Evals

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

Top Agent Evaluation Tools in 2025: Best Platforms for Reliable Enterprise Evals

Top Agent Evaluation Tools in 2025: Best Platforms for Reliable Enterprise Evals

TL;DR Evaluating AI agents in 2025 requires platforms that can simulate multi-turn interactions, check whether agents make correct tool calls, and test how well they handle and recover from errors during a task. Leading platforms such as Maxim AI, LangSmith, Langfuse, Arize Phoenix, Comet, Confident AI, and RAGAS differ

Evaluating Agentic Workflows: The Essential Metrics That Matter

Evaluating Agentic Workflows: The Essential Metrics That Matter

TL;DR Agentic AI systems must be evaluated beyond static benchmarks. Effective assessment spans three layers: system efficiency (latency, tokens, tool usage), session-level outcomes (task success, step completion, trajectory quality, self-aware failures), and node-level precision (tool selection, error rate, tool call accuracy, plan evaluation, step utility). This structure quantifies planning,