AI has evolved beyond simple chatbots. Today's AI systems can plan, collaborate, and solve complex problems - just like a team of engineers working together. At AWS re:Invent 2025, Betty Zheng (Senior Developer Advocate at AWS) and Trista Pan (AWS Data Hero & Senior AI Engineer at Tetrate) delivered an incredible deep dive into building production-ready multi-agent systems.
This session covered everything from architecture fundamentals to real-world production deployments, with practical examples and code patterns you can use today.
Watch the full session:
Why Multi-Agent Systems Matter
As Betty explained in her opening: "AI has moved beyond chat. Today AI systems can plan, cooperate and fix real complex problems - just like we work with a team of engineers."
Single AI agents are powerful, but multi-agent systems unlock new capabilities:
- Specialization - Each agent can focus on specific tasks
- Collaboration - Agents work together to solve complex problems
- Scalability - Distribute workload across multiple agents
- Resilience - System continues working even if one agent fails
Real Production Examples from Tetrate
Trista brought invaluable real-world experience, sharing two production AI agents currently running at Tetrate:
Customer Support Agent
A sophisticated multi-agent workflow that handles both casual conversation and professional product recommendations. The system uses semantic search to understand user intent and intelligently routes between:
- Conversational responses for general questions
- Technical product recommendations with detailed specifications
- Integration with knowledge bases for accurate information retrieval
Key insight: The agent doesn't just answer questions - it understands context and adapts its response style based on whether the user needs casual help or professional technical guidance.
Troubleshooting Agent
This autonomous system goes beyond traditional chatbots by actually fixing problems in production:
- Pulls Jira tickets automatically based on priority and type
- Analyzes issues using runbooks and QA repositories
- Uses MCP (Model Context Protocol) servers to execute real fixes in production environments
Key insight: This isn't just suggesting solutions - it's taking action. The agent can execute commands, update configurations, and resolve issues autonomously while maintaining proper guardrails and logging.
Architecture Components for Production AI Agents
Trista outlined five critical components for building production-ready agent systems:
1. Models
Your foundation layer includes:
- Amazon Bedrock - Managed service with multiple model options
- OpenAI - GPT-4 and other commercial models
- Open-source models - Llama, Mistral, and others for specific use cases
Best practice: Start with managed services like Bedrock for faster iteration, then optimize with specific models as you understand your requirements.
2. AI Agent Building Platforms
Choose based on your team's technical expertise:
- Low-code platforms (n8n) - For non-technical users and rapid prototyping
- Open-source SDKs (LangChain, LlamaIndex) - For developers needing flexibility
- Strands Agents SDK - For production-grade multi-agent systems with minimal code
Strands Agents SDK deserves special attention - it's an open-source SDK that lets you build multi-agent systems with just a few lines of code while maintaining production-grade reliability.
3. Workflow Orchestration
Three main patterns for multi-agent coordination:
Orchestration Model - One lead agent delegates tasks to specialized agents
- Best for: Clear hierarchies and well-defined task delegation
- Example: A project manager agent coordinating specialist agents
Swarm Model - Agents work collaboratively without a central leader
- Best for: Dynamic problem-solving where agents need to self-organize
- Example: Multiple agents analyzing different aspects of a problem simultaneously
Workflow-Based - Static workflows connecting multiple agents
- Best for: Predictable processes with clear steps
- Example: Document processing pipeline with specialized agents at each stage
4. Knowledge Base (RAG)
Enterprise RAG requires handling both static and dynamic data:
Hybrid Search Approach:
- Vector databases - For semantic similarity search across documents
- Natural Language to SQL - For querying structured databases
- API calls - For real-time data from external systems
Key insight: Don't rely on a single data source. Production systems need to orchestrate multiple data sources with proper security controls and data freshness considerations.
5. DevOps for AI Agents
Trista emphasized: "AI agents are software - DevOps principles apply here too."
Essential practices:
- Observability - Log agent decisions, tool calls, and reasoning chains
- Security - Implement proper authentication, authorization, and data access controls
- Availability - Design for failure with retries, fallbacks, and circuit breakers
- Testing - Unit tests for individual agents, integration tests for multi-agent workflows
Production Guardrails: Three Layers of Safety
Running AI agents in production requires robust safety mechanisms. Trista outlined three types of guardrails:
1. Rule-Based Guardrails
- Filter keywords and patterns (profanity, PII, sensitive data)
- Fast and deterministic
- Easy to implement and maintain
- Use case: Blocking obvious harmful content
2. Metric-Based Guardrails
- Use hallucination scores and risk metrics
- Evaluate response quality and accuracy
- Monitor for drift and degradation
- Use case: Ensuring response quality meets thresholds
3. LLM-Based Guardrails
- Helper models detect malicious intent before processing
- Analyze context and nuance
- More sophisticated but slower
- Use case: Detecting subtle prompt injection or jailbreak attempts
Best practice: Implement all three layers. Use rule-based for fast filtering, metric-based for quality control, and LLM-based for sophisticated threat detection.
Key Takeaways and Best Practices
Start Simple, Scale Gradually
Trista's most important advice: "Start with single agents before scaling to multi-agent systems."
Don't jump straight to complex multi-agent architectures. Build and validate single agents first, then add complexity as you understand your requirements.
Framework Selection Matters
Choose based on your team and use case:
- Prototyping? Use low-code platforms like n8n
- Need flexibility? Use open-source SDKs like LangChain
- Production scale? Consider Strands Agents SDK or Amazon Bedrock AgentCore
Observability is Non-Negotiable
You can't debug what you can't see. Implement comprehensive logging:
- Agent decisions and reasoning
- Tool calls and their results
- Error conditions and fallbacks
- Performance metrics and latency
Security from Day One
Don't treat security as an afterthought:
- Implement guardrails at input and output
- Use proper authentication and authorization
- Audit all agent actions
- Implement rate limiting and abuse prevention
About This Series
This post is part of DEV Track Spotlight, a series highlighting the incredible sessions from the AWS re:Invent 2025 Developer Community (DEV) track.
The DEV track featured 60 unique sessions delivered by 93 speakers from the AWS Community - including AWS Heroes, AWS Community Builders, and AWS User Group Leaders - alongside speakers from AWS and Amazon. These sessions covered cutting-edge topics including:
- 🤖 GenAI & Agentic AI - Multi-agent systems, Strands Agents SDK, Amazon Bedrock
- 🛠️ Developer Tools - Kiro, Kiro CLI, Amazon Q Developer, AI-driven development
- 🔒 Security - AI agent security, container security, automated remediation
- 🏗️ Infrastructure - Serverless, containers, edge computing, observability
- ⚡ Modernization - Legacy app transformation, CI/CD, feature flags
- 📊 Data - Amazon Aurora DSQL, real-time processing, vector databases
Each post in this series dives deep into one session, sharing key insights, practical takeaways, and links to the full recordings. Whether you attended re:Invent or are catching up remotely, these sessions represent the best of our developer community sharing real code, real demos, and real learnings.
Follow along as we spotlight these amazing sessions and celebrate the speakers who made the DEV track what it was!
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