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    <title>Evals guides · The Tech Gazette</title>
    <link>https://techgazette.com.br/learn/evals/</link>
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    <description>New guides on evals, by the people who build it. Measuring what a model or agent does.</description>
    <language>en</language>
    <ttl>180</ttl>
    <lastBuildDate>Fri, 16 Oct 2026 20:48:00 GMT</lastBuildDate>
    <item>
      <title>Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore</title>
      <link>https://aws.amazon.com/blogs/machine-learning/building-trust-in-ai-agents-a-production-ready-evaluation-framework-with-amazon-bedrock-agentcore/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/building-trust-in-ai-agents-a-production-ready-evaluation-framework-with-amazon-bedrock-agentcore/</guid>
      <pubDate>Fri, 16 Oct 2026 20:48:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>Evals</category>
      <description>AWS Machine Learning — Autonomous AI agents fail softly: latency stays flat while answer quality quietly erodes. This post shares a nine-stage evaluation framework, built on Amazon Bedrock AgentCore, that catches silent quality regressions acr…</description>
    </item>
    <item>
      <title>How to plan your migration to the AWS Region in Saudi Arabia</title>
      <link>https://aws.amazon.com/blogs/migration-and-modernization/how-to-plan-your-migration-to-the-aws-region-in-saudi-arabia/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/migration-and-modernization/how-to-plan-your-migration-to-the-aws-region-in-saudi-arabia/</guid>
      <pubDate>Thu, 15 Oct 2026 14:22:00 GMT</pubDate>
      <dc:creator>AWS Migration &amp; Modernization</dc:creator>
      <category>Evals</category>
      <description>AWS Migration &amp; Modernization — The first AWS Region in Saudi Arabia is on track for December 2026. Learn which workloads to evaluate, what data to prepare, and how an AWS Optimization and Licensing Assessment helps you compare costs before you commit.</description>
    </item>
    <item>
      <title>Deploy and evaluate models with Amazon SageMaker JumpStart</title>
      <link>https://aws.amazon.com/blogs/machine-learning/deploy-and-evaluate-models-with-amazon-sagemaker-jumpstart/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/deploy-and-evaluate-models-with-amazon-sagemaker-jumpstart/</guid>
      <pubDate>Wed, 14 Oct 2026 13:14:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>Evals</category>
      <description>AWS Machine Learning — Learn how to deploy an available model from Amazon SageMaker JumpStart, run a lightweight and repeatable performance test using the SageMaker Python SDK, and delete the endpoint when testing is complete.</description>
    </item>
    <item>
      <title>EU AI Act compliance for AI agents: a practical engineering guide</title>
      <link>https://arize.com/blog/eu-ai-act-compliance-ai-agents/</link>
      <guid isPermaLink="true">https://arize.com/blog/eu-ai-act-compliance-ai-agents/</guid>
      <pubDate>Tue, 13 Oct 2026 16:00:00 GMT</pubDate>
      <dc:creator>Arize</dc:creator>
      <category>Evals</category>
      <description>Arize — Learn how agent traces, human oversight, production evaluations, and log retention support EU AI Act compliance with a practical credit-assistant example. The post EU AI Act compliance for AI agents: a practical engineer…</description>
    </item>
    <item>
      <title>Build long-horizon agents with Grok 4.7 on Amazon Bedrock</title>
      <link>https://aws.amazon.com/blogs/machine-learning/build-long-horizon-agents-with-grok-4-7-on-amazon-bedrock/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/build-long-horizon-agents-with-grok-4-7-on-amazon-bedrock/</guid>
      <pubDate>Tue, 13 Oct 2026 15:52:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>Evals</category>
      <description>AWS Machine Learning — Grok 4.7 is now available on Amazon Bedrock. In this post, we build a tool-calling agent on Grok 4.7 and evaluate it on three reasoning tasks at four reasoning effort levels, measuring correctness and token cost per comp…</description>
    </item>
    <item>
      <title>Tutorial: Benchmarking GPT-6 Astra vs Claude Fable 5.1 vs GPT-5.6 Sol using W&amp;B Weave</title>
      <link>https://wf.coreweave.com/blog/tutorial-benchmarking-gpt-6-astra-vs-claude-fable-5-1-vs-gpt-5-6-sol-using-w-b-weave</link>
      <guid isPermaLink="true">https://wf.coreweave.com/blog/tutorial-benchmarking-gpt-6-astra-vs-claude-fable-5-1-vs-gpt-5-6-sol-using-w-b-weave</guid>
      <pubDate>Tue, 13 Oct 2026 14:47:00 GMT</pubDate>
      <dc:creator>CoreWeave</dc:creator>
      <category>Evals</category>
      <description>CoreWeave — Learn how to benchmark GPT-6 Astra, Claude Fable 5.1, and GPT-5.6 Sol with Weave, comparing model quality, latency, cost, and performance across practical evaluation tasks.</description>
    </item>
    <item>
      <title>Azure Content Understanding Prebuilt Analyzers: packaging domain knowledge for document AI</title>
      <link>https://devblogs.microsoft.com/foundry/more-than-a-schema-content-understanding-prebuilt-analyzers/</link>
      <guid isPermaLink="true">https://devblogs.microsoft.com/foundry/more-than-a-schema-content-understanding-prebuilt-analyzers/</guid>
      <pubDate>Tue, 13 Oct 2026 14:20:00 GMT</pubDate>
      <dc:creator>Microsoft Foundry</dc:creator>
      <category>Evals</category>
      <description>Microsoft Foundry — Learn how Azure Content Understanding prebuilt analyzers combine domain knowledge with grounding and confidence, and what Advanced Contextualization preview benchmarks show about quality, modeled cost, and latency. The p…</description>
    </item>
    <item>
      <title>How to test agent tool descriptions with Harbor and Phoenix</title>
      <link>https://arize.com/blog/how-to-test-agent-tool-descriptions-with-harbor-and-phoenix/</link>
      <guid isPermaLink="true">https://arize.com/blog/how-to-test-agent-tool-descriptions-with-harbor-and-phoenix/</guid>
      <pubDate>Mon, 12 Oct 2026 14:30:00 GMT</pubDate>
      <dc:creator>Arize</dc:creator>
      <category>Evals</category>
      <description>Arize — Build a reproducible benchmark, compare tool use across agents, and test whether a description change reduces unnecessary work while preserving outcomes. The post How to test agent tool descriptions with Harbor and Phoen…</description>
    </item>
    <item>
      <title>Alignment is a human-AI workflow: how Custom Views make agent review possible</title>
      <link>https://arize.com/blog/alignment-is-a-human-ai-workflow-custom-views/</link>
      <guid isPermaLink="true">https://arize.com/blog/alignment-is-a-human-ai-workflow-custom-views/</guid>
      <pubDate>Fri, 09 Oct 2026 18:25:00 GMT</pubDate>
      <dc:creator>Arize</dc:creator>
      <category>Evals</category>
      <description>Arize — Automated evals can score every agent session, but they do not automatically reflect what your team considers a good outcome. That takes human judgment. The post Alignment is a human-AI workflow: how Custom Views make ag…</description>
    </item>
    <item>
      <title>ICYMI: What landed for AI builders in September 2026</title>
      <link>https://aws.amazon.com/blogs/machine-learning/icymi-what-landed-for-ai-builders-in-september-2026/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/icymi-what-landed-for-ai-builders-in-september-2026/</guid>
      <pubDate>Fri, 09 Oct 2026 15:38:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>Evals</category>
      <description>AWS Machine Learning — A monthly recap of the latest Amazon Bedrock, Amazon Bedrock AgentCore, and Strands updates from September 2026: broader model choice, faster serverless agents with built-in evaluation, and automated knowledge base synci…</description>
    </item>
    <item>
      <title>Game-Based Benchmarks for LLMs: How Models Behave Beyond the Leaderboard</title>
      <link>https://www.digitalocean.com/community/tutorials/game-based-benchmarks</link>
      <guid isPermaLink="true">https://www.digitalocean.com/community/tutorials/game-based-benchmarks</guid>
      <pubDate>Fri, 09 Oct 2026 12:00:00 GMT</pubDate>
      <dc:creator>DigitalOcean Tutorials</dc:creator>
      <category>Evals</category>
      <description>DigitalOcean Tutorials — What game-based LLM benchmarks measure, where they work and where they fall short, how models differ in games like poker and crisis simulations, and how to use those behavioral results alongside capability benchmarks whe…</description>
    </item>
    <item>
      <title>How to Manage Generative AI Risk With NIST AI 600-1, in Six Steps</title>
      <link>https://www.anaconda.com/blog/nist-ai-600-1-generative-ai-profile-guide</link>
      <guid isPermaLink="true">https://www.anaconda.com/blog/nist-ai-600-1-generative-ai-profile-guide</guid>
      <pubDate>Thu, 08 Oct 2026 16:19:00 GMT</pubDate>
      <dc:creator>Anaconda</dc:creator>
      <category>Evals</category>
      <description>Anaconda — A practical guide to NIST AI 600-1, the Generative AI Profile: the 12 risks it names, the actions it suggests, and how to put red teaming to work. The post How to Manage Generative AI Risk With NIST AI 600-1, in Six Step…</description>
    </item>
    <item>
      <title>Your skills need an evaluation mechanism</title>
      <link>https://expo.dev/blog/your-skills-need-an-evaluation-mechanism</link>
      <guid isPermaLink="true">https://expo.dev/blog/your-skills-need-an-evaluation-mechanism</guid>
      <pubDate>Thu, 08 Oct 2026 13:30:00 GMT</pubDate>
      <dc:creator>Expo</dc:creator>
      <category>Evals</category>
      <description>Expo — How Expo tests whether coding agents actually find and use its skills, with an eval harness in CI and lessons from catalog truncation.</description>
    </item>
    <item>
      <title>How to Find Real Failures in AI Agents with Signals and Test Them with Evaluations</title>
      <link>https://www.digitalocean.com/community/tutorials/agent-observability-evaluation-signals</link>
      <guid isPermaLink="true">https://www.digitalocean.com/community/tutorials/agent-observability-evaluation-signals</guid>
      <pubDate>Thu, 08 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>DigitalOcean Tutorials</dc:creator>
      <category>Evals</category>
      <description>DigitalOcean Tutorials — Signals flags the AI agent conversations that went wrong. It watches only the agents you turn it on for, hides personal details, keeps the data in your region, and never uses it to train AI for anyone else. It found all…</description>
    </item>
    <item>
      <title>Part 4: Safety and governance for LLM systems: guardrails, PII, audit, and memory</title>
      <link>https://stackoverflow.blog/2026/10/07/part-4-safety-and-governance-for-llm-systems-guardrails-pii-audit-and-memory/</link>
      <guid isPermaLink="true">https://stackoverflow.blog/2026/10/07/part-4-safety-and-governance-for-llm-systems-guardrails-pii-audit-and-memory/</guid>
      <pubDate>Wed, 07 Oct 2026 20:48:00 GMT</pubDate>
      <dc:creator>Stack Overflow Blog</dc:creator>
      <category>Evals</category>
      <description>Stack Overflow Blog</description>
    </item>
    <item>
      <title>Part 2: Evals as a deployment gate — and how to know when they drift</title>
      <link>https://stackoverflow.blog/2026/10/07/evals-as-a-deployment-gate-and-how-to-know-when-they-drift/</link>
      <guid isPermaLink="true">https://stackoverflow.blog/2026/10/07/evals-as-a-deployment-gate-and-how-to-know-when-they-drift/</guid>
      <pubDate>Wed, 07 Oct 2026 20:31:00 GMT</pubDate>
      <dc:creator>Stack Overflow Blog</dc:creator>
      <category>Evals</category>
      <description>Stack Overflow Blog</description>
    </item>
    <item>
      <title>Azure Document Intelligence and Azure Content Understanding: a practical guide</title>
      <link>https://devblogs.microsoft.com/foundry/choosing-azure-document-intelligence-and-content-understanding/</link>
      <guid isPermaLink="true">https://devblogs.microsoft.com/foundry/choosing-azure-document-intelligence-and-content-understanding/</guid>
      <pubDate>Wed, 07 Oct 2026 18:45:00 GMT</pubDate>
      <dc:creator>Microsoft Foundry</dc:creator>
      <category>Evals</category>
      <description>Microsoft Foundry — Compare Azure Document Intelligence and Azure Content Understanding across extraction architectures, workload fit, Layout capabilities, deployment options, and practical evaluation criteria. The post Azure Document Intel…</description>
    </item>
    <item>
      <title>AI Code Reviewers vs. Guardrail Engines: Where each fits</title>
      <link>https://earthly.dev/blog/ai-code-reviewers-vs-guardrail-engines/</link>
      <guid isPermaLink="true">https://earthly.dev/blog/ai-code-reviewers-vs-guardrail-engines/</guid>
      <pubDate>Wed, 07 Oct 2026 04:00:00 GMT</pubDate>
      <dc:creator>Earthly</dc:creator>
      <category>Evals</category>
      <description>Earthly — Your AI reviewer caught it. Next week, the same violation shows up in another repo. Across different tech stacks and custom workflows, how do you know the standard is being checked everywhere? Explore where AI reviewers…</description>
    </item>
    <item>
      <title>How we replaced our host vulnerability scanner with the Datadog Agent</title>
      <link>https://www.datadoghq.com/blog/how-we-replaced-our-host-vulnerability-scanner-with-the-datadog-agent/</link>
      <guid isPermaLink="true">https://www.datadoghq.com/blog/how-we-replaced-our-host-vulnerability-scanner-with-the-datadog-agent/</guid>
      <pubDate>Wed, 07 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>Datadog</dc:creator>
      <category>Evals</category>
      <description>Datadog — Learn how Datadog evaluated Agent-based host vulnerability scanning, compared detection results, and preserved coverage and audit reporting during the migration.</description>
    </item>
    <item>
      <title>Control investigation costs before they start with AWS DevOps Agent</title>
      <link>https://aws.amazon.com/blogs/mt/control-investigation-costs-before-they-start-with-aws-devops-agent/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/mt/control-investigation-costs-before-they-start-with-aws-devops-agent/</guid>
      <pubDate>Tue, 06 Oct 2026 14:22:00 GMT</pubDate>
      <dc:creator>AWS Cloud Operations</dc:creator>
      <category>Evals</category>
      <description>AWS Cloud Operations — Introduction Before an AWS DevOps Agent investigation runs its first billable query, you can see what it will cost and decide whether to proceed. The Investigation Cost Guardrail skill brings cost awareness to AWS DevOps…</description>
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    <item>
      <title>What agent traces can tell you without an LLM judge</title>
      <link>https://arize.com/blog/agent-traces-without-llm-judge/</link>
      <guid isPermaLink="true">https://arize.com/blog/agent-traces-without-llm-judge/</guid>
      <pubDate>Tue, 06 Oct 2026 14:00:00 GMT</pubDate>
      <dc:creator>Arize</dc:creator>
      <category>Evals</category>
      <description>Arize — Some agent failures are already in the trace. tracelint proves them on Phoenix OpenInference spans and fails CI, without an LLM judge. The post What agent traces can tell you without an LLM judge appeared first on Arize…</description>
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    <item>
      <title>Advancing computer use with Ironclad</title>
      <link>https://openai.com/index/advancing-computer-use-with-ironclad</link>
      <guid isPermaLink="true">https://openai.com/index/advancing-computer-use-with-ironclad</guid>
      <pubDate>Tue, 06 Oct 2026 10:00:00 GMT</pubDate>
      <dc:creator>OpenAI</dc:creator>
      <category>Evals</category>
      <description>OpenAI — Learn how OpenAI and Ironclad are training and evaluating AI agents on complex contracting workflows to advance computer use for professional work.</description>
    </item>
    <item>
      <title>Evaluating multi-agent systems for explainability and helpfulness with Amazon Bedrock AgentCore</title>
      <link>https://aws.amazon.com/blogs/machine-learning/evaluating-multi-agent-systems-for-explainability-and-helpfulness-with-amazon-bedrock-agentcore/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/evaluating-multi-agent-systems-for-explainability-and-helpfulness-with-amazon-bedrock-agentcore/</guid>
      <pubDate>Mon, 05 Oct 2026 15:50:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>Evals</category>
      <description>AWS Machine Learning — Multi-agent systems need deeper guarantees than fluent responses: they must select the right tools, respect constraints, and explain their decisions. Learn how to build a Strands-based multi-agent supply chain decisionin…</description>
    </item>
    <item>
      <title>How we benchmark AI agents and tools with Harbor and Arize Phoenix</title>
      <link>https://arize.com/blog/ai-agent-benchmarking-harbor-arize-phoenix/</link>
      <guid isPermaLink="true">https://arize.com/blog/ai-agent-benchmarking-harbor-arize-phoenix/</guid>
      <pubDate>Mon, 05 Oct 2026 14:19:00 GMT</pubDate>
      <dc:creator>Arize</dc:creator>
      <category>Evals</category>
      <description>Arize — See how Arize uses Harbor and Phoenix to benchmark AI agents, MCP servers, skills, and CLIs in reproducible sandboxes, then inspect traces and annotations. The post How we benchmark AI agents and tools with Harbor and Ar…</description>
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    <item>
      <title>The Hidden Line in the Footer: How to Stop AI Agents From Following Bad Instructions</title>
      <link>https://www.anaconda.com/blog/indirect-prompt-injection-ai-agents</link>
      <guid isPermaLink="true">https://www.anaconda.com/blog/indirect-prompt-injection-ai-agents</guid>
      <pubDate>Mon, 05 Oct 2026 14:16:00 GMT</pubDate>
      <dc:creator>Anaconda</dc:creator>
      <category>Evals</category>
      <description>Anaconda — A hidden line of text can trick an AI agent into leaking data or sending emails. Learn how indirect prompt injection works and how guardrails stop it in time. The post The Hidden Line in the Footer: How to Stop AI Agents…</description>
    </item>
    <item>
      <title>How Cresta turned CX expertise into an agent builder on the Claude Agent SDK</title>
      <link>https://claude.com/resources/articles/how-cresta-turned-cx-expertise-into-an-agent-builder-on-the-claude-agent-sdk</link>
      <guid isPermaLink="true">https://claude.com/resources/articles/how-cresta-turned-cx-expertise-into-an-agent-builder-on-the-claude-agent-sdk</guid>
      <pubDate>Mon, 05 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>Claude Blog</dc:creator>
      <category>Evals</category>
      <description>Claude Blog — See how Cresta built Conductor, an agent that builds other agents, on the Claude Agent SDK, and how the team evaluates it with every new Claude model.</description>
    </item>
    <item>
      <title>Scaling and benchmarking a critical message bus using a new indexing strategy</title>
      <link>https://blog.janestreet.com/scaling-and-benchmarking-a-critical-message-bus/</link>
      <guid isPermaLink="true">https://blog.janestreet.com/scaling-and-benchmarking-a-critical-message-bus/</guid>
      <pubDate>Mon, 05 Oct 2026 00:00:00 GMT</pubDate>
      <dc:creator>Jane Street Tech Blog</dc:creator>
      <category>Evals</category>
      <description>Jane Street Tech Blog — The following is part of a series of posts about 2026 summer intern projects—for more, see “What the interns have wrought, special jumbo 2026 edition”</description>
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    <item>
      <title>AI Model Management in Production: A Practical Workflow</title>
      <link>https://launchdarkly.com/blog/ai-model-management/</link>
      <guid isPermaLink="true">https://launchdarkly.com/blog/ai-model-management/</guid>
      <pubDate>Sat, 03 Oct 2026 13:55:00 GMT</pubDate>
      <dc:creator>LaunchDarkly</dc:creator>
      <category>Evals</category>
      <description>LaunchDarkly — Manage AI models, prompts, evaluations, rollouts, and production performance with a practical AI model management workflow.</description>
    </item>
    <item>
      <title>Prompt caching benchmark: high cache reuse doesn’t always mean lower cost</title>
      <link>https://arize.com/blog/prompt-caching-benchmark/</link>
      <guid isPermaLink="true">https://arize.com/blog/prompt-caching-benchmark/</guid>
      <pubDate>Fri, 02 Oct 2026 18:01:00 GMT</pubDate>
      <dc:creator>Arize</dc:creator>
      <category>Evals</category>
      <description>Arize — 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 same multi-turn shopping assistant agent. The…</description>
    </item>
    <item>
      <title>Why Open Models Can Win on Enterprise Cost and Control</title>
      <link>https://wf.coreweave.com/blog/why-open-models-can-win-on-enterprise-cost-and-control</link>
      <guid isPermaLink="true">https://wf.coreweave.com/blog/why-open-models-can-win-on-enterprise-cost-and-control</guid>
      <pubDate>Fri, 02 Oct 2026 14:12:00 GMT</pubDate>
      <dc:creator>CoreWeave</dc:creator>
      <category>Evals</category>
      <description>CoreWeave — See how enterprise teams cut inference costs and keep control of fine-tuning, distillation, and guardrails by running open models on their own infrastructure.</description>
    </item>
    <item>
      <title>Claude’s hillclimb loop for AI agents: start with production traces</title>
      <link>https://arize.com/blog/claude-hillclimb-production-traces/</link>
      <guid isPermaLink="true">https://arize.com/blog/claude-hillclimb-production-traces/</guid>
      <pubDate>Fri, 02 Oct 2026 13:30:00 GMT</pubDate>
      <dc:creator>Arize</dc:creator>
      <category>Evals</category>
      <description>Arize — Anthropic's Claude hillclimb loop designs evals and improves agents one change at a time. Here's how to run that loop against production traces in Arize AX. The post Claude’s hillclimb loop for AI agents: start with prod…</description>
    </item>
    <item>
      <title>Agentic workflows in production: Mechanisms, trade-offs, costs and design patterns</title>
      <link>https://wf.coreweave.com/blog/agentic-workflows-in-production-mechanisms-trade-offs-costs-and-design-patterns</link>
      <guid isPermaLink="true">https://wf.coreweave.com/blog/agentic-workflows-in-production-mechanisms-trade-offs-costs-and-design-patterns</guid>
      <pubDate>Thu, 01 Oct 2026 17:41:00 GMT</pubDate>
      <dc:creator>CoreWeave</dc:creator>
      <category>Evals</category>
      <description>CoreWeave — Explore how production agentic workflows are designed, orchestrated, evaluated, and governed, with practical guidance on patterns, trade-offs, costs, and failure modes.</description>
    </item>
    <item>
      <title>How Jev Compares to Other Rerankers</title>
      <link>https://www.lancedb.com/blog/how-jev-compares-to-other-rerankers</link>
      <guid isPermaLink="true">https://www.lancedb.com/blog/how-jev-compares-to-other-rerankers</guid>
      <pubDate>Thu, 01 Oct 2026 03:58:00 GMT</pubDate>
      <dc:creator>LanceDB</dc:creator>
      <category>Evals</category>
      <description>LanceDB — See how Jev performs in a benchmark of 19 reranker configurations across five datasets, covering accuracy, latency, and prompt-driven relevance for LanceDB search.</description>
    </item>
    <item>
      <title>MariaDB Foundation: Why Test Case Tags Matter in TAF</title>
      <link>https://mariadb.org/mariadb-foundation-why-test-case-tags-matter-in-taf/</link>
      <guid isPermaLink="true">https://mariadb.org/mariadb-foundation-why-test-case-tags-matter-in-taf/</guid>
      <pubDate>Wed, 30 Sep 2026 16:39:00 GMT</pubDate>
      <dc:creator>MariaDB Foundation</dc:creator>
      <category>Evals</category>
      <description>MariaDB Foundation — In performance testing, numbers alone rarely tell the full story. A benchmark result without context is just a result floating in space disconnected from the intent, and the conditions that produced it. … Continue readin…</description>
    </item>
    <item>
      <title>Terraform for agents: how we define our software factory in code</title>
      <link>https://www.warp.dev/blog/software-factory-as-code</link>
      <guid isPermaLink="true">https://www.warp.dev/blog/software-factory-as-code</guid>
      <pubDate>Wed, 30 Sep 2026 16:00:00 GMT</pubDate>
      <dc:creator>Warp</dc:creator>
      <category>Evals</category>
      <description>Warp — We define every agent in our software factory as code: orchestration, secrets, MCP servers, automations, runners, scorers, and benchmarks. Here’s a walkthrough of each part of the factory config.</description>
    </item>
    <item>
      <title>Building production-ready AI agents with Atlas Agent Engine and Arize AX</title>
      <link>https://arize.com/blog/using-arize-ax-with-mongodb-atlas-agent-engine/</link>
      <guid isPermaLink="true">https://arize.com/blog/using-arize-ax-with-mongodb-atlas-agent-engine/</guid>
      <pubDate>Wed, 30 Sep 2026 15:00:00 GMT</pubDate>
      <dc:creator>Arize</dc:creator>
      <category>Evals</category>
      <description>Arize — Agents built and deployed on MongoDB’s Atlas Agent Engine can export OpenTelemetry traces to Arize AX, where teams can inspect each run, evaluate both the outcome and execution path, and test fixes before redeploying. Th…</description>
    </item>
    <item>
      <title>Using Jev as a search reranker: benchmarks and how to implement</title>
      <link>https://www.elastic.co/search-labs/blog/ecommerce-search-reranking-llm-alternative-jev</link>
      <guid isPermaLink="true">https://www.elastic.co/search-labs/blog/ecommerce-search-reranking-llm-alternative-jev</guid>
      <pubDate>Wed, 30 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Elasticsearch Labs</dc:creator>
      <category>Evals</category>
      <description>Elasticsearch Labs — We had Jev score Elasticsearch hybrid search results and let a short Python policy do the reranking, taking nDCG@10 from 0.9351 to 0.9565 on 250 Amazon Shopping Queries, and the code is all here.</description>
    </item>
    <item>
      <title>Jev vs. LLM-as-a-Judge for AI Evals</title>
      <link>https://live-comet-marketing-site.pantheonsite.io/blog/jev-model-vs-llm-as-a-judge/</link>
      <guid isPermaLink="true">https://live-comet-marketing-site.pantheonsite.io/blog/jev-model-vs-llm-as-a-judge/</guid>
      <pubDate>Tue, 29 Sep 2026 22:11:00 GMT</pubDate>
      <dc:creator>Comet</dc:creator>
      <category>Evals</category>
      <description>Comet — A model that answers yes/no questions instead of writing text came out 3.5× cheaper and 3.8× faster than our gpt-4o-mini judge on 1,000 real production turns, and agreed with it on 897 of them. Here’s the test that showe…</description>
    </item>
    <item>
      <title>Integrating Claude Code with Auth0 APIs</title>
      <link>https://auth0.com/blog/integrating-claude-code-with-auth0-apis/</link>
      <guid isPermaLink="true">https://auth0.com/blog/integrating-claude-code-with-auth0-apis/</guid>
      <pubDate>Tue, 29 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Auth0</dc:creator>
      <category>Evals</category>
      <description>Auth0 — Pair Claude Code with the Auth0 MCP Server to audit JWT configs, scaffold M2M apps, and refactor auth code from the terminal — with scoped access and security guardrails.</description>
    </item>
    <item>
      <title>Why model versioning is not enough for production AI</title>
      <link>https://stackoverflow.blog/2026/09/28/why-model-versioning-is-not-enough-for-production-ai/</link>
      <guid isPermaLink="true">https://stackoverflow.blog/2026/09/28/why-model-versioning-is-not-enough-for-production-ai/</guid>
      <pubDate>Mon, 28 Sep 2026 18:24:00 GMT</pubDate>
      <dc:creator>Stack Overflow Blog</dc:creator>
      <category>Evals</category>
      <description>Stack Overflow Blog — An MLOps workflow for evaluating deploying and rolling back AI applications</description>
    </item>
    <item>
      <title>Can your Postgres survive a bad query?</title>
      <link>https://clickhouse.com/blog/can-your-postgres-survive-a-bad-query</link>
      <guid isPermaLink="true">https://clickhouse.com/blog/can-your-postgres-survive-a-bad-query</guid>
      <pubDate>Mon, 28 Sep 2026 15:37:00 GMT</pubDate>
      <dc:creator>ClickHouse</dc:creator>
      <category>Evals</category>
      <description>ClickHouse — How do Postgres providers handle a query that exhausts memory? A recursive query benchmark compares query failures and cluster survival across ClickHouse Managed Postgres, Cloud SQL, PlanetScale, and Amazon RDS.</description>
    </item>
    <item>
      <title>NarrateAI: production-ready LLM quality assurance on Amazon Bedrock</title>
      <link>https://aws.amazon.com/blogs/machine-learning/narrateai-production-ready-llm-quality-assurance-on-amazon-bedrock/</link>
      <guid isPermaLink="true">https://aws.amazon.com/blogs/machine-learning/narrateai-production-ready-llm-quality-assurance-on-amazon-bedrock/</guid>
      <pubDate>Fri, 25 Sep 2026 16:15:00 GMT</pubDate>
      <dc:creator>AWS Machine Learning</dc:creator>
      <category>Evals</category>
      <description>AWS Machine Learning — NarrateAI delivers production-ready LLM quality assurance on Amazon Bedrock. This post details five techniques—adaptive pipeline orchestration, cross-account multi-model failover, real-time streaming evaluation, composit…</description>
    </item>
    <item>
      <title>The Limitations of Running a Workload Generator In the Same JVM as the System-Under-Test</title>
      <link>https://inside.java/2026/09/25/limitations-of-running-a-workload-generator-in-the-same-jvm/</link>
      <guid isPermaLink="true">https://inside.java/2026/09/25/limitations-of-running-a-workload-generator-in-the-same-jvm/</guid>
      <pubDate>Fri, 25 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Inside Java</dc:creator>
      <category>Evals</category>
      <description>Inside Java — When you evaluate a system with a garbage collector and want to understand its tail latency, you often see SPECjbb2015 or DaCapo used. While both offer corrective measures for coordinated omission, only SPECjbb2015 can r…</description>
    </item>
    <item>
      <title>How to build a remote evaluator in Arize AX with Jev</title>
      <link>https://arize.com/blog/jev-remote-evaluator/</link>
      <guid isPermaLink="true">https://arize.com/blog/jev-remote-evaluator/</guid>
      <pubDate>Thu, 24 Sep 2026 18:38:00 GMT</pubDate>
      <dc:creator>Arize</dc:creator>
      <category>Evals</category>
      <description>Arize — Evaluate agent responses with Jev and Arize AX. Build a FastAPI remote evaluator, return labels and scores, and choose a threshold for your data. The post How to build a remote evaluator in Arize AX with Jev appeared fir…</description>
    </item>
    <item>
      <title>What if your agent's hallucinations had a budget? How to start using SLOs for agent behavior</title>
      <link>https://grafana.com/blog/what-if-your-agent-s-hallucinations-had-a-budget-how-to-start-using-slos-for-agent-behavior/</link>
      <guid isPermaLink="true">https://grafana.com/blog/what-if-your-agent-s-hallucinations-had-a-budget-how-to-start-using-slos-for-agent-behavior/</guid>
      <pubDate>Thu, 24 Sep 2026 17:14:00 GMT</pubDate>
      <dc:creator>Grafana Labs</dc:creator>
      <category>Evals</category>
      <description>Grafana Labs — At Grafana Labs, observability is what we do. So as we started building AI agents, we naturally reached for the same instincts we bring to every system: measure it, set targets, and make reliability something you can rea…</description>
    </item>
    <item>
      <title>Trust, but verify: Atomic claim checking against LLM hallucinations</title>
      <link>https://www.elastic.co/blog/atomic-claim-checking-against-llm-hallucinations</link>
      <guid isPermaLink="true">https://www.elastic.co/blog/atomic-claim-checking-against-llm-hallucinations</guid>
      <pubDate>Thu, 24 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Elastic</dc:creator>
      <category>Evals</category>
      <description>Elastic — Hallucinations kept an LLM out of our knowledge base cleanup for years. Splitting generation from atomic claim verification fixed it and cleared a four-year backlog of duplicate articles.</description>
    </item>
    <item>
      <title>Using TypeSafe’s Jev for evals in Datadog Agent Observability</title>
      <link>https://www.datadoghq.com/blog/jev-evals-agent-observability/</link>
      <guid isPermaLink="true">https://www.datadoghq.com/blog/jev-evals-agent-observability/</guid>
      <pubDate>Thu, 24 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Datadog</dc:creator>
      <category>Evals</category>
      <description>Datadog — See how we wired Jev into online evals on live spans and offline evals inside Datadog experiments, using a single rubric for both.</description>
    </item>
    <item>
      <title>How to Test and Fix Voice AI Agent Prompts with Twilio Conversation Relay and Cekura</title>
      <link>https://www.twilio.com/en-us/blog/developers/tutorials/integrations/test-voice-agent-cekura-conversation-relay</link>
      <guid isPermaLink="true">https://www.twilio.com/en-us/blog/developers/tutorials/integrations/test-voice-agent-cekura-conversation-relay</guid>
      <pubDate>Thu, 24 Sep 2026 00:00:00 GMT</pubDate>
      <dc:creator>Twilio</dc:creator>
      <category>Evals</category>
      <description>Twilio — Build and test an AI voice agent using Twilio Conversation Relay and Cekura. Write metrics, run evaluations, and fix prompt bugs.</description>
    </item>
    <item>
      <title>High-volume AI scoring with Jev and Pydantic Evals</title>
      <link>https://pydantic.dev/articles/jev-evals</link>
      <guid isPermaLink="true">https://pydantic.dev/articles/jev-evals</guid>
      <pubDate>Wed, 23 Sep 2026 16:00:00 GMT</pubDate>
      <dc:creator>Pydantic</dc:creator>
      <category>Evals</category>
      <description>Pydantic — Use Jev for categorical judgments, probabilities, and numeric rubric scores in Pydantic Evals, then track the results in Logfire without a per-score fee.</description>
    </item>
    <item>
      <title>How SWE-Serve Exposes the Gap Between Local Tests and Live Serving</title>
      <link>https://developer.nvidia.com/blog/how-swe-serve-exposes-the-gap-between-local-tests-and-live-serving/</link>
      <guid isPermaLink="true">https://developer.nvidia.com/blog/how-swe-serve-exposes-the-gap-between-local-tests-and-live-serving/</guid>
      <pubDate>Wed, 23 Sep 2026 16:00:00 GMT</pubDate>
      <dc:creator>NVIDIA Developer</dc:creator>
      <category>Evals</category>
      <description>NVIDIA Developer — An AI coding agent’s patch can pass tests yet fail when the server loads a real model and handles requests. Evaluating changes to inference-serving software...</description>
    </item>
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