MLOps.community The Hidden Bottlenecks Slowing Down AI Agents
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Aug 5, 2025 
Paul van der Boor, VP of AI at Prosus Group, and Bruce Martens, an AI Engineer at the same company, dive into the real bottlenecks in AI agent development. They reveal that it's not just about the tools but also the importance of evaluation and feedback. The duo discusses innovative strategies for creating effective evaluation sets and the impact of existing AI tools on productivity. They also examine the complexities of building custom AI solutions and the challenges of navigating cognitive load when reviewing code, highlighting the balance between AI assistance and developer mental strain.
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Eval Sets Are The True Bottleneck
- The real bottleneck in AI agent development is generating curated eval datasets and product feedback loops, not the tools themselves.
- Human time and cultural feedback loops are critical for effective model evaluation and improvement flywheels.
Buy Tools When Possible
- Encourage teams to buy existing tools to avoid wasting time building what already exists.
- Focus internal resources on core product areas where off-the-shelf solutions don’t fit or lack maturity.
User Experience Drives Tool Choice
- Teams with deep AI expertise need more low-level, headless tools rather than drag-and-drop solutions.
- Different users require vastly different tooling experiences, from engineers needing SDKs to non-technical users wanting simple interfaces.