Scaling Enterprise Support Volume with Context-Aware AI Agents
Executive Summary
Developing a sandboxed code execution and query retrieval system that resolves 70% of technical database queries automatically for customer support operations.
The Challenge
A global B2B SaaS provider was spending excessive resources on support operations handling developer inquiries. Customers regularly requested complex reports, custom query builders, and database schema clarifications. Resolving these tickets required human support engineers to manually construct SQL scripts, verify permissions, and return the data, leading to average ticket resolution times of 18 hours.
The Approach
We designed and built a multi-agent system powered by LLMs, integrating pgvector for semantic schema retrieval. To run user queries safely, we implemented a micro-VM compute sandbox. The agent translates user requests into structured SQL queries, evaluates the queries against a read-only database replica inside the sandboxed environment, checks boundaries for strict tenant-level isolation, and outputs structured analytical charts directly to the user dashboard.
The Impact
The automated AI agent pipeline resolved 70% of technical inquiries within 10 seconds of submission, bypassing human support lines completely. Security boundaries performed flawlessly, resisting database injections and unauthorized tenant cross-reads. Support overhead decreased by 68%, allowing the core engineering team to focus entirely on their main product backlog.
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