ChatGPT Launches Group Conversations: How to Collaborate with Multiple AIs and Use in Practice for Software Development

Hello HaWkers, OpenAI just launched one of ChatGPT's most anticipated features: group conversations with multiple AI agents. Now you can have different AI "specialists" in the same conversation - one focused on backend, another on frontend, a third on security - all collaborating to solve your problem.

This change transforms ChatGPT from individual assistant to virtual development team. For developers, this opens incredible possibilities for collaboration, code review, architecture planning and much more. Let's explore how to use this feature in practice to supercharge your workflow.

How Group Conversations Work in ChatGPT

The feature allows you to add up to 5 specialized AI agents in a single conversation. Each agent has shared context but can have different instructions and "personalities".

Feature Architecture

How It Works:

1. You create a group conversation
2. Add agents with specific roles (backend dev, frontend dev, security expert, etc.)
3. Ask questions and agents collaborate to answer
4. Agents can "talk to each other" to reach the best solution

Differences vs Normal Conversation:

Aspect	Normal Conversation	Group Conversation
Perspectives	One (generalist)	Multiple (specialized)
Depth	Limited	Each agent focuses on their area
Debate	None	Agents can disagree and debate
Context	Single	Shared but with different interpretations
Ideal use	Simple questions	Complex, multi-faceted problems

Available Agent Types

OpenAI offers pre-configured templates:

• Software Engineer: Focus on code, algorithms, best practices
• System Architect: Architecture decisions, scalability, design patterns
• Security Analyst: Vulnerabilities, OWASP, threat modeling
• DevOps Engineer: CI/CD, containers, infrastructure
• UX Designer: User experience, accessibility, usability
• Product Manager: Requirements, prioritization, business trade-offs
• Data Scientist: ML, statistics, data analysis

You can also create custom agents with specific instructions.

Practical Use Cases For Developers

Let's see real scenarios where group conversations shine:

1. System Architecture Planning

Scenario: You need to architect a high-scale e-commerce system.

Conversation Setup:

• Agent 1: Backend Architect (focus on APIs, database)
• Agent 2: Frontend Architect (focus on React, performance)
• Agent 3: DevOps Engineer (focus on deploy, monitoring)
• Agent 4: Security Expert (focus on authentication, PCI compliance)

Interaction Example:

You: I need to architect an e-commerce that handles 100k simultaneous users
on Black Friday. Let's discuss the best approach?

Backend Architect: I suggest microservices architecture:
- API Gateway (Kong or AWS API Gateway)
- Catalog Service (PostgreSQL + Redis cache)
- Checkout Service (event-driven with RabbitMQ)
- Payment Service (isolated, PCI compliant)
Database sharding by geographic region for low latency.

Frontend Architect: For performance with 100k users:
- Next.js with SSR for catalog (SEO + performance)
- Global CDN (Cloudflare) for static assets
- Service Workers for partial offline mode
- Aggressive image lazy loading (WebP with fallback)
- Smart prefetching of product pages
Question: How will we sync cart state between devices?

Backend Architect: Good question. I suggest:
- Cart persisted in Redis (TTL of 7 days)
- Sync via WebSocket when user logs in
- For anonymous, localStorage + sync on account creation
Alternative: Full cart event sourcing.

Security Expert: ⚠️ Attention on some points:
- Payment can NEVER go through frontend directly
- Implement tokenization (Stripe, PayPal)
- Aggressive rate limiting on checkout API (prevent bots)
- WAF in front of API Gateway
- Secrets in vault (not in environment variables)
- CSRF tokens on all mutations

DevOps Engineer: To scale 100k simultaneous:
- Kubernetes with HPA (Horizontal Pod Autoscaler)
- Metrics: Prometheus + Grafana + AlertManager
- Load testing before event (k6 or Gatling)
- Database read replicas (PostgreSQL streaming replication)
- Circuit breakers (Hystrix/Resilience4j) for graceful degradation
Question Backend: What's the deploy strategy on event day?
Blue-green or canary?

Backend Architect: Definitely Blue-Green:
- Canary is risky with such high traffic
- Blue-green allows instant rollback
- Feature flags for A/B tests of specific features
Crucial: Code freeze 72h before event.

See the richness of the discussion? Multiple perspectives, questions between agents, and a much more robust solution than a single agent would give.

2. Multi-Perspective Code Review

Scenario: You wrote an API endpoint and want complete review.

Setup:

• Agent 1: Backend Reviewer (focus on logic, performance)
• Agent 2: Security Reviewer (focus on vulnerabilities)
• Agent 3: DX Reviewer (Developer Experience - clean code, tests)

Code for Review:

// POST /api/users
app.post('/api/users', async (req, res) => {
  const { email, password, name } = req.body;

  // Create user
  const user = await db.query(
    `INSERT INTO users (email, password, name) VALUES ('${email}', '${password}', '${name}') RETURNING *`
  );

  // Send welcome email
  sendEmail(email, 'Welcome!', `Hello ${name}!`);

  res.json({ user });
});

Agent Reviews:

Backend Reviewer:
❌ Critical Problems:
1. Missing input validation (email can be invalid)
2. Password not being hashed (NEVER store plain text)
3. Missing error handling (what if email already exists?)
4. Query doesn't use prepared statements (SQL injection risk)
5. sendEmail not awaited (can fail silently)

✅ Suggested Refactor:
async (req, res) => {
  try {
    const { email, password, name } = req.body;

    // Validation
    if (!isValidEmail(email)) {
      return res.status(400).json({ error: 'Invalid email' });
    }

    // Hash password
    const hashedPassword = await bcrypt.hash(password, 10);

    // Use prepared statement
    const user = await db.query(
      'INSERT INTO users (email, password, name) VALUES ($1, $2, $3) RETURNING id, email, name',
      [email, hashedPassword, name]
    );

    // Background job for email
    await emailQueue.add({ email, name });

    res.status(201).json({ user: user.rows[0] });
  } catch (error) {
    if (error.code === '23505') { // unique violation
      return res.status(409).json({ error: 'Email already exists' });
    }
    res.status(500).json({ error: 'Internal server error' });
  }
}

Security Reviewer:
🚨 CRITICAL VULNERABILITIES:
1. SQL INJECTION - direct string interpolation (line 5)
   Attacker can do: email = "'; DROP TABLE users; --"

2. PASSWORD PLAIN TEXT - PCI-DSS violation, GDPR violation

3. No rate limiting - allows email brute force

4. Missing CSRF protection

5. Returns complete user (can leak sensitive data)

🔒 Additional Recommendations:
- Implement zxcvbn for password strength enforcement
- Email confirmation before activating account
- Logging of signup attempts (audit trail)
- Honeypot field to detect bots
- CAPTCHA after N failed attempts

DX Reviewer (Developer Experience):
📝 Code Quality Issues:
1. Missing JSDoc or TypeScript (makes maintenance difficult)
2. Zero unit tests
3. Magic numbers/strings (hardcoded status codes)
4. Giant function (violates SRP - Single Responsibility Principle)
5. Missing validation middleware (Joi, Zod, etc.)

✨ Refactor for Better DX:

// validators/user.validator.js
const userSchema = Joi.object({
  email: Joi.string().email().required(),
  password: Joi.string().min(8).required(),
  name: Joi.string().min(2).max(100).required()
});

// services/user.service.js
class UserService {
  async createUser({ email, password, name }) {
    const hashedPassword = await bcrypt.hash(password, 10);

    const user = await this.userRepository.create({
      email,
      password: hashedPassword,
      name
    });

    await this.emailService.sendWelcomeEmail(user);

    return this.sanitizeUser(user);
  }

  sanitizeUser(user) {
    const { password, ...safeUser } = user;
    return safeUser;
  }
}

// routes/user.routes.js
router.post(
  '/users',
  rateLimiter({ max: 5, windowMs: 60000 }), // 5 requests/min
  validate(userSchema),
  catchAsync(userController.create)
);

// tests/user.service.test.js
describe('UserService.createUser', () => {
  it('should hash password before saving', async () => {
    const user = await userService.createUser({
      email: 'test@example.com',
      password: 'plainPassword123',
      name: 'Test User'
    });

    expect(user.password).toBeUndefined(); // sanitized

    const dbUser = await db.query('SELECT password FROM users WHERE id = $1', [user.id]);
    expect(dbUser.rows[0].password).not.toBe('plainPassword123');
  });
});

Three perspectives, three sets of problems identified. An incomparably more complete code review!

3. Complex Debugging

Scenario: Production bug that nobody can reproduce locally.

Setup:

• Agent 1: Backend Debugger
• Agent 2: Database Expert
• Agent 3: Infrastructure/Network Expert

Problem:

You: We have a strange bug in production:
- 0.5% of requests to GET /api/products/:id return 500
- Logs show "Connection timeout" from Postgres
- Only occurs at 2pm-4pm (business hours)
- Local environment works perfectly
- Database has 2GB RAM, 2 CPU cores

Exact error:
Error: Connection timeout
  at Pool.query (pg-pool.js:123)
  at ProductService.findById (product.service.js:45)

Backend Debugger:
🔍 Initial analysis:
- Time pattern (2pm-4pm) suggests load problem, not code bug
- 0.5% failure rate is low but consistent
- Connection timeout indicates connection pool exhausted

Questions to investigate:
1. How many connections in pool?
2. How many requests/second at that time?
3. Slow queries in Postgres?

Database Expert:
📊 Diagnostic checklist:

1. See active connections:
SELECT count(*) FROM pg_stat_activity;

2. Slow queries (> 1s):
SELECT pid, now() - pg_stat_activity.query_start AS duration, query
FROM pg_stat_activity
WHERE state = 'active' AND now() - pg_stat_activity.query_start > interval '1 second';

3. Locks:
SELECT * FROM pg_locks WHERE NOT granted;

Suspicion: Do you have index on 'id' column of products table?
If you have many JOINs or aggregations, composite indexes might be missing.

You: Pool configured with max 10 connections.
Requests: ~500/s at peak.
Slow queries: several with 5-10s duration (!!)

Database Expert:
🚨 FOUND THE PROBLEM!

10 connections for 500 req/s is absurdly low.
Quick calculation:
- 500 req/s = each request has 0.002s (2ms) available
- If query takes 5-10s, it holds connection for 5000-10000ms
- With 10 connections, you handle 10 simultaneous queries
- 490 requests wait for timeout

IMMEDIATE SOLUTION:
Increase pool to 50-100 connections:
const pool = new Pool({
  max: 50, // was: 10
  idleTimeoutMillis: 30000,
  connectionTimeoutMillis: 5000
});

LONG TERM SOLUTION:
Investigate slow queries and optimize with indexes.

Infrastructure Expert:
🏗️ Wait! Don't increase pool without checking infrastructure.

PostgreSQL on machine with 2GB RAM has limit:
- Each connection consumes ~10MB RAM
- 100 connections = 1GB just for connections
- Only 1GB left for cache, buffers, etc.

BETTER SOLUTION:
1. Implement external connection pooler (PgBouncer):
   - "transaction" mode allows thousands of virtual connections
   - Only N real connections to Postgres
   - Minimal overhead (5MB RAM total)

2. Scale Postgres vertically (4GB RAM, 4 cores)

3. Read replicas for read queries:
Primary (writes) <-- App (writes)
Replica 1 (reads) <-- App (90% of traffic)
Replica 2 (reads) <-- App (failover)

Backend Debugger:
✅ Consolidated action plan:

SHORT TERM (today):
1. Deploy PgBouncer (2h setup)
2. Increase pool to 20 (balanced with available RAM)
3. Add query timeout (30s max):
   statement_timeout = '30s'

MEDIUM TERM (this week):
1. Query audit with pg_stat_statements
2. Add indexes on slow queries
3. Implement Redis cache for most accessed products

LONG TERM (next sprint):
1. Scale database (4GB+ RAM)
2. Implement read replicas
3. Consider partitioning if products table > 10M rows

Metrics to monitor:
- Connection pool wait time (target: <100ms)
- Query duration p95 (target: <500ms)
- Database CPU (target: <70%)

A collaborative investigation that got to the root of the problem and proposed complete solution in minutes!

How to Configure Group Conversations Efficiently

To make the most of this feature:

1. Choose Complementary Agents

Good Combinations:

• Backend + Frontend + DevOps (full-stack review)
• Architect + Security + Performance (system design)
• PM + UX + Engineer (product decisions)
• Data Scientist + Backend + ML Engineer (ML features)

Avoid:

• Very similar agents (redundancy)
• More than 5 agents (conversations get confusing)

2. Give Shared Context

At the start of conversation, provide:

Context setup for all agents:

PROJECT: Personal finance management system
STACK: Node.js (Express), React, PostgreSQL, Redis
SCALE: 50k active users
COMPLIANCE: GDPR, Open Banking
DEADLINE: MVP in 8 weeks

CURRENT PROBLEM:
Implement automatic bank statement import via Open Banking.

3. Use Directed Prompts

When you want specific agent opinion:

@SecurityExpert: How to ensure Open Banking tokens
are stored securely?

@BackendDev: What's the best strategy for rate limiting
on bank APIs?

4. Synthesize Decisions

At the end of long discussions:

You: Great discussion! Can one of the agents please make an executive
summary of decisions made in bullet points?

Limitations and When NOT to Use

Group conversations are not a silver bullet:

When to Use Normal Conversation

• Simple questions (syntax, definitions)
• Quick tasks (generate boilerplate)
• Individual brainstorming
• Learning basic concepts

When to Use Group

• Important architectural decisions
• Robust code review
• Complex multi-layer debugging
• Planning large features
• Trade-off analysis (performance vs simplicity, etc.)

Current Limitations

• Maximum 5 agents per conversation
• Can get expensive quickly (each agent consumes tokens)
• Responses take longer (agents "debate")
• Not always agents disagree (sometimes just agree)

Costs and Plans

Availability:

• ChatGPT Plus: Up to 3 agents, 50 conversations/month
• ChatGPT Team: Up to 5 agents, unlimited
• ChatGPT Enterprise: Custom agents, priority

Token Consumption:

Group conversations consume approximately 2-4x more tokens than normal conversations, because:

• Each agent processes complete context
• Agents "talk to each other" (generating more tokens)
• Longer and more detailed responses

Tip: Use groups for important decisions, not trivial questions.

Future: Where This Is Going

Group conversations are just the beginning:

Emerging Trends

1. Autonomous Agents

In the future, agents will be able to:

• Execute code and run tests automatically
• Access your codebase via GitHub integration
• Create PRs with improvement suggestions
• Run CI/CD pipelines

2. Vertical Specialization

Ultra-specialized agents:

• "React Performance Expert" (only React optimization)
• "PostgreSQL Query Optimizer" (only SQL queries)
• "AWS Cost Optimizer" (only cloud cost reduction)

3. Team Workflows

Pre-defined flows:

Workflow: "Feature Development"
1. PM Agent defines requirements
2. Architect Agent proposes design
3. Backend + Frontend Agents implement
4. QA Agent generates test cases
5. Security Agent audits
6. DevOps Agent creates pipeline

Industry Impact

Group conversations accelerate "AI-augmented development" trend:

• Juniors can do senior work (with AI guidance)
• Seniors focus on strategic decisions (AIs do tactical work)
• Automatic code review becomes standard
• Onboarding new devs gets faster

Prediction: By 2027, 40% of devs will use some form of collaboration with multiple AIs daily.

Conclusion: Your Virtual Team Waiting For You

ChatGPT's group conversations transform AI from individual tool to true team of specialists. For developers, this means more robust code reviews, better-grounded architectural decisions and more efficient debugging.

Start experimenting with simple setups (2-3 agents) and increase complexity as you get the hang of it. Document the best "teams" for recurring problems in your workflow.

The future of development is collaborative - humans + AIs working together. And that future just arrived.

If you want to dive deeper into how AI is transforming software development, I recommend reading: GitHub Copilot Workspace: The End of Traditional Code Editors?, where we explore other revolutionary tools.

Let's go! 🦅

🤖 Master JavaScript to Work With AI

Using AI efficiently in development requires solid mastery of JavaScript fundamentals, async/await, APIs and architecture. Developers who deeply understand can ask better questions and implement AI-suggested solutions.

Complete Material

I've prepared a complete guide covering from fundamentals to advanced patterns:

Investment options:

• 1x of R$9.90 on credit card
• or R$9.90 cash

👉 Know the JavaScript Guide

💡 Solid foundation in JavaScript maximizes your productivity with AI tools