Framework
Build for Agents, Not Just Humans
Agents have fundamentally different interface preferences than humans. Designing for both requires treating them as separate audiences.
How it works
- 1Humans prefer names. Agents prefer IDs - names create ambiguity when the same pipeline exists across multiple teams.
- 2Humans want tables and visuals. Agents want raw, programmatically accessible data they can consume at depth without parsing a display layer.
- 3Humans want multi-step flows. Agents want single commands that encapsulate as much as possible - fewer round trips, more efficiency.
- 4Bruin tests new CLI tooling by running AI agents against it as if they were human users and benchmarking which interaction model produces faster, more accurate results.
- 5Building for agents often produces a product that looks incomprehensible to humans - and that is by design.
Hear it on the podcast
The AI Analyst That Never Sleeps: Burak Karakan of BruinListen to the episodeMore from this episode
The Centralized Data LayerOne AI data analyst serving the whole company beats fifteen teams building their own agents from different sources.The Three Stages of Data IntelligenceMost companies are stuck at Stage 1. The real value - and the real competitive advantage - lives in Stages 2 and 3.Agent Access Control as a Safety FoundationYou cannot let agents go wild if they have read-only access to two tables. Granular permissions are the foundation of safe agentic deployment.
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