Arize AI

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. What do you do now? appeared first on Arize AI .

Hamel Husain explains why the best AI teams treat LLM judges like classifiers, not dashboards. The post AI evals are a data science problem: What most teams get wrong appeared first on Arize AI .

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. The post Trace and evaluate TrueFoundry AI Gateway traffic in Arize AX appeared first on Arize AI .

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-horizon agent benchmarks are fragmenting: a field guide to what each one actually measures appeared first on Arize AI .

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 observability plugs into the one they picked. OpenInference defines the span vocabulary, the The post Project Rosetta Stone: a reference implementation for instrumenting agents in any framework appeared first on Arize AI .

Token spend does not prove AI is creating value. Teams need cost-per-outcome metrics that connect AI usage to resolved tickets, accepted code, shipped features, and other business results. The post Why AI token costs don’t tell you if your AI is working appeared first on Arize AI .

An AI engineering agent built into Phoenix. It works like a coding agent, just point it at your telemetry instead of a source tree. The post Meet PXI: the AI engineering agent inside Phoenix appeared first on Arize AI .

Agent harnesses are replacing frameworks as the real product surface for reliable AI agents, shifting the work from prompt tuning to loops, tools, traces, evals, and operational metrics. The post What is an agent harness? Why harnesses are replacing agent frameworks appeared first on Arize AI .

Anthropic and OpenAI both shipped 'dreaming' for AI memory in May and June 2026, and they built opposite architectures. A look at what each lab shipped, what the empirical literature says, and what to do if you are building memory for your own agent. The post Two labs started dreaming, and they built two different architectures appeared first on Arize AI .

Agent orchestration is not one problem. It spans expression, runtime, and observability, and separating those layers clarifies how teams should build, run, and improve production agents. The post What is agent orchestration? Frameworks, runtimes, and observability explained appeared first on Arize AI .

Material Event filed 2026-06-16