Evals

Measuring LLM Hallucinations: The Metrics That Actually Matter for Reliable AI Apps

Measuring LLM Hallucinations: The Metrics That Actually Matter for Reliable AI Apps

LLM hallucinations aren’t random; they’re measurable. This guide breaks down six core metrics and explains how to wire them into tracing and rubric-driven evaluation so teams can diagnose failures fast and ship reliable AI agents with confidence.

Choosing an Evaluation Platform: 10 Questions to Ask Before You Buy

Choosing an Evaluation Platform: 10 Questions to Ask Before You Buy

Introduction: Why Choosing the Right Evaluation Platform Matters An evaluation platform measures, tests, and improves your AI applications throughout their lifecycle. For teams building AI agents, chatbots, or RAG pipelines, the right platform enables faster iteration, early quality detection, and confident deployment. Poor selection leads to hidden costs, integration failures,

Closing the Feedback Loop: How Evaluation Metrics Prevent AI Agent Failures

Closing the Feedback Loop: How Evaluation Metrics Prevent AI Agent Failures

TL;DR AI agents often fail in production due to tool misuse, context drift, and safety lapses. Static benchmarks miss real-world failures. Build a continuous feedback loop with four stages: detect (automated evaluators on production logs), diagnose (replay traces to isolate failures), decide (use metrics and thresholds for promotion gates)

Agent Evaluation for Multi-Turn Consistency: What Works and What Doesn’t

Agent Evaluation for Multi-Turn Consistency: What Works and What Doesn’t

TL;DR: Multi-turn AI agents need layered evaluation metrics to maintain consistency and prevent failures. Successful evaluation combines session-level outcomes (task success, trajectory quality, efficiency) with node-level precision (tool accuracy, retry behavior, retrieval quality). By integrating LLM-as-a-Judge for qualitative assessment, running realistic simulations, and closing the feedback loop between testing

Prompt Management and Collaboration for AI Agents Using Observability and Evaluation Tools

How to Streamline Prompt Management and Collaboration for AI Agents Using Observability and Evaluation Tools

TL;DR Managing prompts for AI agents requires structured workflows that enable version control, systematic evaluation, and cross-functional collaboration. Observability tools track agent behavior in production, while evaluation frameworks measure quality improvements across iterations. By implementing prompt management systems with Maxim’s automated evaluations, distributed tracing, and data curation capabilities,

How to Evaluate AI Agents in Production: Metrics, Methods, and Pitfalls

How to Evaluate AI Agents in Production: Metrics, Methods, and Pitfalls

TL;DR: AI agents in production now orchestrate complex workflows that traditional model benchmarks weren't designed to evaluate. These agents operate across multiple steps, depend on external tools, and must maintain context throughout conversations. This guide shares a practical framework for evaluating agent reliability at every level, with

AI Agent Evaluation: Metrics, Strategies, and Best Practices

AI Agent Evaluation: Metrics, Strategies, and Best Practices

TL;DR AI agent evaluation is critical for building reliable, production-ready autonomous systems. As organizations deploy AI agents for customer service, coding assistance, and complex decision-making tasks, systematic evaluation becomes essential to ensure these agents meet performance standards, maintain alignment with business goals, and operate safely at scale. This comprehensive