Adding Router and Skill Evaluations
Tracing done! Now it’s time to set up some evals. Focus will be on evaluating agents’ skills…as well as the router's ability to choose the correct tool and execute it correctly given a user’s request.
Discover the latest trends, best practices, and expert insights on AI agents, intelligent automation, and the future of software development from the Aximox team.
Tracing done! Now it’s time to set up some evals. Focus will be on evaluating agents’ skills…as well as the router's ability to choose the correct tool and execute it correctly given a user’s request.
Large Language Models (LLMs) have revolutionized many fields, but they often face challenges in complex reasoning tasks. A common pitfall is limiting an LLM to generate only a single solution per problem.
Research into LLM agent frameworks is a rapidly evolving field, with examples like LangChain, LlamaIndex, and Haystack providing tools for developers. Frameworks like “Agent Laboratory” (Schmidgall et al., 2025) even explore autonomous LLM-based systems
In the rapidly evolving telecommunications landscape, standardization isn’t just beneficial; it’s essential. As service providers navigate the complexities of 5G networks, edge computing.
So you just built your first AI agent, congrats! Now imagine this: you’re giving a live demo, everything’s going great… until it isn’t. Your agent suddenly goes rogue, giving an unexpected answer to a question it handled perfectly just yesterday.