An agent can be thought of as a logical wrapper around an LLM, allowing us to add several features to our AI systems, primarily:

• Tool usage, such as calling APIs for info, executing code,


• Internal thoughts over multiple generation steps


• Ability to use various tools and reasoning steps to answer more complex queries.


• Parallel agents can go and complete

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Thanks to the cognitive architecture design, goal-oriented behavior is at the core of our LLM agents. When presented with a goal, they employ a planning and execution process to achieve it. If the goal can be accomplished using atomic game actions, we generate a plan that outlines how to solve the objective. For longer-term goals, we decompose the... See more