Community discusses multi-agent loop patterns for AI workflows
This r/AI_Agents post covers multi-agent loops — a design where several AI agents pass results back and forth in cycles to tackle complex tasks. Each agent handles a piece of the work, checks the previous agent's output, and feeds improvements forward. It's directly relevant to anyone building AI agent systems and trying to manage costs.
A multi-agent loop works like a relay: agent A drafts something, agent B reviews and improves it, agent A refines again — and so on until the task is done well enough. This cyclic collaboration lets agents catch each other's mistakes and produce higher-quality results than a single agent working alone.
The main trade-off is cost: every loop iteration means more LLM calls and more tokens consumed. Designing a clear stopping condition — how many rounds to run, or when the output is 'good enough' — is the key to keeping token costs under control. The r/AI_Agents community shares hands-on implementation experiences and pitfalls around these patterns.
Key points
- Multiple agents take turns refining each other's output in a repeating cycle
- This approach handles complex tasks better than a single agent working alone
- More loop iterations mean more LLM calls and higher token costs
- A clear stopping condition is essential to avoid runaway costs
- The community shares real implementation tips and lessons learned
Quick term guide
- r/AI_Agents
- A Reddit community focused on AI agents and related tools.
- multi-agent loop
- A pattern where several AI agents pass work back and forth in cycles, each improving on the last agent's output
- multi-agent
- A setup where several AI agents each handle a different subtask and work together to complete a larger goal.
- AI agents
- AI agents are AI tools that can carry out steps toward a goal, not just answer once.
- AI agent
- An AI program that can inspect information and suggest what to do next.
- LLM call
- Sending a question or instruction to an AI language model (like ChatGPT or Claude) and receiving its response.
- token costs
- Token costs are the fees paid for the text an AI model reads and writes.
- token cost
- The money or usage spent when sending text to an AI model and getting text back.