The Missing Context Layer for AI Agents in Large Enterprise Codebases
Summary: HoundDog.ai describes a Rust-based deterministic code scanner that maps service, API, field, and callsite relationships plus sensitive dataflows across repositories, then exposes that graph to AI coding agents through an MCP server. In one internal test on the same prompt and monorepo, an agent without precomputed context took 9 minutes 57 seconds and about $1.75 to reconstruct relationships, versus 1 minute 23 seconds and about $0.29 with a structured context call.
What you should know: The company says its infrastructure runs roughly 100,000 privacy code scans per day for Replit, a platform serving more than 50 million creators - and it notes a developer can add an AI SDK in a pull request that processes customer data without ever appearing in a central AI inventory.
Read the original at The Hacker News ↗
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