Overview
Multi-agent scientific research systems accelerate the pace of discovery by automating and enhancing key research activities. From comprehensive literature review to hypothesis generation to experiment design, agent teams augment human researchers' capabilities and help manage the exponential growth of scientific knowledge.
Architecture
Research Question → Literature Review Agent → Knowledge Base
↓
Hypothesis Generation Agent → Candidate Hypotheses
↓
Experiment Design Agent → Experimental Protocol
↓
Data Analysis Agent → Results & Statistics
↓
Visualization Agent → Figures & Charts
↓
Writing Agent → Draft Manuscript
↓
Peer Review Agent → Feedback & Revisions
↓
Citation Agent → Formatted References
Agent Roles
Literature Review Agent
- Searches across scientific databases (PubMed, arXiv, etc.)
- Summarizes papers and extracts key findings
- Identifies gaps in current knowledge
- Tracks emerging research trends
Hypothesis Generation Agent
- Proposes novel hypotheses based on literature
- Identifies unexplored research directions
- Connects findings across disciplines
- Evaluates hypothesis novelty and testability
Experiment Design Agent
- Designs experimental protocols
- Calculates required sample sizes
- Identifies potential confounds
- Suggests controls and validations
Data Analysis Agent
- Performs statistical analysis
- Runs machine learning models
- Validates assumptions
- Interprets results
Visualization Agent
- Creates publication-quality figures
- Generates data visualizations
- Designs explanatory diagrams
- Ensures accessibility standards
Writing Agent
- Drafts manuscript sections
- Ensures scientific writing standards
- Formats for target journals
- Maintains consistent terminology
Peer Review Agent
- Reviews drafts for logical gaps
- Checks statistical validity
- Identifies missing citations
- Suggests improvements
Citation Agent
- Finds relevant citations
- Formats references correctly
- Tracks citation networks
- Identifies seminal papers
AI-Accelerated Discovery
Literature Processing:
- Analyze thousands of papers in hours, not months
- Identify connections humans might miss
- Stay current with publication flood
Hypothesis Generation:
- Combine knowledge across disciplines
- Identify promising unexplored directions
- Evaluate novelty systematically
Experimental Design:
- Optimize designs for statistical power
- Reduce experimental failures
- Identify potential issues early
Real-World Applications
Drug Discovery:
- Screen millions of compounds computationally
- Identify drug targets from literature
- Design clinical trials
Materials Science:
- Predict material properties
- Suggest novel material combinations
- Analyze characterization data
Biology:
- Analyze genomic data
- Identify gene function from literature
- Design CRISPR experiments
Key Patterns
- Reflection Pattern: Learn from experimental outcomes
- Tool Use Pattern: Integration with scientific tools and databases
- Handoff Pattern: Research passes through stages
- Human-in-the-Loop: Researchers validate all key decisions
Common Failure Modes
- Hallucinated Citations: Agent cites non-existent papers
- Statistical Errors: Incorrect statistical analysis
- Novelty Misjudgment: Proposing already-explored hypotheses
- Confirmation Bias: Finding evidence for preferred hypotheses