{"slug": "amazon-promises-to-slash-ai-token-costs-by-30-with-cloud-migration-agent", "title": "Amazon promises to slash AI token costs by 30% with cloud migration agent", "summary": "Amazon Web Services released an open-source agentic harness, Strands harness, that its in-house tests show cuts token costs by 28% when running the same Claude or GPT models across six benchmarks, with one test using Anthropic's Fable 5 showing 77% lower cost than Claude Code. Built on the Strands harness SDK, the general-purpose agent migrates coding architecture from on-premises to the cloud and can run on Amazon Bedrock, Anthropic, OpenAI, Google, or any provider with a Linux container. AWS attributed the savings to automatic context management that truncates tool results larger than roughly 1,500 tokens and triggers summarisation once active context exceeds 85% of the model's context window.", "body_md": "Amazon Web Services (AWS) released an open-source agent to help developers move coding architecture from local premises to the cloud, potentially saving a pretty penny on tokens to boot.\n\nBuilt on the Strands harness SDK, the Amazon offering is an agentic harness, i.e. the architecture that envelops a large language model (LLM) to turn it into a functional AI agent. Described by AWS as a general-purpose agent, Strand harness produces a ready-to-run agent when deployed, represented by frog iconography rather than the usual lobster motif when it comes to agents.\n\nCarrying code from on-premises to cloud, the agent can handle its own context window, keep long-term memory across runs, delegate open-ended subtasks to a built-in helper agent, and load any pre-existing skills.\n\nInterestingly, while developed by AWS, its open-source nature means it can be run not just on Amazon Bedrock but also Anthropic, OpenAI, Google, and any provider with a Linux container.\n\n[In a blog,](https://strandsagents.com/blog/introducing-strands-harness/) AWS positioned Strands harness as a potential help with increasing AI token cost usage when running prompts, outputs, or intermediate reasoning steps.\n\nAmazon’s in-house tests showed it has 28% less token cost when running the same Claude or GPT models across six benchmarks, while achieving roughly comparable benchmark accuracy to Codex and other agentic harnesses. One test, using Anthropic’s Fable 5, even suggested Strands harness costs 77% less to use than Claude Code.\n\nTokenomics, referring to the economics of AI infrastructure, has seen model makers like DeepSeek [dramatically slash API prices](https://www.sdxcentral.com/news/deepseek-slashes-api-prices-by-90-as-ai-mad-enterprises-embrace-tokenmaxxing/) in a bid to corner the frontier AI market. But as pricing drops, usage escalates, with [Goldman Sachs research](https://www.goldmansachs.com/insights/articles/ai-agents-forecast-to-boost-tech-cash-flow-as-usage-soars) suggesting token usage will multiply 24-times between 2026 and 2030 to 120 quadrillion tokens per month, making for another software 'tax' enterprises need to budget for.\n\n## Under the harness hood\n\nAmazon put Strand's money-saving opportunity down to the tool’s automatic context management. If a tool result is larger than roughly 1,500 tokens, Strands truncates it rather than repeatedly including the entire result in subsequent model calls.\n\nThis also comes with a context compaction capability, where once active context grows beyond 85% of the model's context window, the harness triggers summarisation, replacing older detailed material with a more compact representation that preserves what the agent needs to continue working. This prevents long-running tasks from generating ever-larger prompts.\n\nStrands harness also offloads bulky tool results into files, storing data externally where the agent can retrieve what it needs later, while enabling prompt caching by default. AWS described this as caching the reused parts of each request to save both time and cost.\n\nAmazon stressed the tool does not \"sacrifice accuracy for lower cost,\" claiming Strands harness \"beats other agent harnesses on cost efficiency while maintaining equal or better accuracy.\"", "url": "https://wpnews.pro/news/amazon-promises-to-slash-ai-token-costs-by-30-with-cloud-migration-agent", "canonical_source": "https://www.sdxcentral.com/news/amazon-promises-to-slash-ai-token-costs-by-30-with-cloud-migration-agent/", "published_at": "2026-09-23 08:39:18+00:00", "updated_at": "2026-09-23 09:00:01.461888+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "ai-infrastructure", "artificial-intelligence", "developer-tools"], "entities": ["Amazon Web Services", "Strands harness", "Strands harness SDK", "Amazon Bedrock", "Anthropic", "OpenAI", "Google", "Claude Code"], "alternates": {"html": "https://wpnews.pro/news/amazon-promises-to-slash-ai-token-costs-by-30-with-cloud-migration-agent", "markdown": "https://wpnews.pro/news/amazon-promises-to-slash-ai-token-costs-by-30-with-cloud-migration-agent.md", "text": "https://wpnews.pro/news/amazon-promises-to-slash-ai-token-costs-by-30-with-cloud-migration-agent.txt", "jsonld": "https://wpnews.pro/news/amazon-promises-to-slash-ai-token-costs-by-30-with-cloud-migration-agent.jsonld"}}