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Latest Resources

Practical guides, field notes & frameworks for reliable AI agents.

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Code-first for engineers. Quality frameworks for product managers. Operating models for leaders.

Guide

AI model lifecycle management: 7 stages, controls, and tools

The seven stages of AI model lifecycle management, what to version at each gate, which tools own which record, and what changes for LLM apps and AI agents.

Aaron Winston 42 min read Aug 2026
Guide

AI agent debugging tools: 9 platforms compared for production in 2026

What separates a trace viewer from a production debugging system? Compare 9 tools across failure discovery, diagnosis, regression testing, and remediation.

31 min read
Guide

AI agent testing: 7 failures traditional software tests miss

Traditional tests can confirm that an AI agent request completed. Learn the seven task, tool, trajectory, and session failures that agent evals…

13 min read
Guide

Harness engineering: how to build reliable AI agents

Harness engineering is how you govern a production agent runtime: task contracts, completion gates, deterministic authority, durable checkpoints, and side-effect-aware recovery.

22 min read
The Evaluator newsletter

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New playbooks, field notes, and frameworks for building reliable AI agents.

From the Blog

Latest field notes & frameworks.

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Blog

How Uber evaluates AI agents at production scale

A background comment about pizza exposed a failure that Uber’s offline evaluations had missed. The incident helped reveal what production AI agent…

14 min read
Blog

Arize and Dynatrace: Making the World’s AI Work

Today we are announcing the signing of a definitive agreement for the acquisition of Arize by Dynatrace to accelerate our mission to…

5 min read
Post

AI agent guardrails vs. evals: How to build more reliable agent systems

Guardrails constrain what an agent can do in code; evals judge whether it performed well. Learn how both layers—and the harness around…

9 min read
Customer Lessons

Real teams, shipping AI.

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Case Studies

How Booking.com scales AI observability with Arize

How Booking.com built a unified AI observability stack with Arize for agentic GenAI workflows and traditional ML — from telemetry collection and…

11 min read
Case Studies

How TheFork Leverages Online Evals To Boost Conversions with Arize AX on AWS

TheFork is one of Europe’s leading restaurant discovery and booking platforms, connecting millions of diners with tens of thousands of restaurants across…

4 min read
Case Studies

How Handshake Deployed and Scaled 15+ LLM Use Cases In Under Six Months — With Evals From Day One

Handshake is the largest early-career network, specializing in connecting students and new grads with employers and career centers. It’s also an engineering…

4 min read
Guides

Go deep, chapter by chapter.

Long-form handbooks you can read end to end, or drop into at the chapter you need.

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Handbook

What are AI agents? Architecture, tools & how they work

Learn what AI agents are, how they work, and how to build them. Explore agent architecture, tools, memory, orchestration, security, observability, and evaluation.

  1. 01 AI agent frameworks compared: LangGraph, CrewAI, AutoGen, and more
  2. 02 Agent observability: how to trace, debug, and improve AI agents
  3. 03 How to evaluate AI agents: a production workflow
  4. 04 Agent evaluation metrics: how to measure whether an agent works
Start the guide → 4 chapters
Guide

The definitive guide to LLM evaluations

LLM evaluation: Get from pre-production to deployment with our definitive guide to LLM evaluation. Includes LLM eval types, use cases, templates and tips for continuous improvement.

  1. 01 LLM evaluation metrics: correctness, groundedness, RAG & agent scores
  2. 02 Pre-production LLM evaluation: datasets, synthetic data & benchmarks
  3. 03 CI/CD for LLM apps: experiments, regression tests & release gates
  4. 04 Production LLM evaluation: guardrails, online evals & monitoring
Start the guide → 5 chapters
Compare

How Arize stacks up.

Side-by-side breakdowns for teams evaluating tracing, evals, and agent observability tools.

Comparison

Arize vs. Braintrust

Compare Arize and Braintrust for AI observability, agent evaluation, tracing, production monitoring, deployment, and enterprise scale.

See the comparison → Comparison

Arize vs. Langfuse

Compare Arize Phoenix vs. Langfuse on open source tracing, agent evals, self-hosting, and pricing, plus when teams add Arize AX for production scale.

See the comparison → Comparison

Arize vs. LangSmith

Compare Arize vs. LangSmith for AI observability, agent evaluation, OpenTelemetry, failure discovery, self-hosting, and pricing to choose the right platform.

See the comparison →
Videos & Talks

Demos, workshops & conference talks.

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Rise of the AI Engineer

An agent got the right answer the wrong way | Michael Grinich, WorkOS

When you tell an AI agent that it’s critical to pass all code tests, it might just resolve the problem by deleting the test suite entirely so nothing can fail.

Don't ship vibes.

Trace, evaluate, and continuously improve your agents — built on open source & open standards.

Get started Self-host now