The Agent
Understands the request and decides what to do
The LLM agent is the decision maker. We describe what we want in plain language. The agent turns that into a step by step plan based on the tools we give it. As it executes this plan, it adapts to new information to ultimately achieve its goal.
Tools
Pulls in data and capabilities from other systems
Tools allow the agent to interact with other systems: read/create files, run code, pull data from external sources. The agent calls a tool only when it helps. We can add or remove tools without changing how the agent thinks.
Interface
A clear place to see and approve the result
The interface is how you review the outcome. It shows the agent trace, the plan, the results and outputs, anything relevant to the task. It also gives the agent a way to ask for approval from the human user.
Evaluations
Dataset-based runs that verify quality end-to-end
An eval is a set of representative datasets and tasks we create to ensure the agent process produces quality outputs consistently. We run the full system on them end to end. We compare the outputs to expected results and track scores over time. If scores drop, we fix the issue before deployment.