In this short art­icle, I’ll dis­cuss Auto­Gen and its role in improv­ing Large Lan­guage Model (LLM) applic­a­tions through the use of multi-agent conversations.

What you’ll learn:

• What an AI agent does.
• The gen­esis and mech­an­ics of AutoGen.
• Com­mon agent types within AutoGen.
• Real-world applic­a­tions using AutoGen.
• The advant­ages and chal­lenges of AutoGen.
• A prac­tical demo show­cas­ing Auto­Gen’s capabilities.

What’s an AI Agent?

In this con­text, we are refer­ring to an AI agent as an agent that has an LLM at his com­pu­ta­tional core. How­ever, it does far more than gen­er­ate text. It engages in con­ver­sa­tions, accom­plishes tasks, applies reas­on­ing, per­forms cus­tom func­tions, and shows some level of autonom­ous behavior.

Agents oper­ate through prompts that provide per­son­al­ity, instruc­tions, per­mis­sions, and con­text. For instance, an agent can fix a mis­taken loc­a­tion in a weather query, turn­ing an error into a cor­rect fore­cast for the user.

Tools like Lang­Chain also use AI agents (you can find out more about this here).

What’s Auto­Gen?

Auto­Gen is a frame­work designed to cre­ate applic­a­tions that util­ize mul­tiple agents in col­lab­or­at­ive efforts to com­plete tasks. It’s an open-source and com­munity-sup­por­ted pro­ject with con­tri­bu­tions from Microsoft and numer­ous aca­demic institutions.

Developers can use Auto­Gen in vari­ous con­fig­ur­a­tions, com­bin­ing dif­fer­ent LLMs, human input, and tools, and can even util­ize local LLMs like those from Azure or OpenAI. The frame­work stream­lines the man­age­ment of LLM work­flows and allows developers to define com­mu­nic­a­tion pat­terns and agent num­bers for their spe­cific needs.

Types of Agents in AutoGen

Auto­Gen has a vari­ety of agents with dif­fer­ent roles:

• Con­vers­able Agent: This is the found­a­tional class in Auto­Gen that can be pro­grammed for a wide range of tasks.
• Assist­ant Agent: Focuses on task res­ol­u­tion, often sug­gest­ing Python code and inter­act­ing with other agents.
• Com­press­ible Agent: Sim­ilar to the Assist­ant Agent but with the added func­tion of com­press­ing mes­sages to save on token usage.
• GPT Assist­ant Agent: Con­nects with the latest OpenAI Assist­ant API and retrieves know­ledge for use in applications.
• User Proxy Agent: Executes tasks and provides feed­back to agents, act­ing under user com­mand when needed.
• Math User Proxy & Retrieve User Proxy Agents: Spe­cial­ized in solv­ing math­em­at­ical prob­lems and retriev­ing data based on given configurations.
• Text Ana­lyzer Agent: Improves the qual­ity of text communication.
• Teach­able Agent: Learns from user inter­ac­tions and recalls inform­a­tion for later use.
• Mul­timodal & Llava Agents: Cap­able of ana­lyz­ing images and provid­ing rel­ev­ant information.

Industry Use Cases for AutoGen

Auto­Gen can be applied in vari­ous scen­arios, from gen­er­at­ing and debug­ging code to play­ing chess with visual board rep­res­ent­a­tion. It also includes enhanced chat cap­ab­il­it­ies for inform­a­tion retrieval.

In the auto­mot­ive and man­u­fac­tur­ing indus­tries, for example, here are some poten­tial ways Auto­Gen can be leveraged:

• Intel­li­gent Cus­tomer Ser­vice Bots: Auto­Gen can power vir­tual agents that handle cus­tomer inquir­ies regard­ing car mod­els, fea­tures, and avail­ab­il­ity. These agents can con­verse nat­ur­ally with cus­tom­ers, pull up rel­ev­ant inform­a­tion from the manufacturer’s data­base, and even guide them through com­plex pro­cesses like cus­tom­iz­a­tion options or fin­an­cing plans.
• Auto­mated Qual­ity Assur­ance: For man­u­fac­tur­ers, Auto­Gen can assist in qual­ity con­trol by integ­rat­ing with sensors and cam­eras on the pro­duc­tion line. Agents can be pro­grammed to ana­lyze images for defects, inter­pret sensor data for incon­sist­en­cies, and com­mu­nic­ate with other sys­tems to ini­ti­ate cor­rect­ive actions without human intervention.

Pros and Cons of AutoGen

While Auto­Gen offers flex­ib­il­ity in agent roles and the abil­ity to sim­u­late com­plex organ­iz­a­tional struc­tures, it also presents challenges:

Pros:

• Ease of assign­ing spe­cific func­tions to agents
• Cap­ab­il­ity for agents to self-reflect and provide feedback
• Sim­u­la­tion of organ­iz­a­tional roles (e.g., Product Man­ager, Product Owner, Devs, QA, etc.) for struc­tured workflows.

Cons:

• Com­plex­ity in determ­in­ing the appro­pri­ate num­ber of agents and their roles. (we need the ideal num­ber of agents to have the bal­ance between per­form­ance, costs and res­ults and it’s only pos­sible to get there by trial and error)
• Sig­ni­fic­ant costs asso­ci­ated with test­ing and scaling
• Chal­lenges in debugging
• Need for effect­ive memory man­age­ment (every time agents talk to one another, the LLM is requested)
• Reli­ab­il­ity and con­sist­ency (dif­fer­ent runs of the applic­a­tion can pro­duce dif­fer­ent results)

Live Demon­stra­tion of AutoGen

Below you can watch me per­form a demo of activ­at­ing four dis­tinct agents sim­ul­tan­eously: (1) one for cre­at­ing an image based on a prompt; (2) another gath­er­ing data from Wiki­pe­dia; (3) a third com­bin­ing descrip­tion and image into an HTML file; and (4) one check­ing for task com­ple­tion. Each shows AutoGen’s abil­ity to auto­mate com­plex tasks and integ­rate vari­ous data sources into a uni­fied output.

Con­clu­sion

I hope with this art­icle you have a clearer pic­ture of the types of agents and what you can do with each and every one of them to help you make bet­ter decisions and improve your AI applic­a­tion.

Every day new and improved AI tools appear and new chal­lenges arrive, and at synvert we want to be at the edge of the curve so we will con­tinue to fol­low, search for and exper­i­ment with new break­throughs and updates to tools like Auto­Gen, and of course, we’ll keep you posted!

Want to learn more?
Check out these materials:

• Git­Hub Link for AutoGen
• Code Examples
• Use Cases

About the Author:

Fran­cisco Gas­par is a soft­ware engin­eer at synvert with a focus on back-end devel­op­ment. He has a strong interest in AI and works on AI applic­a­tions for major inter­na­tional brands.