Building Supercharger: How Rocket Close optimized title operations with agentic AI

2026-06-12 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

Rocket Close, a Detroit-based title agency, developed Supercharger, an agentic AI solution, to optimize its title operations and address bottlenecks in the homebuying process. This system, built in collaboration with AWS, employs Strands Agents, large language models (LLMs) like Anthropic Claude via Amazon Bedrock, Amazon Bedrock Knowledge Bases, and Model Context Protocol (MCP) tools. Supercharger centralizes knowledge and automates research-heavy tasks, offering conversational analytics, state-level title examination assistance, API integration, and robust guardrails. The solution has significantly improved operational efficiency, reducing contact center calls and emails by 30%, enhancing state exam accuracy, and achieving 3x latency improvements through architectural refinements. It aims to guide teams through complex workflows, improving both internal processes and client experience.

Key takeaway

For AI Architects designing solutions for knowledge-intensive processes, you should consider an agentic AI framework like Strands Agents combined with an MCP tool-based architecture. This approach enables flexible data source integration and dynamic tool selection, significantly improving operational efficiency and reducing latency. Prioritize clear separation of concerns and descriptive tool naming to ensure maintainability and scalability as your system evolves.

Key insights

Agentic AI solutions can streamline complex, knowledge-intensive workflows by centralizing information and automating research, significantly boosting operational efficiency.

Principles

• Efficient data retrieval is a cornerstone of performance.
• Separate agent and tool logic for maintainability.
• Describe "what" for LLM prompts, not "how".

Method

Supercharger's agentic workflow involves WebSocket connection, token validation, Strands Agent invocation, knowledge base query, tool selection, MCP tool execution for data retrieval, context synthesis, and streaming the combined response to the user.

In practice

• Use Strands Agents for model-driven AI agent development.
• Implement MCP tools for extensible data source integration.
• Employ WebSocket streaming for immediate user feedback.

Topics

• Agentic AI
• Strands Agents
• Amazon Bedrock
• Model Context Protocol
• Title Operations
• Workflow Automation

Best for: AI Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.