Building Multi-Agent AI Systems with Strands

Introduction

The landscape of artificial intelligence has fundamentally shifted with the emergence of sophisticated multi-agent systems that can collaborate to solve complex business problems. Rather than relying on a single monolithic AI model to handle all tasks, organizations are now discovering the power of specialized agents working in concert, each bringing unique capabilities and expertise to the table. This approach represents a paradigm shift in how we think about AI automation, moving from simple question-answering systems to coordinated teams of intelligent agents that can research, analyze, synthesize, and recommend solutions with remarkable sophistication. In this comprehensive guide, we’ll explore how to build production-ready multi-agent systems using Strands, an open-source framework from Amazon Web Services that makes agent development accessible, flexible, and powerful. Whether you’re looking to automate business intelligence reporting, streamline operational workflows, or create intelligent research systems, understanding how to orchestrate multiple specialized agents is becoming essential knowledge for modern development teams.

What Are Multi-Agent AI Systems and Why They Matter

Multi-agent AI systems represent a fundamental departure from traditional single-model AI approaches. Rather than asking one AI model to handle every aspect of a complex task, multi-agent systems decompose problems into specialized domains, with each agent becoming an expert in its particular area. This architectural approach mirrors how human teams work in organizations—a marketing team, a research team, a financial team, and an operations team each bring specialized knowledge and tools to solve different aspects of a larger business challenge. In the context of AI, this means you might have one agent specialized in gathering and processing real-time information from news sources, another focused on sentiment analysis and social media trends, a third dedicated to competitive research and market analysis, and yet another responsible for synthesizing all this information into actionable strategic recommendations. The power of this approach lies in its ability to handle complexity through specialization, improve accuracy through diverse perspectives, enable parallel processing of different tasks, and create more maintainable and scalable systems. When implemented correctly, multi-agent systems can accomplish in minutes what might take human teams hours or days, while maintaining the nuance and context that makes business intelligence truly valuable.

Understanding the Evolution of AI Agent Frameworks

The journey toward modern agent frameworks like Strands reflects the dramatic improvements in large language model capabilities over the past few years. In the early days of AI agents, around 2023 when the ReAct (Reasoning and Acting) paper was published, developers had to build incredibly complex orchestration logic to get language models to reliably use tools and reason through problems. The models themselves weren’t trained to act as agents—they were primarily designed for natural language conversation. This meant developers had to write extensive prompt instructions, build custom parsers to extract tool calls from model outputs, and implement sophisticated orchestration logic just to get basic agent functionality working. Even then, getting a model to produce syntactically correct JSON or reliably follow a specific format was a significant challenge. Teams would spend months tuning and tweaking their agent implementations to get them production-ready, and any change to the underlying model often required substantial reworking of the entire system. However, the landscape has transformed dramatically. Modern large language models like Claude, GPT-4, and others have native tool-use and reasoning capabilities built directly into their training. They understand how to call functions, reason about which tools to use, and handle complex multi-step tasks with minimal guidance. This evolution meant that the complex orchestration frameworks that were necessary in 2023 have become unnecessary overhead. Strands was built with this realization at its core—why build complex workflows when modern models can handle the reasoning and planning themselves? This shift from complex orchestration to model-driven simplicity is what makes Strands so powerful and why it represents the future of agent development.

Strands: The Open-Source Framework Revolutionizing Agent Development

Strands Agents is an open-source SDK developed by AWS that takes a fundamentally different approach to building AI agents. Rather than requiring developers to define complex workflows, state machines, or orchestration logic, Strands embraces the capabilities of modern language models to handle planning, reasoning, and tool selection autonomously. The framework is built on a simple but powerful principle: an agent is the combination of three core components—a model, a set of tools, and a prompt. That’s it. You define what model you want to use (whether that’s Claude, GPT-4, Llama, or any other capable model), you specify what tools the agent has access to (whether built-in tools, custom Python functions, or MCP servers), and you write a clear prompt describing what you want the agent to do. The model then uses its reasoning capabilities to figure out the rest. What makes Strands particularly revolutionary is its complete model and provider agnosticism. You’re not locked into AWS Bedrock—though that’s certainly an excellent option. You can use OpenAI’s models, Anthropic’s Claude through their API, Meta’s Llama models, local models through Ollama, or virtually any other LLM provider through LiteLLM. This flexibility means you can start development with a local model for rapid iteration, switch to a more powerful model for production, or even change providers entirely without rewriting your agent code. The framework also integrates seamlessly with other popular agent frameworks like CrewAI and LangGraph, and it has native support for Model Context Protocol (MCP) servers, which means you can leverage an entire ecosystem of pre-built tools and integrations. Additionally, Strands includes built-in support for conversation memory and session management, making it suitable for both simple one-off tasks and complex multi-turn interactions.

Setting Up Your First Strands Project: A Step-by-Step Guide

Getting started with Strands is remarkably straightforward, which is one of its greatest strengths. The setup process requires just a few basic steps that any Python developer can complete in minutes. First, you’ll create a new project directory and set up your Python environment. Create a requirements.txt file where you’ll specify your dependencies—at minimum, you’ll need the strands package and strands-agents package, but you might also add other packages depending on what tools you want to use. Next, create a .env file where you’ll store your environment variables, most importantly your credentials for whichever LLM provider you’re using. If you’re using AWS Bedrock, you’ll need to set up IAM permissions in your AWS account. Navigate to the IAM console, select your user, attach the Bedrock policy to grant permissions, and then create access keys for programmatic access. Store these keys securely in your .env file as AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. If you’re using a different provider like OpenAI, you’d instead store your API key. Then create your main Python file—let’s call it strands_demo.py. In this file, you’ll import the necessary components from Strands, instantiate an agent with your chosen model and tools, and give it a task to complete. The beauty of Strands is that this entire setup, from project creation to running your first agent, can be accomplished in under five minutes. The framework handles all the complexity of managing the agent loop, parsing model outputs, calling tools, and managing context. You simply define what you want and let the model do the reasoning.

Creating Your First Agent: The Calculator Example

To understand how Strands works in practice, let’s walk through the simplest possible example—creating an agent with a calculator tool. This example demonstrates the core concepts you’ll use in more complex systems. You start by importing the Agent class from the Strands library and the calculator tool from the Strands tools library. Then you instantiate an Agent object, passing it the calculator tool. You create a simple prompt asking the agent to calculate the square root of 1764. You assign the result to a variable and print it. That’s four lines of code. When you run this script, the agent receives your prompt, reasons that it needs to use the calculator tool to find the square root, calls the calculator with the appropriate input, receives the result (42), and returns it to you. What’s happening behind the scenes is quite sophisticated—the model is parsing your natural language request, determining which tool is appropriate, formatting the tool call correctly, executing it, and then synthesizing the result back into natural language. But from your perspective as a developer, it’s just four lines of code. This simplicity is the key insight behind Strands’ design philosophy. The framework handles all the orchestration, parsing, and management, leaving you free to focus on defining what you want your agents to do rather than how they should do it.

Building Custom Tools: Extending Agent Capabilities

While Strands comes with built-in tools like the calculator, the real power emerges when you create custom tools tailored to your specific needs. Creating a custom tool in Strands is elegantly simple. You write a Python function that does what you want, decorate it with the @tool decorator, and add a docstring that describes what the function does. That docstring is crucial—it’s what the agent reads to understand what the tool does and when to use it. For example, if you want to create a tool that adds two numbers, you’d write a function called add_numbers with a docstring explaining “Add two numbers together,” then implement the addition logic. The agent will read that docstring, understand that this tool adds numbers, and use it whenever it needs to perform addition. You can create tools for virtually anything you can write Python code for—fetching data from APIs, querying databases, processing files, calling external services, or performing complex calculations. The @tool decorator handles all the registration and integration with the agent framework. You can also use MCP (Model Context Protocol) servers as tools, which opens up an entire ecosystem of pre-built integrations. Strands includes a repository of built-in tools covering everything from memory management to file operations to AWS service interactions. This combination of custom tools and pre-built integrations means you can quickly assemble powerful agent capabilities without reinventing the wheel.

Multi-Agent Orchestration: Creating Specialized Agent Teams

The true power of Strands emerges when you move beyond single agents to create teams of specialized agents that work together. This is where you can build sophisticated systems that tackle complex business problems. The approach is straightforward: you create multiple agents, each with its own specific role, tools, and expertise. One agent might be specialized in gathering information from news sources, another in analyzing sentiment from social media, a third in researching competitive landscapes, and a fourth in synthesizing all this information into strategic recommendations. Each agent has access to different tools appropriate to its role. The news-gathering agent has tools for scraping and parsing news websites. The sentiment analysis agent has tools for processing text and scoring emotional tone. The research agent has tools for querying databases and compiling information. The synthesis agent has tools for formatting and organizing information into reports. You then orchestrate these agents by passing tasks between them, with each agent contributing its specialized expertise to the overall goal. The beauty of this approach is that it mirrors how human teams work—you wouldn’t ask your entire team to do everything; instead, you’d have specialists handle their areas of expertise and then bring their work together. With Strands, you can implement this same pattern in code, creating intelligent systems that are more capable, more maintainable, and more scalable than monolithic single-agent approaches.

Building a Business Intelligence System with Strands

To illustrate the power of multi-agent systems in practice, let’s examine a concrete example: building an automated business intelligence system that generates comprehensive reports on any topic. This system demonstrates how multiple specialized agents can collaborate to produce sophisticated analysis. The system includes a content agent responsible for gathering and processing live news from sources like TechCrunch, extracting relevant articles and summarizing their key points. A social media analyst agent simulates realistic online conversation analysis, identifying sentiment trends and key discussion topics. A research specialist agent compiles background intelligence, researches key players in the space, and creates timelines of important events. A strategic expert agent analyzes market dynamics, competitive landscapes, and identifies opportunities. A sentiment analyst agent scores the emotional tone of various sources and provides psychological insights into stakeholder sentiment. A recommendations agent creates actionable strategic advice with specific implementation steps. Finally, an executive synthesizer agent combines all the insights from the other agents into a polished, presentation-ready report. Each agent has a specific role, appropriate tools, and clear instructions about what it should focus on. When you ask the system a question like “What is happening with OpenAI right now?”, the system springs into action. The content agent goes to TechCrunch and gathers recent articles about OpenAI. The research agent compiles background information about the company and key developments. The sentiment agent analyzes the tone of coverage. The strategic agent identifies market implications. The synthesizer agent brings it all together into a coherent report. The entire process happens in minutes, producing analysis that would take a human team hours to compile. This is the power of well-orchestrated multi-agent systems.

Implementing Custom Tools for Real-World Data Collection

One of the most practical aspects of building multi-agent systems is creating custom tools that connect your agents to real-world data sources. Let’s examine how to build a tool that fetches AI news headlines from TechCrunch, which would be used by the content agent in our business intelligence system. The tool starts with a clear docstring that describes exactly what it does: “Fetch AI news headlines from TechCrunch.” This description is crucial because the agent reads it to understand when and how to use the tool. The tool then specifies its arguments—in this case, it might take a search query or topic as input. It also describes what it returns—a pipe-separated string of news headlines. The actual implementation involves defining the URL to scrape, setting up appropriate HTTP headers to avoid being blocked, making the request to the website, checking for successful response codes, parsing the HTML to extract headlines, and returning the results in the specified format. Error handling is important here—you want to gracefully handle network failures, parsing errors, or other issues that might occur when fetching external data. The tool might include logging statements to help you debug issues and understand what’s happening when the agent uses it. Once this tool is created and decorated with @tool, the agent can use it whenever it needs to gather news information. The agent doesn’t need to know how to scrape websites or parse HTML—it just knows that this tool exists, what it does, and when to use it. This separation of concerns makes the system more maintainable and allows you to update data sources without changing the agent logic.

Model Selection and Provider Configuration

One of Strands’ greatest strengths is its flexibility in model selection and provider configuration. You’re not locked into any particular model or provider, which means you can choose the best tool for your specific use case and budget. By default, Strands will look for AWS credentials and use Amazon Bedrock, which offers access to multiple models including Claude, Llama, and others. However, if you prefer to use OpenAI’s models, the process is straightforward. You import the OpenAI model class from Strands, instantiate it with your chosen model ID (like “gpt-3.5-turbo” or “gpt-4”), and pass it to your agent. The agent code remains identical—only the model configuration changes. This flexibility extends to other providers as well. You can use Anthropic’s Claude models directly through their API, Meta’s Llama models through Llama API, local models through Ollama for development and testing, or virtually any other provider through LiteLLM. This means you can start development with a fast, inexpensive local model for rapid iteration, then switch to a more powerful model for production without changing your agent code. You can also experiment with different models to see which one works best for your specific use case. Some models might be better at reasoning, others at following instructions precisely, and others at handling specific domains. The ability to swap models without rewriting code is a significant advantage that Strands provides over more rigid frameworks.

Advanced Patterns: Agent-to-Agent Communication and Handoffs

As your multi-agent systems become more sophisticated, you’ll want to implement advanced patterns like agent-to-agent communication and handoffs. These patterns allow agents to delegate tasks to other agents, creating hierarchical or networked agent systems. In a handoff pattern, one agent recognizes that a task is outside its expertise and passes it to another agent better suited to handle it. For example, in our business intelligence system, the content agent might gather raw news articles, then hand off the task of analyzing sentiment to the sentiment analysis agent. The sentiment agent processes the articles and returns its analysis, which the content agent can then incorporate into its report. This pattern mirrors how human teams work—when someone encounters a problem outside their expertise, they hand it off to someone who specializes in that area. Strands supports these patterns through its agent-as-tool capability, where one agent can be used as a tool by another agent. This creates powerful hierarchical systems where high-level agents can coordinate lower-level specialized agents. You can also implement swarm patterns where multiple agents work in parallel on different aspects of a problem, then their results are aggregated. These advanced patterns enable you to build systems of arbitrary complexity, from simple two-agent handoffs to elaborate networks of dozens of specialized agents all working together toward a common goal.

Integrating with AWS Services and External APIs

Strands’ integration with AWS services is particularly powerful for organizations already invested in the AWS ecosystem. You can create tools that interact with AWS services like S3 for file storage, DynamoDB for databases, Lambda for serverless computing, and many others. This means your agents can not only gather and analyze information but also take actions in your AWS infrastructure. For example, an agent might generate a report and automatically save it to S3, or it might query data from DynamoDB and use that information to inform its analysis. Beyond AWS, Strands supports integration with virtually any external API through custom tools. You can create tools that call REST APIs, interact with webhooks, query third-party services, or integrate with any external system your business uses. This extensibility means Strands can become the central nervous system of your automation infrastructure, coordinating activities across your entire technology stack. The combination of AWS integration and external API support makes Strands suitable for building enterprise-grade systems that need to interact with complex, heterogeneous technology environments.

Deployment Considerations and Production Readiness

While Strands makes development easy, deploying agents to production requires careful consideration of several factors. First, you need to think about where your agents will run. Strands can run anywhere Python runs—on your local machine for development, on EC2 instances for traditional server deployment, on Lambda for serverless execution, on EKS for Kubernetes-based deployment, or on any other compute platform. Each deployment option has different considerations around scaling, cost, and management. You also need to think about how your agents will be triggered. Will they run on a schedule? Will they be triggered by API calls? Will they respond to events? Strands integrates well with various triggering mechanisms, but you need to design this carefully based on your use case. Security is another critical consideration. Your agents will have access to credentials, API keys, and potentially sensitive data. You need to ensure these are managed securely, typically through environment variables or AWS Secrets Manager rather than hardcoded in your code. You should also implement proper logging and monitoring so you can understand what your agents are doing and quickly identify any issues. Error handling is crucial in production—agents should gracefully handle failures, retry appropriately, and alert you when something goes wrong. Finally, you should implement rate limiting and cost controls to prevent runaway spending on API calls or model inference.

Comparing Strands with Other Agent Frameworks

While Strands is powerful and elegant, it’s worth understanding how it compares to other popular agent frameworks like CrewAI and LangGraph. CrewAI is another popular framework that emphasizes team-based agent orchestration, with a focus on defining roles and hierarchies. CrewAI provides more structure and scaffolding around agent teams, which can be helpful for complex systems but also adds complexity. LangGraph, built on top of LangChain, provides a graph-based approach to agent orchestration, allowing you to define explicit state machines and workflows. This gives you more control over agent behavior but requires more upfront design work. Strands takes a different approach—it trusts the model to handle reasoning and planning, requiring less explicit workflow definition. This makes Strands faster to develop with but potentially less suitable for systems that require very specific, deterministic behavior. The good news is that these frameworks aren’t mutually exclusive. Strands can work alongside CrewAI and LangGraph, and you can use the best tool for each part of your system. For rapid development and systems that benefit from model-driven reasoning, Strands excels. For systems that need explicit workflow control, LangGraph might be better. For team-based agent systems with clear hierarchies, CrewAI might be the right choice. Understanding the strengths and weaknesses of each framework helps you make the right architectural decisions for your specific use case.

Practical Tips for Building Effective Multi-Agent Systems

Building effective multi-agent systems requires more than just understanding the technical framework—it requires thoughtful system design. First, clearly define the role and expertise of each agent. What is this agent responsible for? What tools does it need? What should it focus on? Clear role definition makes agents more effective and easier to debug. Second, write clear, specific prompts. The prompt is how you communicate with the agent, so invest time in making it clear and comprehensive. Describe the agent’s role, what it should focus on, what it should avoid, and what format you want the output in. Third, give agents appropriate tools. An agent with too many tools might get confused about which to use. An agent with too few tools might not be able to accomplish its task. Think carefully about what tools each agent actually needs. Fourth, test agents individually before integrating them into a system. Make sure each agent works correctly in isolation before trying to coordinate multiple agents. Fifth, implement proper error handling and logging. When something goes wrong, you need to understand what happened. Sixth, start simple and gradually add complexity. Build a working two-agent system before trying to build a ten-agent system. Seventh, monitor agent behavior in production. Track what agents are doing, how long they take, what errors they encounter, and whether they’re achieving their goals. This monitoring data is invaluable for optimization and debugging.

The Future of Multi-Agent Systems and Agentic AI

The field of multi-agent AI systems is evolving rapidly, and Strands is positioned at the forefront of this evolution. As language models continue to improve, agents will become more capable, more reliable, and more autonomous. We’re likely to see increased adoption of multi-agent systems across industries as organizations recognize the benefits of specialized, coordinated AI agents over monolithic single-agent approaches. The integration of agents with business processes will deepen, with agents not just analyzing information but actively making decisions and taking actions within business systems. We’ll likely see more sophisticated agent-to-agent communication patterns, with agents negotiating, collaborating, and competing to solve problems. The tools available to agents will expand dramatically as more services expose APIs and as MCP becomes more widely adopted. We’ll see agents that can learn from experience, adapting their behavior based on outcomes. We’ll see agents that can explain their reasoning, making them more trustworthy and easier to debug. The combination of improving models, better frameworks like Strands, and increasing adoption will create a future where multi-agent systems are as common as web applications are today. Organizations that master multi-agent system development now will have a significant competitive advantage as this technology becomes mainstream.

Leveraging FlowHunt for Enhanced Multi-Agent Workflows

While Strands provides the framework for building and running multi-agent systems, FlowHunt complements this by providing workflow automation and orchestration capabilities that enhance multi-agent systems. FlowHunt can manage the scheduling and triggering of agents, ensuring they run at the right time and in response to the right events. FlowHunt can handle data flow between agents, transforming outputs from one agent into inputs for another. FlowHunt can provide visibility into agent performance, tracking metrics like execution time, success rates, and resource usage. FlowHunt can manage error handling and retries, ensuring that temporary failures don’t derail your entire workflow. FlowHunt can integrate with your existing business systems, triggering agents based on business events and feeding agent outputs back into your systems. Together, Strands and FlowHunt create a powerful combination—Strands handles the intelligent reasoning and decision-making, while FlowHunt handles the orchestration, scheduling, and integration with your broader business processes. This combination allows you to build end-to-end intelligent automation systems that are both powerful and maintainable.

Conclusion

Multi-agent AI systems represent a fundamental shift in how we approach automation and intelligence in business. Rather than relying on single monolithic models to handle all tasks, we can now build teams of specialized agents that collaborate to solve complex problems with sophistication and efficiency. Strands, AWS’s open-source framework, makes building these systems accessible to any developer while maintaining the flexibility and power needed for production systems. The framework’s model-agnostic approach, simple API, and support for custom tools and integrations make it an excellent choice for organizations looking to harness the power of multi-agent systems. Whether you’re building business intelligence systems, automating operational workflows, or creating intelligent research assistants, the patterns and techniques discussed in this guide provide a foundation for success. Start with simple agents and gradually build toward more complex multi-agent systems. Invest in clear role definition and effective prompts. Test thoroughly before deploying to production. Monitor and optimize based on real-world performance. As you gain experience with multi-agent systems, you’ll discover new possibilities and applications that can transform how your organization operates. The future of AI is not about building bigger, more powerful single models—it’s about building smarter, more specialized teams of agents that work together to accomplish what no single agent could achieve alone.