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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 5 min read August 2026
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Guide

Agent reliability: how to measure and improve AI agents in production

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Guide

6 best agent engineering tools in 2026: A practical comparison

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Guide

8 best agent orchestration tools in 2026: frameworks and durable runtimes compared

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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

8 continual learning tools for AI agents, compared by the layer they own

Compare eight continual learning tools for AI agents across tracing, evaluation, prompt optimization, model training, release controls, pricing,…

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Guide

What is swarm management for AI agents?

Learn how agent swarm management gives AI agent fleets durable identity, concurrency controls, permissions, recovery, delivery, and fleet-level…

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Guide

Continual learning for AI agents and LLM systems: A developer guide

Continual learning for AI agents turns production traces and feedback into verified prompt, retrieval, harness, and model updates.…

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Guide

Arize alternatives: How the top AI observability and evaluation tools compare

Compare Arize alternatives including LangSmith, Langfuse, Braintrust, Helicone, and Fiddler on evals, tracing, self-hosting, and production depth. See…

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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 5 chapters
From the blog

The latest field notes and frameworks.

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Blog

How Signal found two hidden retry loops in our production agent Alyx

We ran Signal on Alyx, the AI engineering agent built into Arize AX. It surfaced a duplicate task-state…

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Blog

Arize Phoenix has a built-in MCP server that lets your agents query traces with SQL

Read-only SQL and code mode let coding agents answer questions across your traces without paging thousands of spans…

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Blog

Why better models don’t fix every agent failure: Lessons from OpenAI

In this installment of Rise of the AI Engineer, Stuart Sy from OpenAI, explains why the bottleneck has…

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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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Arize Signal vs. LangSmith Engine vs. Braintrust Topics

How each analyzes production traces, diagnoses failures, and closes the agent improvement loop.

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Breaking down AI observability pricing

Compare AI observability pricing across LangSmith, Langfuse, Braintrust, Datadog, and Arize.

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7 best AI agent evaluation platforms compared for 2026

Compare Arize AX, Phoenix, LangSmith, Braintrust, Langfuse, W&B Weave, and Comet Opik.

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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 breaks. Agents depend on context, tools, state, infrastructure, evaluation, and runtime controls. A strong model may even work around defects and return a plausible answer, making the system look healthier than it is.

Why it matters

The final answer only tells you part of the story. A successful output can hide bad retrieval, unnecessary retries, broken tool calls, lost state, rising cost, or unsafe actions. To understand reliability, teams need enough trace evidence to reconstruct the full trajectory and see how the agent reached its result.

The agent reliability feedback loop

  1. 1 Instrument complete traces
  2. 2 Inspect representative runs
  3. 3 Classify recurring failures
  4. 4 Turn failures into evals
  5. 5 Bring domain experts into review
  6. 6 Replay fixes, release, and monitor
Report

The agent reliability gap

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