Everyone Is Building Deep Research Agents. Most of Them Are Architecturally Broken.

1. 1Analyze existing deep research agents for architectural weaknesses
2. 2Identify potential bottlenecks in the agent's architecture
3. 3Design alternative architectures that address these weaknesses
4. 4Test and evaluate the performance of new architectures
5. 5Compare results with existing models to identify areas for improvement

Machine learning engineers and researchers building deep research agents can benefit from understanding the architectural flaws in current models to improve their design

💡 Current deep research agents often have architectural flaws that hinder their performance

💡 Most deep research agents are broken! Learn how to build better ones