AI Evaluation & Reliability
Measuring, testing, and trusting AI systems.
AI evaluation is the practice of measuring whether an AI system actually works — through evals, benchmarks, and reliability testing that catch hallucinations and regressions before they reach production.
A successful agent run needs a definition beyond a convincing final message. Anthropic's evaluation guide distinguishes the transcript from the final state of the environment and recommends graders matched to the task, with human calibration for model-based judgments (Anthropic, Demystifying evals for AI agents). For a refund agent, you might check the amount and database state as well as the explanation sent to the customer. Keep those checks separate so a good explanation cannot hide a failed action.
Alex Ratner in episode 57 describes the gap between agent capabilities and the ability to define and measure them. Hamel Husain in episode 37 starts with reading failures and building error categories. What are AI evals? introduces the practice; how to evaluate AI agents applies it to multi-step work. LLM-as-a-judge vs human evaluation covers choosing and calibrating reviewers.
Start here
- Every AI Agent Has an Evaluation Gap | Alex Ratner, Snorkel AI
Compare single-answer benchmarks with agent tasks whose objectives change across steps.
- Explaining Eval Engineering | Galileo's Vikram Chatterji
Hear why an eval must be authored, tested, and measured, not just written as an LLM judge.
Go deeper
38 episodes
- For AI Agent Fleets, the Model Is Rarely the Bottleneck | Theory Ventures' Andy Triedman
- Genspark's Bet: AI Agents Become the Users of Software | Wen Sang
- Thomson 1: The New $40M Legal AI Model | Thomson Reuters Joel Hron
- Parenting Your AI Agents for Best Results | Cisco's Jeetu Patel
- We Built Agents, Nobody Built HR | Tyler Akidau, Redpanda
- How Superhuman Built AI Into a 100ms Product | Loïc Houssier
- Every AI Agent Has an Evaluation Gap | Alex Ratner, Snorkel AI
- Hallucinations Are a Data Architecture Problem | Sudhir Hasbe, Neo4j
- Why LLMs Are Plausibility Engines, Not Truth Engines | Dan Klein, Scaled Cognition
- How Intercom Cut $250K/Month by Ditching GPT for Open Models | Fergal Reid
- Explaining Eval Engineering | Galileo's Vikram Chatterji
- Beyond Transformers: How Liquid AI Is Rethinking LLM Architecture | Maxime Labonne
- Architecting AI Agents: The Shift from Models to Systems | Aishwarya Srinivasan
- Vercel's Playbook for AI Agents: From Vibe Check to Production | Malte Ubl
- From Demo to Defensibility: How to Build an AI Business that Lasts | Aurimas Griciūnas
- Mindset Over Metrics: How to Approach AI Engineering | Hamel Husain
- Mastering Multi-Agent Systems | MongoDB’s Mikiko Chandrasekhar
- The AI Agent Trust Gap: Bridging Risk to Reliability | Elastic’s Philipp Krenn
- Architecting Reliable Agentic AI | Cisco’s Giovanna Carofiglio on the AGNTCY Collective
- The Emerging AI Agent Stack | CrewAI’s João Moura
- Your Key to AI Success is Hiding in Plain Sight | Cohesity's Greg Statton
- The 2025 AI Shift: From Chat to Task Completion & Reliable Action | Galileo Founders
- Amplitude's AI Playbook: How Wade Chambers Builds for the Agentic Future
- AI's Two Extremes – Foundations & The Frontier | Databricks’ Denny Lee
- Why Enterprises Need a Different Approach to AI Agents | Lyzr’s Siva Surendira
- Breaking the Language Barrier: Smartling's AI Translation Pipeline | Olga Beregovaya
- Low-Code AI: From Requirements to Apps in Minutes | OutSystems' Rodrigo Coutinho
- Inside IBM's watsonx: Building Enterprise AI That Ships | Dr. Maryam Ashoori
- Using AI to Modernize Your Legacy Applications | MongoDB’s Rachelle Palmer
- How Engineering Leaders Can Future-Proof Their AI Strategy | Vikram Chatterji on Dev Interrupted
- The Making of Gemini 2.0: DeepMind's Approach to AI Development and Deployment | Logan Kilpatrick
- How DeepSeek Changed the AI Race Overnight
- AI in 2025: Agents & The Rise of Evaluation-Driven Development | Galileo's Yash Sheth & Atindriyo Sanyal
- Beyond Chatbots: How Twilio Uses AI to Strengthen Human Connection | Vinnie Giarrusso
- The Enterprise AI Deployment Playbook | ServiceTitan, Indeed & Twilio
- Practical Lessons for GenAI Evals | Chip Huyen & Vivienne Zhang
- GenAI Predictions for 2025 | Databricks & Cohere
- The State of AI: Open-Source Models & Enterprise Trust | May Habib
Explainers on this topic
- How to Check an AI Answer
- How to Use Synthetic Data for AI Evaluation
- AI Evals: A Guide for Leaders
- Why LLMs Hallucinate
- Shipping AI in a Regulated Industry
- Code Review Is the New Bottleneck
- How to Measure Whether AI Is Actually Paying Off
- LLM-as-a-Judge vs. Human Evaluation: When to Use Which
- AI Agent Metrics Beyond Accuracy
- Are AI Hallucinations a Data Problem?
- How AI Agents Use Tools
- How to Evaluate a RAG System
- How to Test an AI System
- Observability vs. Evaluation vs. Benchmarking
- What Is AI Observability
- What Is LLM-as-a-Judge
- Why Enterprise AI Projects Fail to Show ROI
- Why Multi-Agent Systems Fail
- When AI Hallucinations Are Good (and When They're Dangerous)
- How to Evaluate an AI Agent: Steps, Outcomes and Side Effects
Terms on this topic
- A/B Testing
- Ablation
- Accuracy
- Agent Trajectory
- AI Benchmark
- AI Evaluation
- AI Hallucination
- AI Observability
- AI Red Teaming
- AI Safety Institute
- Answer Relevance
- ARC-AGI
- AUC-ROC
- Baseline (Machine Learning)
- Benchmark Contamination
- Benchmark Saturation
- BERTScore
- Bias-variance tradeoff
- BLEU Score
- Catastrophic Forgetting
- Cluster analysis
- Cohen's Kappa
- Concept drift
- Confidence Calibration
- Confusion Matrix
- Context Relevance
- Cross-Model Review
- Cross-Validation
- Data Labeling
- Data Leakage
- Data Poisoning
- Data Science
- Dataset
- Eval-Driven Development
- Explainability
- F1 Score
- Faithfulness
- False Positives and False Negatives
- Frontier Safety Framework
- Generalization
- Golden Dataset
- Ground Truth
- Human Evaluation
- Human in the Loop
- Humanity's Last Exam
- Imbalanced Dataset
- Instruction Adherence
- Latency
- LLM as a Judge
- LLM Leaderboard
- LLM Tracing
- Logits and log probabilities
- Mean Reciprocal Rank (MRR)
- METEOR
- MMLU
- Model Card
- Model Drift
- Non-Determinism
- Overfitting
- Pairwise Evaluation
- Pass at k
- Perplexity
- Precision and Recall
- Prompt Management
- Regression Testing
- Reinforcement Learning with Verifiable Rewards
- Reward Hacking
- Reward Model
- ROUGE
- Scheming
- Selection Bias
- Self-Correction
- Shadow Deployment
- Sparse autoencoder
- SWE-bench
- Sycophancy
- Synthetic Data
- Task Success Rate
- Token Maxxing
- Toxicity
- Training, Validation and Test Sets
- Underfitting
- Word Error Rate (WER)
Tool comparisons on this topic
Essays on this topic
Guests on this topic
Andy TriedmanWen SangJoel HronJeetu PatelTyler AkidauLoïc HoussierAlex RatnerSudhir HasbeDan KleinFergal ReidVikram ChatterjiMaxime LabonneAishwarya SrinivasanMalte UblAurimas GriciūnasHamel HusainMikiko ChandrasekharPhilipp KrennGiovanna CarofiglioJoão MouraGreg StattonAtindriyo SanyalWade ChambersDenny LeeSiva SurendiraOlga BeregovayaRodrigo CoutinhoMaryam AshooriRachelle PalmerAndrew ZiglerLogan KilpatrickYash ShethVinnie GiarrussoMehmet Murat EzbiderliGrant LedfordChip HuyenVivienne ZhangSara HookerCraig WileyMay Habib