AI agents and real-world applications of generative AI are debuting at an incredible clip this year, narrowing the time from AI research paper to industry application and propelling productivity growth across industries. From Cline in coding to AI agents deployed in real estate and construction, tremendous value is being created — but getting these agents work and work well is a big challenge. As agents and multiagent systems get deployed, often the teams shipping the most reliable and performant agents are those that invest in evaluation and observability from the start.
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This new analysis on the best LLM evaluation tools and frameworks zeroes in on the top five platforms in helping teams build and manage AI agents.
Why You Need This Comparison of Top LLM Evaluation Tools and Frameworks
Unfortunately, navigating the sea of frameworks and tools that all purport to help in LLM evals and observability is difficult and fraught with out-of-date or outright misinformation. To help, Chris Cooning (an alumni of Observable, Typeform, and Deputy as well as L3 Technologies and Boeing) set out to create a fair portrait of the landscape with a lens toward tools that actually support building high quality agents at scale. As he notes, “building proof of concepts is easy; engineering highly functional agents is not. Reliable agents aren’t discovered; they’re engineered through systems built to observe, measure, and improve behavior over time.”