Knowledge catalogue
Public benchmarks can show how a model performs in general. Production reliability depends on the context and harness around it, which only your team can evaluate against its own data, workflows, and
Orchestrator-executor just became the smart default for production agents: an expensive model plans, cheap models execute, and cost per completed task decides the roster. The post How cheap models cha
Traditional APM can collect every span and still leave developers guessing about intent, causality, and drift. As agents multiply, the observability stack must learn to interpret the systems it watche
Learn how Arize Signal turns production traces into ranked issues, proposed fixes, regression datasets, and reviewable pull requests for AI agents. The post How to debug production AI agents with Sign
Signal, a managed agent built into Arize AX, continuously reviews production traces, surfaces ranked issues with evidence and proposed fixes, and — with Managed Agents — can carry investigations into
Learn how to build trustworthy AI agent evals using regression tests, capability evals, production traces, LLM judges, and reproducible environments. The post AI agent evaluation: Tips from Anthropic
A skill cut agent costs by 44% and latency by 56%, but also reduced answer completeness. Here’s how tracing, evals, and a long-running agent exposed and corrected the regression. The post How to impro
How Booking.com built a unified AI observability stack with Arize for agentic GenAI workflows and traditional ML — from telemetry collection and PII redaction to latency monitors and evaluations. The
Three recent studies show what actually makes an AI agent skill effective: human expertise, compact procedures, tight routing, harness-specific testing, and eval-gated changes—not longer Markdown. The
Arize and Fireworks benchmarked 10 AI models across 2,400 agent runs. Learn why cost per successful task beats token price for model evaluation and routing. The post Cost per successful task: Benchmar
No single metric proves an LLM judge is trustworthy. This field guide shows how to measure human–human agreement, compare it to LLM–human agreement, and diagnose errors with precision, recall, and F1.
At ChatGPT scale, user frustration arrives as support tickets, ratings, social posts, and corrections buried inside conversations. OpenAI built a feedback system that can find the pattern behind a com
As background agents take on more implementation work, Cursor is rebuilding the software development lifecycle around risk scores, developer-like environments, video evidence, and review systems that
Use Arize Skills with Kiro CLI to trace coding-agent changes, build datasets from failures, run experiments, and validate prompts before shipping. The post Kiro CLI observability: trace and evaluate a
At Observe 2026, Jason Lopatecki and Aparna Dhinakaran described the shift from human-operated agent development to systematic agent improvement—and what builders should change in their stacks first.
AI productivity is best measured by connecting AI usage to validated downstream outcomes. Tokens, prompts, and generated lines show activity, but they do not prove value. A better measurement model tr
Microsoft published a 109-page technical report on MAI-Thinking-1. Here’s the abbreviated version of how a modern lab actually trains a frontier reasoning model — from scraping the web to reinforcemen
A local coding agent, an in-app customer assistant, and an AI SRE triaging production logs may all use the same model class—but not the same harness, eval plan, or rollout risk. Mastra CEO Sam Bhagwat
The AI engineering world is using “loop” to describe several different agent architectures. This post maps execution loops, task loops, product loops, system loops, and the human oversight loop that c
Kubernetes is strong for long-lived services, but it is often a poor default for short-lived agent sandboxes. Trace sandbox creation, tool execution, eval latency, and full trajectory time before you
WorkOS founder Michael Grinich explains why the next era of AI engineering depends on the systems around agents: identity, permissions, evals, memory, and feedback loops that keep autonomous software
If you're struggling to get started with evals, you're not alone. This post explains how to write LLM evals as ordinary tests in CI with Phoenix, pytest, and Vitest/Jest. The post Evals in CI: How to
As models become cheaper and more interchangeable, the durable advantage shifts to the agent harness: the loop, tools, memory, permissions, and workflow you can own and refine. The post Own the loop:
Agent evals are repeatable tests that score whether AI agents completed a task correctly. Learn how to design rubrics, test suites, and trace-based evals that catch failures and prevent reward hacking
Flat-rate AI plans are subsidizing agentic workloads. Learn why LLM inference costs are moving to metered pricing and how evals reveal cost per successful task. The post Model subsidies are ending. Wh
Learn how TrueFoundry AI Gateway exports OpenTelemetry traces to Arize AX so teams can trace, evaluate, and monitor production LLM and agent traffic without embedding a vendor SDK in every service. Th
A field guide to the new wave of long-horizon agent benchmarks: what each one actually measures, the realism-versus-verifiability bargain it strikes, and the seam where its score leaks. The post Long-
We've fielded the same question at every conference this year. An engineer has chosen a framework, CrewAI one week, LangGraph the next, Mastra the week after, and wants to see exactly how observabilit
Arize reduced median support resolution time from 22 hours to roughly 2.5 hours by building AI-native internal workflows for context gathering, debugging, escalation, and continuous improvement. The p
In May 2026, a malicious version of a popular VS Code extension spent 18 minutes in the marketplace before anyone caught it. In that time it ran on roughly 6,000... The post How to detect credential t
Phoenix crossed 10,000 GitHub stars. Here is how the open-source AI observability project grew from a Jupyter notebook extension into a community-shaped platform for traces, evals, OpenInference, and
Arize AX is adding managed agents, full-agent experimentation, expanded multimodal support, and Harness-as-a-Judge to help teams observe, evaluate, and improve production agents. The post Building the
Microsoft's open trust stack for AI agents puts ASSERT and Agent Control Specification on top of OpenInference, connecting evaluation, runtime controls, and observability through a shared trace contra
Models got smart enough to cheat their benchmarks, and outcome-only scores stopped measuring what we thought they measured. The fix, full trace analysis, is the same methodology production AI teams ha
Fine-tuning isn't dead, but the way most teams iterate on AI products has split in two. A tiny fraction run continuous RL against their own environments; everyone else has moved the iteration loop out
Hermes from NousResearch is a strong open-source agent harness. This post examines how its runtime loop, context management, tool scoping, session infrastructure, and orchestration patterns map to a m
A practical comparison of production AI evaluation harnesses, including what to look for across instrumentation, evaluators, online evals, CI gates, and agent workflows. The post The best eval harness
Agents are easy to prototype and hard to improve. A repeatable loop of traces, evals, failed-span inspection, and targeted harness changes makes agent behavior easier to debug and improve. The post Ho
Production AI traces are the raw material for better evals, prompts, datasets, and fine-tuned models. This post shows how the Arize AX Airflow Provider turns that feedback loop into scheduled, monitor
Most production AI features don't need a frontier model. Here's how capability evals and prompt engineering can help ship a local SLM that matches frontier-model quality with lower latency and cost. T
Learn how to design, calibrate, and run LLM-as-a-judge evaluators with fixed labels, human agreement checks, trace context, and Phoenix Evals. The post How to build LLM-as-a-Judge evaluators that hold
Model swaps look like configuration changes, but they behave more like product migrations. A new model may be cheaper, faster, easier to get capacity for, or stronger on public benchmarks.... The post
You can build a measurably better agent from data you already have, without retraining a thing. The data is what your experienced humans do when they correct the AI. Capture... The post Building a sel
Announcing coding harness tracing for observing, evaluating, and improving coding agent workflows across Claude Code, Cursor, Codex, GitHub Copilot, and Gemini CLI. The post Coding agent tracing and e
How Arize uses Alyx to debug Alyx: searching dense traces, aggregating failures, triaging dogfooding issues, and closing the AI engineering feedback loop. The post How we use Alyx to build Alyx: How t
A year ago, frontier models started losing track of instructions somewhere around 200–300 simultaneous constraints. With 2026 models, that ceiling is closer to 2,000 — an order-of-magnitude jump. We r
As agents start changing software, they need a way to verify their work that includes traces, evals, feedback, and APIs. This is where Phoenix goes next — not the next release, but what this product b
A benchmark-driven look at why agent harnesses need adaptive finish logic as model behavior changes across Claude, GPT-4o, and Gemma. The post Agent harnesses have an expiration date appeared first on
AI agents require specialized testing and debugging approaches that differ from traditional software due to their non-deterministic behavior and complex decision-making processes. This entry likely co
As we have built our own harness management tools internally at Arize, and watched external systems like Devin @cognition start managing other Devins, managed agents at @AnthropicAI and long running T
An evaluation harness is the standardized infrastructure that decides what gets evaluated, runs the evaluation, and acts on the result. The post What is an evaluation harness? appeared first on Arize
Twitter said pick a side. The eval said the question was wrong. Six months ago, MCP (model context protocol) was the hot new thing: tool usage with a built-in discovery... The post MCP vs. CLI Skills
Enterprise agents are moving from demos into production workflows, which creates a basic problem: teams need to understand what those agents actually did. The post Why agent telemetry needs standards
This post was written in April 2026. Cloud products, feature maturity, and recommended patterns change over time, so readers should treat these examples as directional guidance. For teams already usin
Enterprise software is on the verge of its first compounding data loop, the same kind of self-reinforcing mechanism that built the most valuable consumer businesses of the last twenty years.... The po
A version of this article originally appeared on X. Every agent harness runs into the same limit: the context window is too small for everything the model might want to remember.... The post Context m
A version of this article originally appeared on X. Someone asked me at a hacker event last week: “Can anyone actually tell me what a harness really is?” It was... The post What is an agent harness? a
Building an AI agent has never been easier. But getting one into production that’s reliable is still hard. Most teams can ship a working demo in a day. The agent... The post Beyond models: How context
Coding agents can update prompts, wire in tools, and change application logic across your codebase in a single run. The hard part isn’t getting the agent to make changes, but... The post How to add an
Keynotes at Europe’s first flagship AI Engineer Conference shared one theme: code generation has accelerated past our ability to verify it, and the industry is quietly reorganizing around that fact...