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Active 2026-01-10

Experiment 002: Multi-Agent Research Pipeline

Exploring whether specialized AI agents can collaborate to conduct comprehensive research with minimal supervision.

agents research collaboration

Objective

Design a multi-agent system where specialized AI agents collaborate to conduct research: one agent for information gathering, one for synthesis, one for verification, and one for documentation. The system should operate with minimal human intervention while maintaining research quality.

System Configuration (Sanitized)

  • 4 specialized agents with distinct roles
  • Shared knowledge base and communication protocol
  • Automated workflow orchestration
  • Quality scoring mechanism with confidence thresholds

Output Summary

Conducted 23 research tasks over 3 weeks:

  • Average completion time: 4.2 hours per task
  • Human review time reduced by 76%
  • Output quality: 8.1/10 (expert evaluation)

Observations

What Worked

Agent specialization improved output quality significantly. The verification agent caught 89% of factual errors before final output. Automated workflow orchestration removed bottlenecks and improved throughput.

What Failed

Agents occasionally fell into circular reasoning loops when encountering ambiguous information. The system struggled with research requiring creative hypothesis generation. Inter-agent communication overhead was higher than expected.

Unexpected Behavior

Agents developed implicit coordination patterns not programmed into the system. The documentation agent consistently exceeded its mandate, adding context not explicitly requested. System performance improved over the first week without explicit training.

Human Intervention Required

  • Task definition and scope setting
  • Breaking circular reasoning deadlocks (3 instances)
  • Final quality review and publication approval
  • Weekly system performance review

Total intervention time: ~4 hours per week

Next Iteration

Implement deadlock detection and automatic resolution. Add creative hypothesis generation capability. Reduce inter-agent communication overhead. Test scaling to 6-8 specialized agents.