One major hurdle for agent implementations is the issue of observability and steerability.
Agents frequently employ strategies such as chain-of-thought or planning to handle user inquiries, relying on multiple interactions with a Large Language Model (LLM).
Yet, within this iterative approach, monitoring the agent’s inner mechanisms or intervening to correct its trajectory midway through execution proves challenging.
To address this issue, LlamaIndex has introduced a lower-level agent specifically engineered to provide controllable, step-by-step execution on a RAG (Retrieval-Augmented Generation) pipeline.
This demonstration underscores LlamaIndex’s goal of showcasing the heightened control and transparency that the new API brings to managing intricate queries and navigating extensive datasets.
Added to this, introducing agentic capabilities on top of a RAG pipeline can allow you to reason over much more complex questions.
The Human In The Loop chat capabilities allows for a step-wise approach by a human via a chat interface. While it is possible to ask agents complex questions which demands multiple reasoning steps. These queries can be long running and can in some instances be wrong.
