Blog
Builder Center2026Language models solved only 34% of SWE-InfraBench's infrastructure-as-code tasks in one attempt. We ran Kiro over all 100 and found that a full agent loop with Sonnet 5 reaches 82%, with partially correct answers converging once the agent can re-run the tests itself.
AWS Blog2026Defects that clear compilers, static analyzers and code review still surface during vehicle integration testing, the most expensive place to find them. We built a multi-agent system that detected 30+ high-priority findings on production code.
Builder Center2026We benchmark Task Arithmetic, TIES, DARE, and Model Soups for merging Qwen3.5 experts across five domains. The best expert training duration turns out to depend on the merging method, with sparsification-based methods peaking well past the validation optimum.
Builder Center2026Self-reflection lets a model critique and revise its own output before returning it. We apply it at inference time on Amazon Bedrock and measure the accuracy gain against the added cost and latency across reflection depths.
AWS Blog2026We paired a fine-tuned Qwen3-32B with Claude Sonnet orchestration over Amazon Bedrock and MCP to build a multi-model coding assistant. Fine-tuning on 7,000 annotated functions cut C++ architecture compliance violations to 0.02 against generic models.
AWS Blog2025RAG systems still answer confidently when the retrieved context does not support the answer. We add a detection layer that flags those cases, comparing methods such as LLM-as-judge and semantic similarity on accuracy against cost.
AWS Blog2023When retrieval returns a weak answer, most RAG systems reply anyway. We add a clarification tool so the system asks a follow-up question instead, combining Amazon Kendra retrieval, LangChain orchestration, and Amazon Bedrock.