Building Multi Agent AI Workflows with Strands Agents and Meta Llama 4 on Amazon Bedrock

Introduction

As organizations embrace generative AI for increasingly sophisticated applications, traditional single-model systems often struggle to manage complex processing tasks such as long-context reasoning, multimodal analysis, and structured coordination. These scenarios require AI workflows that can reason over extended content, delegate subtasks, and integrate diverse data types, such as video frames and text.

This blog explores how to construct a robust multi-agent workflow using Strands Agents, Meta’s Llama 4 family of models, and Amazon Bedrock. The combined solution enables developers to create coordinated AI agents that collaborate on complex tasks such as video processing, thereby improving modularity, scalability, and reliability.

Multi-Agent Workflow

A multi-agent architecture distributes work across several specialized AI components, each designed to handle a portion of the overall task. This workflow enables agents to interact through structured outputs while preserving context and shared information.

Instead of treating a large task as a single block of reasoning, the system breaks it down into well-defined subtasks, each managed by an individual agent. In the demonstrated solution, this approach is applied to a video processing pipeline where agents collaboratively extract frames, analyze visuals, reason over time, and generate summaries.

Meta’s Llama 4: Foundation Model Capabilities

Meta’s Llama 4 models provide key capabilities that make them suitable for agentic workflows. Llama 4 Scout supports ultra-long context windows of up to millions of tokens, enabling reasoning across very large datasets. The models also support multimodal understanding, allowing agents to process both text and image inputs.

Additionally, the mixture-of-experts architecture improves efficiency while maintaining high reasoning performance. These characteristics make Llama 4 well suited for multi-step, multi-agent workflows deployed on Amazon Bedrock.

Solution Overview

The solution described uses a multi-agent video processing workflow built with Strands Agents and hosted on Amazon Bedrock. Each agent contributes a specific capability such as frame extraction, visual analysis, temporal reasoning, or summarization.

While video analysis is used as the primary example, the architecture is broadly applicable to other enterprise scenarios, including large document processing, cross-document summarization, and multimodal intelligence applications.

Coordinator Agent: Orchestration and Task Planning

The coordinator agent serves as the entry point to the system. It interprets the user’s request, decomposes it into smaller subtasks, and orchestrates execution across specialist agents.

This agent maintains global context, ensures tasks are executed in the correct order, and aggregates outputs into a coherent final response. Its role is critical in managing complexity and ensuring smooth collaboration between agents.

Specialist Agents: Focused Task Execution

Specialist agents perform narrowly scoped tasks delegated by the coordinator agent. In the video processing workflow, these include agents for extracting frames, analyzing visual content, and performing temporal reasoning.

Each specialist agent uses Meta’s Llama 4 models via Amazon Bedrock to perform reasoning within a constrained scope. This separation of responsibilities improves accuracy, reduces hallucinations, and enhances reusability across workflows.

Validation Agents: Quality and Consistency Control

The validation agent ensures that outputs produced by specialist agents are logically consistent, complete, and aligned with the original objective. It reviews intermediate and final results before they are returned to the user.

This additional validation step increases trustworthiness and makes the multi-agent system suitable for enterprise and decision-critical workloads.

Execution Flow with Amazon Bedrock

The workflow runs on Amazon Bedrock, which provides managed, scalable access to Meta’s Llama 4 foundation models. A request is received, processed by the coordinator agent, delegated to specialist agents, and reviewed by the validation agent.

Intermediate outputs can be stored in Amazon S3, and long-running or auxiliary tasks can be handled using AWS Lambda. Amazon Bedrock manages inference scalability, security, and access control throughout the process.

Technical Challenges and Optimizations

Designing effective multi-agent systems requires careful task decomposition, controlled context sharing, and clear agent boundaries. Poor design can introduce latency or redundant reasoning.

Optimizations include limiting shared context, keeping agent responsibilities narrowly focused, and introducing validation steps early. Observability and structured outputs are essential for debugging and performance tuning as workflows scale.

Conclusion

This architecture enables collaborative reasoning and provides a strong foundation for next-generation enterprise AI applications.

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