Everything we’ve published — page 8.
Microsoft’s open trust stack runs on OpenInference
Microsoft's open trust stack for AI agents puts ASSERT and Agent Control Specification on top of OpenInference, connecting…
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The end of fine-tuning: Why evals, context, and traces matter more
Fine-tuning isn't dead, but the way most teams iterate on AI products has split in two. A tiny…
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AI benchmarks are breaking. Trace analysis is what comes next.
Models got smart enough to cheat their benchmarks, and outcome-only scores stopped measuring what we thought they measured.…
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Prompt Playground
A prompt playground offers a UI to experiment with prompt templates, input variables, LLM models and LLM parameters.…
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How Hermes implements an open source agent harness architecture
Hermes from NousResearch is a strong open-source agent harness. This post examines how its runtime loop, context management,…
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The best eval harness for production AI and agents: A comparison
A practical comparison of production AI evaluation harnesses, including what to look for across instrumentation, evaluators, online evals,…
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How to build a better agent harness with traces and evals
Agents are easy to prototype and hard to improve. A repeatable loop of traces, evals, failed-span inspection, and…
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From production traces to better AI agents: Automating the LLMOps feedback loop
Production AI traces are the raw material for better evals, prompts, datasets, and fine-tuned models. This post shows…
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LangSmith alternatives for AI observability and LLM evaluation
Compare the top LangSmith alternatives for AI observability and evaluation, focusing on tools that support framework-agnostic tracing, production…
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How to ship a local LLM that matches frontier LLMs with evals and prompt engineering
Most production AI features don't need a frontier model. Here's how capability evals and prompt engineering can help…
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Langfuse alternatives for LLM observability and AI evaluation
Compare the top Langfuse alternatives for LLM observability and evaluation, focusing on tools that support production monitoring, enterprise…
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How to build LLM-as-a-Judge evaluators that hold up in production
Learn how to design, calibrate, and run LLM-as-a-judge evaluators with fixed labels, human agreement checks, trace context, and…
Read the postDon’t ship vibes.
Arize gives AI teams observability and evals to understand and improve agent performance.
