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Featured report

The agent reliability gap

Agent capability has advanced faster than the production systems around it. A strong model can still fail when an application supplies incomplete context, exposes the wrong tool, loses state, mishandles a dependency, or permits an action that violates policy. The final answer may still…

Aaron Winston 4 min read August 2026
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Guide

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

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Guide

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

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Guide

AI agent testing: 7 failures traditional software tests miss

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Latest resources

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

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

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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,…

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Guide

What “self-hosted” actually means in AI observability

Learn what self-hosted AI observability means. Compare SaaS, hybrid, open-source, private, and air-gapped deployments with a vendor checklist.

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Guide

AI observability pricing: how traces, spans, scores, and seats change your bill

Compare AI observability pricing across LangSmith, Langfuse, Braintrust, Datadog, and Arize. See what each vendor meters, how evals…

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Guide

7 best AI agent evaluation platforms compared for 2026

The best LLM evaluation platform is the one that can evaluate the units your application actually produces, run…

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The Evaluator newsletter

The agent feedback loop, in your inbox.

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

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 4 of 5 chapters
From the blog

The latest field notes and 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…

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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…

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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…

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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 —…

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Case Studies

How LG Uplus is building better AI customer service agents with evaluation-driven development

How LG Uplus uses Arize AX to build evaluation-driven AI contact center agents — combining production traces, user…

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Case Studies

How Tripadvisor is building the AI product development lifecycle for agentic travel

Tripadvisor VP of Data and AI Rahul Todkar on building a production AI lifecycle for traditional ML and…

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Compare

How Arize stacks up.

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

Arize vs. Braintrust

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

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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.

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Arize vs. LangSmith

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

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Videos & talks

Demos, workshops & conference talks.

Watch on YouTube

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.

Rise of the AI Engineer 2:36
Answer

Why do capable AI agents still fail in production?

Short answer

A capable model can still fail when the system around it supplies incomplete context, exposes the wrong tool, loses state, mishandles a dependency, or permits an unsafe action. Evaluate the full agent trajectory so you can separate model limitations from failures in the harness, tools, infrastructure, and runtime controls.

Why it matters

Stronger models can recover from weak systems and still produce plausible outputs. Output-only evaluation may record a success while retry loops, stale data, rising cost, latency, or policy violations remain hidden.

The framework

  1. 1 Define the user outcome.
  2. 2 Instrument the full trajectory.
  3. 3 Inspect representative traces.
  4. 4 Classify recurring failures.
  5. 5 Build product-specific evals.
  6. 6 Add deterministic checks.
  7. 7 Replay candidate fixes.
  8. 8 Release, monitor, and refresh.
White paper

The agent reliability gap

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Don’t ship vibes.

Arize gives AI teams observability and evals to understand and improve agent performance.

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