AI agent evaluation platforms are pivotal for testing and monitoring multi-step workflows, as they help trace full sessions, validate decision logic, and catch failures early in development rather than in production. Unlike traditional LLM testing, which focuses on single-turn output quality, AI agent evaluation requires session-level tracing and behavioral testing to address new failure modes such as reasoning drift, tool misuse, and context degradation. The market for AI observability tools is expected to grow significantly, driven by the need for real-time security, automated compliance, and continuous validation integrated into CI/CD pipelines. Various platforms like Openlayer, Braintrust, Arize, Galileo, and LangSmith offer diverse features, from prebuilt test libraries and session tracing to security guardrails and compliance automation, with Openlayer highlighted for its comprehensive governance and monitoring capabilities. However, many platforms lack real-time security and automated compliance features, necessitating additional tools for complete governance. The landscape reflects a shift towards more robust, real-time monitoring solutions as organizations increasingly deploy autonomous agents.