Autonomous Agents & Agent Simulations

AutoGen's design offers multiple advantages: a) it gracefully navigates the strong but imperfect generation and reasoning abilities of these LLMs; b) it leverages human understanding and intelligence, while providing valuable automation through conversations between agents; c) it simplifies and unifies the implementation of complex LLM workflows as... See more

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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3 days reading through the code of the paper that solved this, here are some notes:

- Long term interactions: the promise of LLM agents is to potentially run over weeks. A lot of the ideas in the paper tackle exactly this problem

- Memory: It’s easy to write down interactions of agents. But how do we track these over time? How do these change with ti... See more

First, here a generalized framework for an autonomous agent :

1. Initialize Goal : Define the objective for the AI.


2. Task Creation : The AI checks its memory for the last X tasks completed (if any), and then uses it’s objective, and the context of it’s recently completed tasks, to generate a list of new tasks.


3. Task Execution : The AI executes the ta

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First off, a primer on autonomous agents: - Give them a task - Give them memory - Give them access to tools - They decide on-the-fly how to accomplish your task and remove you from this process because... it's autonomous - Watch them attempt to do the work