- “research”
- “ai”
- “iterative”
- “cyclical-validation”
- “multi-agent-critique”
- “depth-scaling”
- “error-containment”
- “agent-collaboration” group: applied-ai-workflows
Iterative research
A research methodology involving cyclical refinement through repeated analysis, validation, and adjustment to overcome limitations of linear approaches. Mitigates hallucinations, shallow insights, and incomplete evidence by emulating human cognitive iteration.
Key characteristics
- Cyclical validation: Each iteration tests and refines hypotheses using new data
- Multi-agent critique: Incorporates diverse perspectives (e.g., anthropic’s multi-agent system) to challenge assumptions
- Depth scaling: Avoids single-query limitations through layered analysis
- Error containment: Identifies and corrects hallucinations early in the cycle
Implementation example
- Anthropic-inspired multi-agent deep research agent:
- Built via Flowise (using Flowise AI and GitHub repo)
- Designed to overcome limitations of single LLM queries, such as hallucination and insufficient depth
- Detailed guide available in video by Leon van Zyl (https://www.youtube.com/watch?v=GPsKnsYJPiI)
- Core concept involves iterative refinement through multi-agent collaboration
Source Notes
- 2026-04-07: AutoResearch Autonomous AI Agent Self Improvement Through Code Iterati · ▶ source
- 2026-04-24: Strategies to Transform Claude AI · ▶ source
- 2026-04-26: Karpathy