From: redpointai
AI agents are well-connected with the capabilities of language models in certain areas, though not in most [00:00:00]. A deep understanding of the underlying theory is necessary to discern these areas [00:00:08].
Limitations in Novel Planning
Agents struggle with any task that requires novel planning [00:00:12]. If a planning sequence is not almost exactly present in the training data, an agent will not be able to perform it [00:00:14].
For instance, a researcher notes that while their algorithms are often simple (four or five lines of code), they have never received help from a language model in creating these algorithms because, by definition, they are not present in the training data [00:00:43]. This also applies to attempts to create a research breakthrough [00:00:40].
Strengths in Pattern Matching and Common Tasks
Conversely, agents excel at tasks that involve various mixes and matches of information already seen in their training data [00:00:20].
Examples of tasks agents are well-suited for include:
- Helping to answer or label emails [00:00:26].
- Adding CSS to HTML [00:00:32].
- Iteratively debugging common Python problems [00:00:34].