u/Creepy-Row970

Having worked on agentic systems over the past year, one mistake I keep seeing is people treating AI agents as something completely separate from software engineering.

Most products and engineering examples using agents still rely on one large agent handling planning, retrieval, reasoning, and synthesis together.

It works, but scaling quickly becomes messy:
• debugging LLM / Agent tool call gets cumbersome
• context keeps growing
• prompt chains turn unpredictable

So I tried splitting responsibilities across multiple specialized agents instead.

One plans.
Others investigate in parallel.
Separate agents synthesize results.
Everything moves through structured outputs.

The biggest surprise:

The improvement didn’t come from larger, sophisticated prompting.

It came from coordination, specialization, and being able to trace the workflow end to end.

The system became far more reliable once agents had narrow responsibilities instead of trying to do everything at once.

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.

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.