Does being rude to AI work? What 6 models revealed
Does being rude to AI improve answers or lower costs? We tested six models to separate prompt tone from brevity instructions and examine what changes.
16 min read
Together AI integration: Evaluate open models and optimize evaluation costs in Arize AX
Evaluate open models with Together AI in Arize AX. Compare model performance, run LLM-as-a-Judge evaluations, and explore lower-cost alternatives.
4 min read
How to test agent tool descriptions with Harbor and Phoenix
Build a reproducible benchmark, compare tool use across agents, and test whether a description change reduces unnecessary work while preserving outcomes.
11 min readBenchmarks, agent evaluations, and experiments.
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Does being rude to AI work? What 6 models revealed
Does being rude to AI improve answers or lower costs? We tested six models to separate prompt tone from brevity instructions and examine what changes.
October 13, 2026 16 min read
Decision model benchmark: Jev, Kev, Liquid d1, and more
On 15 September, TypeSafe released Jev, and within a few weeks about 10 companies had shipped something that works the same way. OpenAI, Liquid, Cloudflare, AWS and PostHog…
October 7, 2026 13 min read
Prompt caching benchmark: high cache reuse doesn’t always mean lower cost
We benchmarked prompt caching across DeepSeek, GLM, GPT, and Claude using Harbor evals and Phoenix traces, so we could compare cache reuse, estimated cost, and latency on the…
October 2, 2026 7 min readDemos, workshops, and conference talks.
See all on YouTube
The Model Knew What to Say. The Harness Failed. | Laurie Voss
Laurie Voss (Head of Developer Relations at Arize AI) breaks down why the agent harness, specifically code-level guardrails, is the most critical boundary layer in modern software engineering.
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See allHow 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 PII redaction to latency monitors and…
Read moreHow 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 feedback, and domain expertise to continuously improve customer service for…
Read moreHow 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 agentic travel products with unified observability, traces, evals, and governance…
Read moreCatch up on everything you missed.
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Agent EvaluationAlignment is a human-AI workflow: how Custom Views make agent review possible
Automated evals can score every agent session, but they do not automatically reflect what your team considers a good outcome. That takes human judgment. Dheeraj Bandaru October 9, 2026 7 min read -
Agent EngineeringCost Agent: find where your LLM and agent spend goes with your traces
AI costs don’t always scale predictably with traffic. A retry loop, oversized context window, or unnecessarily expensive model can make a small number of traces disproportionately expensive. Fuad Ali Sakshi Gupta October 8, 2026 3 min read -
Agent EvaluationWhat agent traces can tell you without an LLM judge
Some agent failures are already in the trace. tracelint proves them on Phoenix OpenInference spans and fails CI, without an LLM judge. Ashwin Govind Ugale October 6, 2026 8 min read -
Agent EvaluationHow we benchmark AI agents and tools with Harbor and Arize Phoenix
See how Arize uses Harbor and Phoenix to benchmark AI agents, MCP servers, skills, and CLIs in reproducible sandboxes, then inspect traces and annotations. Elizabeth Hutton October 5, 2026 5 min read -
Agent EngineeringIntroducing Arize AX MCP: When to use MCP, CLI, or skills
MCP, CLI, and skills are three ways to extend an agent with the same platform. The question is no longer which one wins. It is where the agent… Fuad Ali Dirk Brand Sragvi Vadali October 2, 2026 11 min read
Don’t ship vibes.
Arize gives AI teams observability and evals to understand and improve agent performance.
