Omer Grinboim
Jan 26, 2026
Intro
Performance optimization in production can feel like searching for a needle in a haystack.
Performance optimization in production can feel like searching for a needle in a haystack. You know there are opportunities out there, but finding them efficiently is another story. That’s exactly what I discovered when testing Hud’s MCP integration with Augment – and the results were nothing short of impressive.
The Setup: Smooth Onboarding with Immediate Context
One of our customers was already using Augment as their coding agent, so we decided to put Hud’s MCP integration through its paces. The onboarding process was refreshingly straightforward, and I immediately appreciated how Hud provided context about my application right from the start.
Data-driven insights are what get me excited about any tool, and Hud delivered on that front immediately.
The Real Test: Finding Performance Opportunities
Here’s where things got interesting. I asked Augment to find performance opportunities in our production environment, and the integration between Augment and Hud worked seamlessly.
Augment was able to query Hud’s data at multiple levels:
- High-level analysis: Identifying long-running endpoints that were obvious candidates for optimization
- Function-level insights: Pinpointing specific bottlenecks within those endpoints
This dual-layer approach gave us a comprehensive view of where performance issues were hiding.
Prioritization That Actually Makes Sense
What really stood out was how Augment ranked the opportunities. Instead of just listing potential issues, it prioritized them based on:
- Production usage patterns: How frequently these endpoints were being called
- Expected impact: The potential performance gains from each optimization
This level of clarity was something I hadn’t experienced with other agents so far (including Sonnet 4). It meant we could focus our efforts where they would have the most impact.
From Discovery to Implementation
With clear priorities in hand, I moved forward to fix the top issue. The process was smooth, and I was able to get to a working PR quickly.
Hud’s context engine visualizations were particularly helpful during this phase. They allowed me to monitor our progress in real-time and adjust my prompts accordingly. It was like having a performance dashboard that actually helped you make better decisions.
Room for Improvement
There was one feature I found myself missing: the ability to set granular permissions for the auto-agent mode. While fully manual mode can be tiring, I wanted the ability to approve MCP actions automatically in certain scenarios. I’m confident this is on their roadmap.
The Bottom Line
This experience transformed how I think about performance optimization. Instead of it being a time-consuming investigation, hunting for performance opportunities has become my new between-meetings hobby. The combination of Augment’s intelligent analysis and Hud’s comprehensive data made what used to be a tedious process into something genuinely enjoyable.
The integration between these tools represents a new paradigm in development workflow-one where AI agents can access real production data to provide actionable insights, and developers can act on those insights with confidence.
Ready to discover your own performance opportunities? The combination of intelligent coding agents and comprehensive observability data might just be the productivity boost your team needs.
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