AWS Launches Strands Harness, an Agent That Brings Its Own Everything but the Model (6 minute read)

# AWS Strands Harness: An AI Agent That Runs Any Model Source: [https://thelettertwo.com/2026/09/21/aws-strands-harness](https://thelettertwo.com/2026/09/21/aws-strands-harness) Amazon Web Services \(AWS\) has a new tool for developers\. On Monday, the company[released its Strands harness](https://strandsagents.com/blog/introducing-strands-harness), a ready\-to\-run agent that can search the web, run commands, edit files, remember what it did yesterday, and hand off work to helper agents\. The only thing that’s missing is the intelligence to direct it\. That’s the part developers choose and can change whenever they want\. The Strands harness supports the latest models from Amazon Bedrock, Anthropic, OpenAI, and Google\. Alternatively, for on\-machine use, developers can elect to use a local Ollama model\. Today’s announcement extends Strands Agents, AWS’s open\-source SDK[introduced in May 2025](https://aws.amazon.com/blogs/opensource/introducing-strands-agents-an-open-source-ai-agents-sdk/)\. It allowed developers to quickly build and run agents with a few lines of code\. The launch of the Strands harness further accelerates that work, giving builders pretty much everything they’d need in an agent, with the exception of the brains\. It’s also cheaper to run, at least according to AWS\. The company claimed that Strands harness costs 26 percent less when “using the same model across seven benchmarks\.” It also said its agent is more token\-efficient and performs better than Claude Code and Codex\. In one evaluation with Fable 5, AWS reported that Strands harness cost 77 percent less than Claude Code while scoring higher on Terminal\-Bench 2\.1, a version the benchmark’s maintainers have since superseded with 4\.0\. However, one harness outperformed Strands harness in token efficiency, and it belonged to DeepSeek\. “Getting an agent running and getting it to perform well at a reasonable cost are different engineering problems,”[Marc Brooker](https://www.linkedin.com/in/marc-brooker-b431772b/), AWS vice president and distinguished engineer, told***The AI Economy***in an email\. “An SDK gives you the building blocks, but you still need to decide how to manage context, integrate tools, and tune the system prompt, then evaluate whether those choices improve performance or just consume more tokens\.” Out of the box, the Strands harness agent runs on current reasoning models from Amazon Bedrock, Anthropic, OpenAI, and Google, and supports the Agent Skills format that Anthropic introduced and rivals have since adopted\. What it does with that model is where AWS’s cost argument lives\. The agent keeps long\-term memory across runs and will pick up an earlier conversation when given a session ID\. It delegates open\-ended subtasks to a built\-in helper agent and tracks multi\-step work against a checklist it maintains itself\. It also manages its own context window, offloading bulky tool results to files and caching reused portions of each request\. Brooker said the Strands harness was designed for three workload families: operations and DevOps automation, back\-office process automation, and conversational experiences inside products\. “The common thread is long\-running, multi\-step tasks that you’d otherwise script or queue for a person,” he said\. “The agent can write and run code as part of a task, but it’s not a coding assistant\. That’s a different category with[great products already in it\.](https://thelettertwo.com/2026/05/12/aws-kiro-neurosymbolic-ai-reliable-coding/)” Still, building this agent wasn’t smooth sailing\. One challenge Brooker noted AWS had to overcome involved context management\. When an agent executed a long task, consisting of dozens of tool calls and thousands of lines of output, the model’s context window filled up, causing costs to “compound\.” The team implemented a context compaction solution that kept the relevant history, summarized older turns, and migrated bulky tool results to storage with a short reference call\. “Getting that right so the agent doesn’t lose the thread of what it’s doing while also keeping costs predictable was one of the hardest engineering problems,” Brooker said\. The way the Strands harness was initially designed also posed a challenge\. According to Brooker, AWS designed it to work immediately using tested defaults, but developers pushed back, saying they didn’t want a black box they couldn’t change\. With those goals in conflict, the company chose to make Strands harness a preconfigured instance of the Strands SDK, exposing every default in regular code so it can be modified without scrapping the setup and starting over\. “That took several iterations to get right,” Brooker shared\. Along with this new agent, AWS has released a Strands command\-line interface \(CLI\), built on Strands harness\. Developers can use it to prototype their agents using natural language\. All they need to do is connect it to their preferred model provider and then provide access to the prompts and tools\. “We found Strands harness unlocks a bunch of ambitious ideas because it’s easy to prototype any agent,” AWS wrote in a blog post\. “Recently, our engineer Gautam Sirdeshmukh, inspired by agent platforms like Grokbot and Muse, built a desktop app that kicks off Strands harness remotely\.” But the company isn’t the first to provide such a framework to developers\. Others with similar tools include Google’s[Antigravity agent](https://ai.google.dev/gemini-api/docs/agents)and Microsoft’s[Agent Framework Harness](https://devblogs.microsoft.com/agent-framework/the-microsoft-agent-framework-harness-is-now-released/)\. It’s also not AWS’s first harness; it has one with[AgentCore](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/harness.html)\. Even so, these agents are built to eliminate setup time and configuration issues\. With something out of the box, developers can focus on prototyping what their agents can do, spending time and resources ensuring they provide real value for their team and organization\. Brooker shared that AWS hopes developers will find this “high\-performing, token\-efficient” agent useful “without having to become experts in harness design\.” It combines the company’s agent\-building knowledge with “tested, tuned defaults” for context management, tools, memory, and the system prompt\. He pointed out that developers will benefit from AWS’s engineering without having to research, assemble, or tune every component themselves\. “That shifts where developers spend their effort: defining what the agent should accomplish, connecting it to their systems, and evaluating it for their use case,” Brooker said\. The Strands Harness agent is available from PyPI \(the Python package index\), npm for TypeScript, and through the previously mentioned CLI\. Developers can install it with a single command\. It can run on a laptop, in a CI pipeline, on private servers, or in a cloud deployment\. AWS infrastructure or Amazon Bedrock aren’t prerequisites either\. Developers are only responsible for the runtime and any connected tools or services\.