Building Supercharger: How Rocket Close optimized title operations with agentic AI
TL;DR
In this post, we explore how Rocket Close built a solution using Strands Agents, large language models (LLMs), Amazon Bedrock, Amazon Bedrock Knowledge Bases, and Model Context Protocol (MCP) tools. We cover solution features, the rationale for the technology stack, lessons learned, and the business impact at Rocket Close.
Nauti's Take
This is the kind of AI project that actually matters: not a shiny chatbot, but an agent wired into a boring, expensive, failure-prone domain. Builders should talk less about autonomy and more about data access, tool boundaries, and who owns the process when the agent gets it wrong.
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