DEV Track Spotlight: Building Production Agent Swarms - Mastering Industrial AI (DEV311)

Source: Dev.to

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 in‑depth session on building production‑ready multi‑agent systems.

Watch the full session: (link to session)

Why Multi‑Agent Systems Matter

“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.” — Betty Zheng

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 – The system continues working even if one agent fails.

Real Production Examples from 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

An autonomous system that 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 takes action. The agent can execute commands, update configurations, and resolve issues autonomously while maintaining proper guardrails and logging.

Architecture Components for Production AI Agents

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.

AI Agent Building Platforms

Choose based on your team’s technical expertise:

• Low‑code platforms (e.g., 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 is an open‑source SDK that lets you build multi‑agent systems with just a few lines of code while maintaining production‑grade reliability.

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.

Knowledge Base (RAG)

Enterprise Retrieval‑Augmented Generation (RAG) requires handling both static and dynamic data:

• Vector databases – Semantic similarity search across documents.
• Natural Language to SQL – Querying structured databases.
• API calls – Real‑time data from external systems.

Key insight: Don’t rely on a single data source. Production systems need to orchestrate multiple sources with proper security controls and data‑freshness considerations.

DevOps for AI Agents

“AI agents are software – DevOps principles apply here too.” — Trista Pan

Essential practices:

• Observability – Log agent decisions, tool calls, and reasoning chains.
• Security – Implement 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

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.

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.

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—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

“Start with single agents before scaling to multi‑agent systems.” — Trista Pan

Validate single agents first, then add complexity as requirements become clear.

Framework Selection Matters

• 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

Implement comprehensive logging:

• Agent decisions and reasoning.
• Tool calls and their results.
• Error conditions and fallbacks.
• Performance metrics and latency.

Security from Day One

• Guardrails at input and output.
• Proper authentication and authorization.
• Audit all agent actions.
• Rate limiting and abuse prevention.

About This Series

This post is part of DEV Track Spotlight, a series highlighting 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. Topics covered cutting‑edge areas such as:

• 🤖 GenAI & Agentic AI – Multi‑agent systems, Strands Agents SDK, Amazon Bedrock AgentCore, and more.