TL;DR — Key Takeaways
– AWS simplifies AI agent development: The new Strands harness provides a customizable foundation for building and deploying AI agents without requiring developers to assemble every component themselves.
– Built-in capabilities reduce development complexity: Strands harness includes shell, file and web tools, long-term memory, context management and task delegation.
– AWS targets AI agent efficiency: The company reports that Strands harness reduced costs by 28% across six benchmarks using the same Claude or GPT models.
Amazon Web Services (AWS) this week revealed it has added a harness to the open source software development kit (SDK) it makes available for building artificial intelligence (AI) agents.
First introduced last year, the Strands SDK makes it simpler for application developers to use AI models to build and deploy AI agents. The Strands harness provides a fully assembled, customizable AI agent that developers can use as a foundation for building agents designed to automate specific tasks.
Marc Brooker, vice president and a distinguished engineer at AWS, said the overall goal is to make it simpler for application developers to build an AI agent that has been trained to automate a task that is unique to their organization. In many cases, those AI agents are now being built and deployed by DevOps teams much like any other workload, he noted.
The challenge is that bringing together the right set of primitives required to build a custom AI agent can be difficult. The Strands harness provides access to all the shell, file and web tools in a way that AI models can reason across in addition to automatically loading any AI skills provided. It also enables long-term memory across runs and resumes an earlier conversation when given a session ID.
Additionally, the Strands harness delegates open-ended subtasks to a built-in helper agent along with a checklist for tracking multi-step workflows.
Just as importantly, AWS included a context window capability that offloads bulky tool results to files and caches that can be reused to reduce the total number of tokens that might be consumed by as much as 28%, said Brooker.
In general, AWS is clearly trying to make it simpler to build and deploy AI agents that run as efficiently as possible, noted Brooker. While AWS has no plans to donate the Strands SDK to a consortium, it does not restrict how its SDK might be used to build AI agents that could potentially run anywhere.
Mitch Ashley, vice president and practice lead for software lifecycle engineering at the Futurum Group, said, in effect, AWS is making it possible to create a working agent using one line of code. As it becomes easier to build AI agents, the focus of DevOps teams will turn more toward deployment, he added. That pace of deployment, however, will still be limited by what DevOps teams can observe, control, and, most crucially, prove that an AI agent has executed a task as required, noted Ashley.
While AI agents represent a new class of workloads that DevOps teams are being asked to manage, the same fundamental best practices will need to be applied. The issue is that organizations may soon have thousands of AI agents that not only have to be built and deployed, but also continuously updated. Most existing software engineering workflows were not designed with that level of scale in mind. As such, DevOps teams, if they have not done so already, should start preparing now for an era of agentic engineering that is only beginning to dawn.
Frequently Asked Questions
What is AWS Strands harness?
Strands harness is a customizable AI agent foundation that provides developers with built-in tools, memory management and task delegation capabilities for creating and deploying AI agents.
How does Strands harness simplify AI agent development?
It eliminates much of the manual work involved in assembling the components required to build AI agents, allowing developers to create working agents with minimal code.
How does Strands harness reduce AI agent costs?
Its context management capabilities offload large tool results to files and cache reusable content, reducing unnecessary token consumption. AWS reports 28% lower costs across six benchmarks using the same Claude or GPT models.
