Designing AI Agents: Mastering Evals, Ceiling vs Floor…

Explore how to evaluate AI agents by focusing on critical issues, using ceiling-floor analysis, clustering, and code-mode testing for production reliability.

Ever feel stuck with endless AI-agent bugs that never seem to stop?

⚡ TL;DR: Focus on the few issues that actually break trust, track when they started and who they affect, and treat evals as code tests.

The Simple Version

Think of your agent like a helpful assistant. It can do amazing things (the ceiling) but the worst thing it might do (the floor) can ruin trust.

How It Actually Works

1. Two Numbers Per Issue

Every problem gets a start time and a user-impact share.

🎯 Think of it this way: It's like noting when a leak appears and how many gallons spill.

2. Clustering Isn't Issue Detection

Boundaries drift, you don't control them, and merging unrelated bugs can hide real problems.

🎯 Think of it this way: Grouping all rainy days as "wet weather" hides whether it's a drizzle or a storm.

3. Code Mode Scales to Traces

Write classifiers, run them in a sandbox at production volume, and treat them as tests.

🎯 Think of it this way: It's like adding unit tests that run on every request.

4. Agents Spot Anomalies Differently

Agents excel at investigating flagged items but aren't great at finding them automatically.

🎯 Think of it this way: A detective can follow a clue you give them, but they won't hunt clues on their own.

Technical Deep Dive

Use a switch harness to isolate failures, and keep evals in your repository as test files.

Track clusters by their concrete impact, not by vague categories, and version your classifiers alongside code.

Why This Matters

When an agent recommends a competitor or sends sloppy emails, user trust evaporates instantly.

By measuring ceiling-floor gaps, teams can prioritize fixes that protect real-world experiences.

Key Takeaways

1. Identify the floor — what breaks trust.

2. Record when an issue started and its user share.

3. Treat evals as code, not playground experiments.

4. Use clustering cautiously; it may hide distinct problems.

5. Scale testing with code-mode classifiers.

6. Guide agents with deterministic signals first.

7. Keep the harness in your repo for reliable CI.

Designing AI Agents: Mastering Evals, Ceiling vs Floor, and Production-Ready Testing