.add_conditional_edges("reviewer", 
  quality_check,  // function
  {"pass": END, "fail": "writer"}  // routing
)

🎬 Production Example – Content Platform

✅ Example

AI Content Platform Architecture:

Agents:

- 🔍 Trend Detector – Monitors social media, news for trending topics

- 📋 Content Planner – Creates editorial calendar

- ✏️ Writer – Drafts articles

- 🎯 SEO Optimizer – Keywords, meta tags, structure

- 📝 Editor – Grammar, tone, fact-checking

- 🖼️ Image Agent – Generates/selects images

- 📤 Publisher – Posts to CMS, social media

Flow:

code

Trend Detector → Content Planner → Writer
                                      ↓
                              SEO Optimizer
                                      ↓
                                   Editor ──(fail)──→ Writer (revise)
                                      ↓ (pass)
                               Image Agent
                                      ↓
                                  Publisher

Result: 50 articles/day, previously 5/day manually! 📈

Quality: 85%+ articles need zero human editing! ✅

⚡ Performance Optimization

Making multi-agent systems fast and efficient:

1. Parallel Execution ⚡

code

// Instead of sequential:
research_result = await researcher.run()  // 5s
analysis_result = await analyst.run()     // 5s
// Total: 10s

// Run in parallel:
[research, analysis] = await Promise.all([
  researcher.run(),   // 5s
  analyst.run()       // 5s
])
// Total: 5s (50% faster!)

2. Model Optimization 🧠

Agent Role	Recommended Model	Why
Router/Classifier	GPT-3.5 / Haiku	Simple task, fast
Researcher	GPT-4 / Sonnet	Needs reasoning
Writer	GPT-4 / Opus	Quality critical
Validator	GPT-3.5	Pattern matching

3. Caching Layer 💾

Similar queries → cached results
40-60% API calls saved

4. Connection Pooling 🔗

Reuse HTTP connections
Reduce latency per API call

5. Async Processing 🔄

Non-blocking agent execution
Queue-based task distribution

Benchmark targets:

Metric	Simple Task	Complex Task
Latency	<5s	<30s
Cost	<₹1	<₹10
Agents used	2-3	5-8

🛡️ Fault Tolerance & Recovery

Production systems MUST handle failures!

Failure Modes:

Failure	Impact	Recovery
Agent crash	Task stuck	Restart agent, resume from checkpoint
LLM timeout	Delayed response	Retry with backoff, fallback model
State corruption	Wrong data	Rollback to last good state
Circular dependency	Infinite loop	Timeout + circuit breaker
Resource exhaustion	System slow	Scaling + rate limiting

Circuit Breaker Pattern:

code

if (agent.consecutive_failures >= 3) {
  agent.status = "CIRCUIT_OPEN"
  // Stop sending tasks to this agent
  // Wait 60 seconds
  // Try one request (half-open)
  // If success → close circuit
  // If fail → keep open
}

Checkpoint & Resume:

code

// Save state after each step
checkpoint(state, step=3)

// On failure, resume from last checkpoint
state = load_checkpoint(task_id)
resume_from(state.last_step)  // Resumes from step 3

Dead Letter Queue:

Failed messages go to special queue
Human reviews and reprocesses
No data lost! 📬

📊 Observability & Monitoring

You can't manage what you can't measure! 📏

Key Metrics to Track:

Metric	What	Target
Task Success Rate	Completed / Total	>95%
Agent Latency	Time per agent step	<5s avg
End-to-End Latency	Total task time	<30s
Token Usage	LLM tokens consumed	Decreasing trend
Cost per Task	Total API + compute cost	Budget compliant
Error Rate	Failures / Total	<5%
Agent Utilization	Active time / Total time	>60%

Logging Strategy:

code

[2026-02-17 10:30:00] [TASK-001] [ORCHESTRATOR] Task started
[2026-02-17 10:30:01] [TASK-001] [ROUTER] Assigned to: researcher
[2026-02-17 10:30:05] [TASK-001] [RESEARCHER] Search API called
[2026-02-17 10:30:06] [TASK-001] [RESEARCHER] 5 results found
[2026-02-17 10:30:07] [TASK-001] [ROUTER] Assigned to: writer
[2026-02-17 10:30:15] [TASK-001] [WRITER] Draft complete (800 words)
[2026-02-17 10:30:16] [TASK-001] [ORCHESTRATOR] Task completed ✅

Tools: LangSmith, Weights & Biases, Datadog, custom dashboards 📊

📈 Scaling Strategies

How to scale multi-agent systems:

Horizontal Scaling ↔️

Multiple instances of same agent
Load balancer distributes tasks
Best for: High throughput

Vertical Scaling ↕️

More powerful models/hardware
Better GPU for faster inference
Best for: Quality improvement

Dynamic Scaling 📈📉

Auto-scale based on demand
Morning peak → more agents
Night → fewer agents

Scaling Architecture:

code

Load Balancer
├── Agent Pool A (3 instances)
│   ├── Agent A-1
│   ├── Agent A-2
│   └── Agent A-3
├── Agent Pool B (2 instances)
│   ├── Agent B-1
│   └── Agent B-2
└── Agent Pool C (5 instances)
    ├── Agent C-1 through C-5

Scaling triggers:

Metric	Threshold	Action
Queue depth	>100 tasks	Scale up
Latency	>10s avg	Scale up
CPU usage	>80%	Scale up
Queue depth	<10	Scale down
Cost	>budget	Scale down

🧪 Try It – Design a Multi-Agent Architecture

📋 Copy-Paste Prompt

```
You are a Systems Architect. Design a multi-agent 
architecture for this use case:

USE CASE: "AI-powered Code Review System"
- Receives GitHub PRs automatically
- Reviews code quality, security, performance
- Suggests improvements
- Approves or requests changes

REQUIREMENTS:
1. Identify all agents needed (with roles)
2. Choose an architecture pattern (with justification)
3. Design the state management approach
4. Define the communication protocol between agents
5. Plan error handling and fault tolerance
6. Define monitoring metrics
7. Draw the architecture (ASCII diagram)

Be production-ready in your design!
```

Architecture design is the most valuable skill! 🏗️

⚠️ Architecture Anti-Patterns

⚠️ Warning

Avoid these mistakes:

❌ God Agent – One agent does everything (defeats purpose)

❌ Over-fragmentation – 20 agents for simple task (overhead)

❌ No state management – Agents lose track of progress

❌ Tight coupling – Changing one agent breaks others

❌ No monitoring – Can't debug production issues

❌ Synchronous everything – Blocks on every agent call

Rules of thumb:

- ✅ 3-7 agents for most systems

- ✅ Each agent = 1 clear responsibility

- ✅ Loose coupling, high cohesion

- ✅ Async where possible

- ✅ Monitor everything

📝 Summary

Key Takeaways:

✅ Architecture patterns: Supervisor, Pipeline, DAG, Swarm

✅ State management: Centralized + Event Sourcing hybrid recommended

✅ Orchestration: LangGraph (complex), CrewAI (simple)

✅ Performance: Parallel execution, model optimization, caching

✅ Fault tolerance: Circuit breakers, checkpoints, dead letter queues

✅ Monitoring: Track success rate, latency, cost, utilization

✅ Scaling: Horizontal + Dynamic based on demand

Next article la MCP (Model Context Protocol) paapom – the new standard for agent-tool integration! 🔌

🏁 🎮 Mini Challenge

Challenge: Design Enterprise Multi-Agent System

Real-world enterprise application-ku architecture design:

Scenario: Healthcare patient management system

Intake agent (patient info)
Diagnosis assistant agent (symptoms analyze)
Treatment planner agent (treatment suggest)
Coordinator agent (manage all)

Step 1: Identify Agents (3 mins)

4 agents define:

Intake: Collect patient history, symptoms
Diagnostic: Analyze, suggest possible conditions
Planner: Create treatment plan
Coordinator: Orchestrate all agents

Step 2: Define Interactions (4 mins)

Agent communication flow:

code

Coordinator receives patient data
  ├─ Delegates to Intake
  ├─ Waits for patient profile
  ├─ Delegates to Diagnostic
  ├─ Waits for diagnosis
  ├─ Delegates to Planner
  └─ Waits for treatment plan
Finally: Sends complete plan to doctor

Step 3: State Management (3 mins)

Shared state define:

Patient ID, name, age
Symptoms list
Medical history
Diagnosis results
Treatment plan

Centralized store (Redis/DB) manage

Step 4: Error Handling (3 mins)

Multi-agent specific:

Agent timeout? → Reassign
Quality too low? → Human review
Conflict between agents? → Coordinator decides
System fail? → Graceful degradation

Step 5: Scaling Plan (2 mins)

10 concurrent patients? 100?

Add intake agents
Parallel diagnostic processing
Load balancing on coordinator
Database optimization

Enterprise system design complete! 🏢

💼 Interview Questions

Q1: Multi-agent architecture select panna criteria enna?

A: Consider: Task complexity, latency needs, cost budget, team expertise, scalability requirements, fault tolerance needs. Simple task? Single agent. Complex enterprise workflow? Multi-agent. Assess each factor carefully!

Q2: State management multi-agent system-la critical edhuku?

A: Agents data share pannum! Shared state incorrect aa irundha, whole system broken. Centralized state store (Redis/PostgreSQL) essential. Event sourcing helps track all changes. Consistency maintain critical!

Q3: Agent conflicts handle panna best approach?

A: Supervisor/coordinator pattern (one agent as authority). Voting systems (consensus). Predefined priorities (rule-based). Most practical production: Hierarchical with clear authority chain. Conflicts early prevent, escalate procedures clear!

Q4: Multi-agent production deployment risks?

Network latency (inter-agent communication)
Cost explosion (more LLM calls)
Debugging complexity (distributed system)
State synchronization issues
Agent failures cascade

Mitigate: Monitoring, redundancy, circuit breakers, gradual rollout!

Q5: Microservices vs multi-agent – production-ku which better?

A: Different tools! Microservices: deterministic, structured code. Multi-agents: AI-powered, reasoning. Ideal: Both use! Microservices handle infrastructure, agents handle intelligence. Hybrid architectures increasingly popular! 🏗️

❓ Frequently Asked Questions

❓ Multi-agent architecture production la use aagudha?

Yes! Devin, Perplexity, Salesforce Einstein, GitHub Copilot Workspace – ellaam multi-agent architectures. Enterprise adoption rapidly increasing.

❓ Architecture select panna enna consider pannanum?

Task complexity, latency requirements, cost budget, team expertise, scalability needs, and fault tolerance requirements. Simple tasks ku complex architecture over-engineering.

❓ Multi-agent vs microservices – same aa?

Similar concepts but different. Microservices = deterministic code. Multi-agent = AI-powered with reasoning. Agents can use microservices as tools.

❓ State management multi-agent la epdi handle pannanum?

Centralized state store (Redis/DB) recommended. Each agent reads/writes to shared state. Event sourcing pattern helps track all state changes.

🧠Knowledge Check

Quiz 1 of 1

Test your architecture knowledge:

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Courses

Learning Paths

Exam Prep

Multi-agent architecture

🏗️ Introduction – Architecting Agent Systems

📐 Architecture Patterns

🏗️ Production Multi-Agent Architecture

🔄 State Management

🎛️ Orchestration Deep Dive

🎬 Production Example – Content Platform

⚡ Performance Optimization

🛡️ Fault Tolerance & Recovery

📊 Observability & Monitoring

📈 Scaling Strategies

🧪 Try It – Design a Multi-Agent Architecture

⚠️ Architecture Anti-Patterns

📝 Summary

🏁 🎮 Mini Challenge

💼 Interview Questions

❓ Frequently Asked Questions