A 150-agent design for research APIs facing conflicting sources
Apodex-1.0 Heavy-Duty uses many separate agents to handle tasks where sources disagree or the question contains a false assumption. The common ReAct approach uses one agent that repeatedly thinks, acts, observes, and thinks again inside one . That pattern can hit limits after a few hundred steps.
The becomes crowded, different research paths start to interfere with each other, and self-checking becomes weaker. The same agent that made the mistake may keep the same blind spots when it reviews its own work. This design tries to scale agents instead of only making the longer.
AgentOS acts as a general control layer, where a main coordinator can create up to 150 specialized sub-agents that run . Each sub-agent works in its own clean context, and the title also points to a separate verifier for checking messy evidence.
Key points
- The design targets tasks with conflicting sources or false in the prompt.
- A single can become unreliable when it runs for many steps inside one .
- Mixed research branches can interfere with each other when they share the same .
- AgentOS can launch up to 150 specialized sub-agents under one main coordinator.
- Separate clean contexts may reduce cross-contamination between different research paths.
Sources covering this story (3)
- r/LLMDevsA 150-agent design for research APIs facing conflicting sources ↗
- r/AI_AgentsMost deep-research agents hide it when their sources disagree — here's the verification architecture we built to stop that ↗
- r/LocalLLaMAResearchers trained a Deep Research agent with 32 H100s and open-sourced everything ↗