I tried implementing AI Agents Like Distributed Systems
Most agent setups follow the same pattern: one big prompt + a few tools.
It works, but once you try to scale it, you get hallucinations, debugging becomes tricky making it hard to tell which part of the system actually failed.
Instead of that, I tried structuring agents more like a distributed pipeline, having multiple specialized agents, each doing one job, coordinated as a workflow.
The system works like a small “research committee”:
• A planner breaks down the task
• Two agents run in parallel (e.g. bull vs bear case)
• Separate agents synthesize the outputs into a final result
• Everything flows through structured, typed data
A few things stood out:
• Systems feel more stable when agents are specialized, not general-purpose
• Typed handoffs reduce a lot of the randomness from prompt chaining
• Running agents as background workflows fits better than chat loops
• Parallel agents improve both latency and reasoning quality
• Having a full execution trace makes debugging way more practical
The interesting shift is less about “multi-agent” and more about thinking in systems instead of prompts.
The demo is simple, but this pattern feels much closer to how real production AI systems will be built, closer to microservices than chatbots.