I'm debating whether to build one 'Super Agent' with 20 tools or a Multi-Agent System (MAS) where different agents (Coder, Reviewer, Manager) talk to each other. Microsoft's AutoGen seems cool, but is the communication overhead between agents worth the improved task success rate for enterprise coding tasks?
3 answers
Have you considered the latency impact? Does having three agents talk to each other make the user experience too slow for a real-time coding assistant?
The best part of AutoGen is the "state management." You can pause the conversation, have a human intervene, and then resume, which is much harder to do with a single-agent script.
Exactly, Deborah. The "Human-in-the-Loop" feature in AutoGen is a game changer for sensitive tasks like deploying code to production where you need a final "OK" from a human.
In the enterprise space, Multi-Agent Systems (MAS) are almost always superior for complex workflows. A "Super Agent" with too many tools often suffers from "tool confusion"—it starts using a database tool when it should be using a web search tool. By breaking the task into specialized roles like "Quality Assurance Agent" and "Developer Agent," you create a natural peer-review system. In our last project, the MAS approach caught 30% more syntax errors in the generated code because the "Reviewer" agent had a specific instruction to find bugs, whereas a single agent is often "blinded" by its own creative output during the generation phase.
Kevin, that's the main trade-off. To answer your point, we don't use MAS for real-time autocomplete. We use it for "Asynchronous Tasks" like generating an entire test suite or refactoring a legacy module. In those cases, the user is fine waiting 2 minutes for a perfect result rather than 10 seconds for a broken one. We also use smaller, faster models like GPT-3.5-Turbo for the "Manager" agent to keep costs and latency down, while reserving GPT-4 only for the heavy lifting done by the "Coder" agent.