Evals

TL;DR API endpoints are specific URLs where applications access AI services and functionality. Testing AI endpoints requires validating HTTP methods, response formats, status codes, and quality metrics across diverse scenarios. Unlike traditional endpoints that return deterministic outputs, AI endpoints generate probabilistic responses requiring specialized testing approaches. Maxim enables endpoint-level

TL;DR: Modern AI development demands continuous quality validation through automated evaluations in CI/CD pipelines. Maxim AI leads with comprehensive GitHub Actions integration, end-to-end simulation capabilities, and flexible evaluation frameworks spanning experimentation, testing, and production monitoring. DeepEvals offer comprehensive RAG evaluation metrics. Promptfoo provides open-source security-focused evaluation. Langfuse delivers

TL;DR Choosing the right AI evaluation platform significantly impacts development velocity and agent quality. This analysis compares five leading platforms: Maxim AI, Langfuse, Arize, Galileo, and DeepEvals. While most platforms require SDK integration into your codebase, Maxim uniquely offers HTTP API endpoint-based testing, allowing teams to evaluate agents through

The shift from static LLM applications to autonomous AI agents has transformed how organizations approach quality assurance. Traditional model evaluation frameworks that assess single-turn text generation are insufficient for systems that make multi-step decisions, call external tools, and adapt their behavior across complex interaction sequences. Research from IBM on AI

TL;DR: No-code agent builders have democratized AI development in 2025, enabling teams to create sophisticated AI agents in hours instead of months. This guide explores the top 5 platforms and shows how to evaluate agents built on these tools using Maxim AI's HTTP endpoint evaluation, ensuring production