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AI Agent Evals: The 4 Layers Most Teams Skip
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AI agent evals are how you actually know if your agent works. Testing AI agents isn't like testing normal software — unit tests, integration tests, and E2E tests all break down the moment your system is non-deterministic.
In this video, I break down why traditional testing fails for AI agents and what replaces it: evals. Not pass/fail tests, but probabilistic CI — the engineering discipline for measuring agent quality over time.
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*What you'll learn:*
- Why unit, integration, and E2E tests don't work for non-deterministic systems
- The mental model reframe: evals as manufacturing QA, not grading
- The four layers of agent quality (component, trajectory, outcome, system monitoring)
- The four dimensions that define quality (effectiveness, efficiency, robustness, safety/alignment)
- Why evaluatability is an architectural decision you make on day one
- The agent quality flywheel: turning production failures into a growing regression set
---
*Resources mentioned:*
- Agent Eval Scorecard (one-page, scored across the four layers): https://www.damiangalarza.com/agent-eval-scorecard/?utm_source=youtube&utm_medium=video&utm_campaign=agent-evals
- Work with me on production-ready AI agents:
https://www.damiangalarza.com/ai-agents?utm_source=youtube&utm_medium=video&utm_campaign=agent-evals
- Newsletter (practical AI without the hype):
https://www.damiangalarza.com/newsletter?utm_source=youtube&utm_medium=video&utm_campaign=agent-evals
_Affiliate links -- I may earn a commission or credits at no cost to you:_
- Cal.com: https://refer.cal.com/dgalarza-ucac
- Railway: https://railway.com?referralCode=dgalarza
- Pinecone: https://try.pinecone.io/vc9wh4lq5uj6
- Granola: https://go.granola.ai/damian-galarza
---
*Timestamps:*
0:00 - Shipping isn't knowing it works
0:52 - Why traditional tests break for agents
1:26 - Evals aren't grading, they're manufacturing QA
2:04 - The three things that make evals work
2:27 - Layer 1: Components
2:48 - Layer 2: Trajectory
3:14 - Layer 3: Outcome (and LLM-as-Judge)
4:06 - Layer 4: System monitoring
4:22 - Measuring from the outside in
4:41 - The four dimensions of quality
5:25 - You can only measure what you can see
5:49 - Evaluatability is an architectural decision
6:21 - The agent quality flywheel
7:05 - Recap
7:28 - Get the scorecard
---
*Work with me:*
- 1:1 Coaching (Claude Code, agent architecture, AI workflows): https://www.damiangalarza.com/coaching?utm_source=youtube&utm_medium=video&utm_campaign=agent-evals
- All services: https://www.damiangalarza.com/services
---
*About me:*
I'm Damian Galarza — Senior Software Engineer at August Health, building AI systems in production. Previously CTO at Buoy Software where I scaled a team from 0 to 50+ and shipped FDA-cleared software. I make practical AI engineering content on agent architecture, LLM applications, and production AI systems.
*Connect:*
- LinkedIn: https://www.linkedin.com/in/dgalarza
- X: https://x.com/dgalarza
- Blog: https://www.damiangalarza.com
- Dotfiles: https://github.com/dgalarza/dotfiles
#ClaudeCode #AIEngineering #AIAgents
In this video, I break down why traditional testing fails for AI agents and what replaces it: evals. Not pass/fail tests, but probabilistic CI — the engineering discipline for measuring agent quality over time.
---
*What you'll learn:*
- Why unit, integration, and E2E tests don't work for non-deterministic systems
- The mental model reframe: evals as manufacturing QA, not grading
- The four layers of agent quality (component, trajectory, outcome, system monitoring)
- The four dimensions that define quality (effectiveness, efficiency, robustness, safety/alignment)
- Why evaluatability is an architectural decision you make on day one
- The agent quality flywheel: turning production failures into a growing regression set
---
*Resources mentioned:*
- Agent Eval Scorecard (one-page, scored across the four layers): https://www.damiangalarza.com/agent-eval-scorecard/?utm_source=youtube&utm_medium=video&utm_campaign=agent-evals
- Work with me on production-ready AI agents:
https://www.damiangalarza.com/ai-agents?utm_source=youtube&utm_medium=video&utm_campaign=agent-evals
- Newsletter (practical AI without the hype):
https://www.damiangalarza.com/newsletter?utm_source=youtube&utm_medium=video&utm_campaign=agent-evals
_Affiliate links -- I may earn a commission or credits at no cost to you:_
- Cal.com: https://refer.cal.com/dgalarza-ucac
- Railway: https://railway.com?referralCode=dgalarza
- Pinecone: https://try.pinecone.io/vc9wh4lq5uj6
- Granola: https://go.granola.ai/damian-galarza
---
*Timestamps:*
0:00 - Shipping isn't knowing it works
0:52 - Why traditional tests break for agents
1:26 - Evals aren't grading, they're manufacturing QA
2:04 - The three things that make evals work
2:27 - Layer 1: Components
2:48 - Layer 2: Trajectory
3:14 - Layer 3: Outcome (and LLM-as-Judge)
4:06 - Layer 4: System monitoring
4:22 - Measuring from the outside in
4:41 - The four dimensions of quality
5:25 - You can only measure what you can see
5:49 - Evaluatability is an architectural decision
6:21 - The agent quality flywheel
7:05 - Recap
7:28 - Get the scorecard
---
*Work with me:*
- 1:1 Coaching (Claude Code, agent architecture, AI workflows): https://www.damiangalarza.com/coaching?utm_source=youtube&utm_medium=video&utm_campaign=agent-evals
- All services: https://www.damiangalarza.com/services
---
*About me:*
I'm Damian Galarza — Senior Software Engineer at August Health, building AI systems in production. Previously CTO at Buoy Software where I scaled a team from 0 to 50+ and shipped FDA-cleared software. I make practical AI engineering content on agent architecture, LLM applications, and production AI systems.
*Connect:*
- LinkedIn: https://www.linkedin.com/in/dgalarza
- X: https://x.com/dgalarza
- Blog: https://www.damiangalarza.com
- Dotfiles: https://github.com/dgalarza/dotfiles
#ClaudeCode #AIEngineering #AIAgents
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