Different Answers, Same Problem - Comparing AI Agent Architectures
Different Answers, Same Problem
Give the same automation task to three different agent architectures and you get three different solutions. Not because one is wrong - because each architecture optimizes for different constraints.
The Same Task, Three Ways
Take a common workflow: monitor a folder for new invoices, extract line items, update a spreadsheet, and notify the team. A straightforward automation problem with well-defined inputs and outputs.
Single-agent approach - one agent handles everything sequentially. It watches the folder, parses the PDF, writes to the spreadsheet, and sends the notification. Simple to debug, easy to understand, but blocked if any step is slow.
Multi-agent approach - separate agents for file monitoring, data extraction, spreadsheet updates, and notifications. Each agent is independently deployable and testable. But now you need coordination, message passing, and failure handling between agents.
Hybrid approach - a coordinator agent delegates to specialized sub-agents but maintains a unified context. You get parallelism without full distribution complexity. The tradeoff is that the coordinator becomes a single point of failure.
What the Comparison Reveals
The architecture that "wins" depends entirely on your constraints. If reliability matters most, the single agent is simplest to reason about. If throughput matters, multi-agent parallelism helps. If you need both, the hybrid adds complexity but delivers flexibility.
Most teams pick an architecture before understanding their actual constraints. They optimize for scale they do not have or simplicity they do not need.
The Real Lesson
There is no universal best architecture. The right answer depends on your volume, your tolerance for complexity, and how much time you have for debugging coordination failures. Start with the simplest approach that meets your actual requirements - not your imagined future ones.
Fazm is an open source macOS AI agent. Open source on GitHub.