Amazon Web Services (AWS) Open Source SDK Strand agentsaimed at making the development of AI agents more accessible and adaptable in various fields. By following an approach focused on the model, the SDK of the strands agents sums up a large part of the complexity behind the construction, the orchestration and the deployment of intelligent agents, which facilitates developers to create tools that plan, reason and interact independently.
Define an agent in the strands
Basically, an AI agent built with strands is defined by three essential components: a model, a set of tools and a prompt. These components allow the agent to perform tasks, going from the response to requests through the orchestration of workflows – in reasoning and selecting tools using large language models (LLM).
- Model: Strands supports a range of models, including those of Amazon's foundation (such as Claude or Titan), Anthropic, Meta's Lalama and other suppliers via APIs like Litellm. It also supports the development of local models using platforms like Olllama, and developers can define suppliers of personalized models if necessary.
- Tools: The tools represent external functionalities that the model can invoke. Strands provides more than 20 predefined tools, going from file operations to API calls and AWS service integrations. Developers can also easily record their own Python functions using the
@tooldecorator. In particular, the strands support thousands of model context protocol servers (MCP), allowing dynamic interaction of tools. - Fast: This defines the task or the objective that the agent must perform. The prompts can be defined by the user or defined in the system for general behavior control.
The Agent Loop
The strands work via a loop where the agent interacts with the model and the tools until the task defined by the prompt is finished. Each iteration consists in invoking the LLM with the current context and the descriptions of tools. The model can choose to generate an answer, plan several steps, think about past actions or invoke tools.
When a tool is selected, the strands execute it and strengthen the result to the model, continuing the loop until a final response is ready. This mechanism takes advantage of the LLM growth capacity to reason, plan and adapt in the context.
Extensibility through tools
One of the SDK forces of the strands lies in how the tools can be used to extend the behavior of the agents. Some of the most advanced tools include:
- Recover the tool: Integrates into the knowledge bases of the Amazon foundation to implement semantic research, allowing models to dynamically recover documents or even to select relevant tools from thousands of options using a similarity based on integration.
- Reflection tool: Invite the model to engage in analytical reasoning in several stages, allowing more in-depth planning and self-reflection.
- Multi-agent tools: Including workflow, graph and swarm tools, these allow the orchestration of sub-agents for more complex tasks. Strands plans to support the Agent2agent (A2A) protocol in order to further improve multi-agent collaboration.
Real world applications and infrastructure
The agents of the strands have already experienced an internal adoption in AWS. Teams such as Amazon q Developer, AWS Glue and VPC Reachabity Analyzer integrated it into production workflows. The SDK supports a range of deployment objectives, including local environments, AWS Lambda, Fargate and EC2.
Observability of the agent is integrated by OpenTelemetry (Otel), allowing detailed monitoring and diagnosis – criticism for production quality systems.
Conclusion
Strands SDK agents offers a structured but flexible framework to build AI agents by emphasizing a clean separation between models, tools and prompts. Its loop and its integration focused on the model with existing LLM ecosystems make it a technically solid choice for developers who seek to implement autonomous agents with a minimum of driving and strong personalization capacities.
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