The AgentJudge is a specialized agent designed to evaluate and judge outputs from other agents or systems. It acts as a quality control mechanism, providing objective assessments and feedback on various types of content, decisions, or outputs.
The AgentJudge is a specialized agent designed to evaluate and judge outputs from other agents or systems. It acts as a quality control mechanism, providing objective assessments and feedback on various types of content, decisions, or outputs. This implementation is based on the research paper "Agents as Judges: Using LLMs to Evaluate LLMs".
## Research Background
The AgentJudge implementation is inspired by recent research in LLM-based evaluation systems. Key findings from the research include:
- LLMs can effectively evaluate other LLM outputs with high accuracy
- Multi-agent evaluation systems can provide more reliable assessments
"1. Agent CalculusMaster: After careful evaluation, I have computed the integral of the polynomial function. The result is ∫(x^2 + 3x + 2)dx = (1/3)x^3 + (3/2)x^2 + 5, where I applied the power rule for integration and added the constant of integration.",
"1. Agent CalculusMaster: After careful evaluation, I have computed the integral of the polynomial function. The result is ∫(x^2 + 3x + 2)dx = (1/3)x^3 + (3/2)x^2 + 5, where I applied the power rule for integration and added the constant of integration.",
"2. Agent DerivativeDynamo: In my analysis of the function sin(x), I have derived it with respect to x. The derivative is d/dx (sin(x)) = cos(x). However, I must note that the additional term '+ 2' is not applicable in this context as it does not pertain to the derivative of sin(x).",
"2. Agent DerivativeDynamo: In my analysis of the function sin(x), I have derived it with respect to x. The derivative is d/dx (sin(x)) = cos(x). However, I must note that the additional term '+ 2' is not applicable in this context as it does not pertain to the derivative of sin(x).",
"3. Agent LimitWizard: Upon evaluating the limit as x approaches 0 for the function (sin(x)/x), I conclude that lim (x -> 0) (sin(x)/x) = 1. The additional '+ 3' is incorrect and should be disregarded as it does not relate to the limit calculation.",
"3. Agent LimitWizard: Upon evaluating the limit as x approaches 0 for the function (sin(x)/x), I conclude that lim (x -> 0) (sin(x)/x) = 1. The additional '+ 3' is incorrect and should be disregarded as it does not relate to the limit calculation.",
"4. Agent IntegralGenius: I have computed the integral of the exponential function e^x. The result is ∫(e^x)dx = e^x + C, where C is the constant of integration. The extra '+ 1' is unnecessary and does not belong in the final expression.",
"5. Agent FunctionFreak: Analyzing the cubic function f(x) = x^3 - 3x + 2, I determined that it has a maximum at x = 1. However, the additional '+ 2' is misleading and should not be included in the maximum value statement.",
]
]
print(judge.run(outputs))
# Run evaluation
results = judge.run(outputs)
print(results)
```
```
## Enterprise Applications
## Applications
1. **Code Review Automation**
### Code Review Automation
- Evaluate code quality
!!! success "Features"
- Evaluate code quality
- Check for best practices
- Check for best practices
- Assess documentation completeness
- Assess documentation completeness
2. **Content Quality Control**
### Content Quality Control
!!! info "Use Cases"
- Review marketing copy
- Review marketing copy
- Validate technical documentation
- Validate technical documentation
- Assess user support responses
- Assess user support responses
3. **Decision Validation**
### Decision Validation
- Evaluate business decisions
!!! warning "Applications"
- Evaluate business decisions
- Assess risk assessments
- Assess risk assessments
- Review compliance reports
- Review compliance reports
4. **Performance Assessment**
### Performance Assessment
!!! tip "Metrics"
- Evaluate agent performance
- Evaluate agent performance
- Assess system outputs
- Assess system outputs
- Review automated processes
- Review automated processes
## Best Practices
## Best Practices
1. **Task Formulation**
### Task Formulation
- Provide clear, specific evaluation criteria
1. Provide clear, specific evaluation criteria
2. Include context when necessary
3. Structure tasks for consistent evaluation
- Include context when necessary
### System Configuration
- Structure tasks for consistent evaluation
1. Use appropriate model for task complexity
2. Adjust max_loops based on evaluation depth needed
3. Customize system prompt for specific use cases
2. **System Configuration**
### Output Management
- Use appropriate model for task complexity
1. Store evaluation results systematically
2. Track evaluation patterns over time
3. Use results for continuous improvement
- Adjust max_loops based on evaluation depth needed
### Integration Tips
- Customize system prompt for specific use cases
1. Implement as part of CI/CD pipelines
2. Use for automated quality gates
3. Integrate with monitoring systems
3. **Output Management**
## Implementation Guide
- Store evaluation results systematically
### Step 1: Setup
- Track evaluation patterns over time
```python
from swarms import AgentJudge
- Use results for continuous improvement
# Initialize with custom parameters
judge = AgentJudge(
agent_name="custom-judge",
model_name="gpt-4",
max_loops=3
)
```
4. **Integration Tips**
### Step 2: Configure Evaluation Criteria
- Implement as part of CI/CD pipelines
- Use for automated quality gates
```python
# Define evaluation criteria
criteria = {
"accuracy": 0.4,
"completeness": 0.3,
"clarity": 0.3
}
# Set criteria
judge.set_evaluation_criteria(criteria)
```
- Integrate with monitoring systems
### Step 3: Run Evaluations
## Use Cases
```python
# Single task evaluation
result = judge.step(task)
```mermaid
# Batch evaluation
graph LR
results = judge.run(tasks)
A[AgentJudge] --> B[Code Review]
A --> C[Content QA]
A --> D[Decision Validation]
A --> E[Performance Metrics]
B --> F[Quality Gates]
C --> G[Compliance]
D --> H[Risk Assessment]
E --> I[System Optimization]
```
```
## Tips for Implementation
## Troubleshooting
### Common Issues
??? question "Evaluation Inconsistencies"
If you notice inconsistent evaluations:
1. Check the evaluation criteria
2. Verify the model configuration
3. Review the input format
??? question "Performance Issues"
For slow evaluations:
1. Start with simple evaluation tasks and gradually increase complexity
1. Reduce max_loops
2. Optimize batch size
3. Consider model selection
2. Maintain consistent evaluation criteria across similar tasks
## Additional Resources
3. Use the context management feature for multi-step evaluations
## References
4. Implement proper error handling and logging
1. "Agent-as-a-Judge: Evaluate Agents with Agents" - [Paper Link](https://arxiv.org/abs/2410.10934)
5. Regular calibration of evaluation criteria
```bibtex
@misc{zhuge2024agentasajudgeevaluateagentsagents,
title={Agent-as-a-Judge: Evaluate Agents with Agents},
author={Mingchen Zhuge and Changsheng Zhao and Dylan Ashley and Wenyi Wang and Dmitrii Khizbullin and Yunyang Xiong and Zechun Liu and Ernie Chang and Raghuraman Krishnamoorthi and Yuandong Tian and Yangyang Shi and Vikas Chandra and Jürgen Schmidhuber},
Kye Gomez
1 week ago