How to build agents over your own data

The biggest bottleneck to building powerful internal agents is giving them the right organizational context. When agents can reliably find that context, they can answer questions, review code, investigate incidents, and automate workflows across the company.

But company knowledge is scattered across Slack, email, Google Drive, code repositories, Linear, internal documentation, and multiple versions of the same information. Putting everything into the model’s context is expensive, risky, and often reduces answer quality. Connecting every source through separate MCP servers creates another bottleneck: the agent must make numerous tool calls while inheriting each service’s latency, rate limits, and uneven search quality.

This talk shows how to separate retrieval from reasoning by giving internal agents a unified search layer for company knowledge. We’ll explore how a search subagent breaks down complex tasks, combines semantic and exact retrieval across systems, preserves source permissions, verifies freshness and versions, and returns compact, cited evidence to the acting agent. Through realistic internal workflows, we’ll demonstrate how better retrieval makes agents more accurate, traceable, and useful while reducing tool calls, latency, and cost.