notes from this short video.
We want agents to be reliable. Reliability = Observability + Evals
Observability means we can see what agent did. What the model response was, what the tool response was.
LLM response desired properties:
Relevance: Answer relevance to the user’s query
Groundedness (relates to hallucination as well): uses the particular context provided (as opposed to the generic response)
Tool Sequence Accuracy: does not do wasteful tool calls, and follows correct sequence of tool calls
Evals = QA for agents
Who does evals? Whoever builds the app, also runs evals.
make a synthetic dataset to run evals (in the beginning to kickstart the eval process) or a golden dataset (domain expert designed)