🧠 Headquarters

What Inspired This

AI workflows are not used enough and keep breaking. Not because models are bad, but because nobody knows which agent to use, when, or why. Every builder is manually stitching tools together and debugging failures themselves. Headquarters fixes this bottleneck.

What We Built

Headquarters is routing and benchmarking infrastructure for AI agents. You define a goal. The system picks the best agent for each step, validates the output, and reroutes on failure — automatically.

The core insight: treat every task as a capability request, then rank available agents by real execution data:

$$\text{Score} = w_1 \cdot \text{Task Rating} + w_2 \cdot \text{User Rating} - w_3 \cdot \text{Average Latency} - w_4 \cdot \text{Average Cost}$$

Every run feeds back into the rankings. The system gets better with use.

What We Learned

Reliability compounds. If each step succeeds with probability $p$, a workflow with $n$ steps succeeds with probability $p^n$. Small per-step failures cascade fast. Better routing directly attacks this.

The real bottleneck in AI today isn't model capability — it's selection.

Challenges

Cold start problem. New agents initially lack performance history, making early recommendations less reliable. To mitigate this, we rely on category-level priors, lightweight baseline evaluations, and gradually shift weight toward real execution data as usage increases.

Category design. Categories that are too broad dilute meaningful comparisons, while overly narrow ones fragment data. We address this by using standardized evaluation frameworks per category, allowing consistent benchmarking across different use cases without requiring domain-specific tuning.

Built With

• figma