Agentic Design

Patterns
🔧

Self-Improving Systems(SIS)

AI agents that autonomously modify and improve their own code, prompts, or reasoning processes

Complexity: highLearning and Adaptation

🎯 30-Second Overview

Pattern: Systems that automatically analyze, modify, and improve their own capabilities and performance over time

Why: Enables continuous optimization, reduces manual maintenance, and adapts to changing requirements automatically

Key Insight: Combines monitoring, analysis, and safe deployment mechanisms to achieve autonomous capability enhancement

⚡ Quick Implementation

1Baseline:Establish initial system performance metrics
2Monitor:Collect performance and error data continuously
3Analyze:Identify improvement opportunities automatically
4Generate:Create and evaluate potential improvements
5Deploy:Implement validated improvements safely
Example: baseline_system → continuous_monitoring → improvement_generation → safe_deployment → enhanced_system

📋 Do's & Don'ts

✅Implement robust safety checks and rollback mechanisms
✅Use gradual deployment and A/B testing for improvements
✅Monitor for unintended side effects and emergent behaviors
✅Maintain human oversight and intervention capabilities
✅Log all improvement attempts and their outcomes
✅Set clear boundaries and constraints on self-modification
❌Allow unconstrained self-modification without safety bounds
❌Deploy improvements without thorough testing
❌Ignore potential security vulnerabilities in self-modification
❌Remove human oversight and control mechanisms
❌Optimize for single metrics without considering trade-offs

🚦 When to Use

Use When

  • • Long-running systems requiring continuous optimization
  • • Dynamic environments with changing requirements
  • • Systems with measurable performance metrics
  • • Sufficient safety infrastructure exists
  • • Human oversight and intervention possible

Avoid When

  • • Safety-critical systems without robust safeguards
  • • Short-term or one-off applications
  • • Systems with unclear or unmeasurable objectives
  • • Highly regulated environments requiring static behavior
  • • Insufficient monitoring and control infrastructure

📊 Key Metrics

Improvement Rate
Performance gains per iteration cycle
Safety Violations
Number of constraint breaches or failures
Convergence Speed
Time to reach optimal performance
Stability Ratio
Successful vs failed improvement attempts
Resource Efficiency
Computational cost of self-improvement
Human Intervention
Frequency of required manual corrections

💡 Top Use Cases

Code Generation: Automatically improve code quality and efficiency through iterative refinement
Recommendation Systems: Continuously optimize algorithms based on user feedback and engagement
Trading Algorithms: Adapt strategies based on market performance and changing conditions
Content Creation: Iteratively improve writing, design, or creative outputs based on success metrics
System Administration: Automatically optimize configurations and resource allocation
Scientific Discovery: Improve hypothesis generation and experimental design through iterative learning

References & Further Reading

Deepen your understanding with these curated resources

Foundational Concepts

The Basic AI Drives (Omohundro, 2008)

Artificial General Intelligence: Concept, State of the Art, and Future Prospects (Goertzel, 2014)

Self-Improving Artificial Intelligence (Yampolskiy, 2015)

The Technological Singularity (Shanahan, 2015)

Self-Modifying Code & Architectures

Self-Modifying Cartesian Genetic Programming (Miller, 2020)

Neural Architecture Search: A Survey (Elsken et al., 2019)

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (Real et al., 2020)

Self-Evolving Neural Networks (Stanley & Miikkulainen, 2002)

Recursive Self-Improvement

Risks from Learned Optimization (Hubinger et al., 2019)

Self-Improving AI: An Analysis (Yudkowsky, 2007)

The AI Control Problem (Russell, 2016)

Concrete Problems in AI Safety (Amodei et al., 2016)

AutoML & Neural Architecture Search

Efficient Neural Architecture Search via Parameter Sharing (Pham et al., 2018)

DARTS: Differentiable Architecture Search (Liu et al., 2018)

Progressive DARTS: Bridging the Optimization Gap (Chen et al., 2019)

Once for All: Train One Network and Specialize it for Efficient Deployment (Cai et al., 2019)

Recent Advances in LLM Self-Improvement

Self-Instruct: Aligning Language Model with Self Generated Instructions (Wang et al., 2022)

Constitutional AI: Harmlessness from AI Feedback (Bai et al., 2022)

Self-Taught Optimizer (STO): Recursively Self-Improving Code Generation (Zelikman et al., 2023)

Large Language Models Can Self-Improve (Huang et al., 2022)

Safety & Alignment Research

AI Alignment: A Comprehensive Survey (Ji et al., 2023)

Scalable Oversight of AI Systems via LLM Judges (Bowman et al., 2022)

Red Teaming Language Models to Reduce Harms (Ganguli et al., 2022)

Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback (Bai et al., 2022)

Evolutionary & Genetic Programming

Genetic Programming: On the Programming of Computers by Means of Natural Selection (Koza, 1992)

NEAT: Evolving Neural Networks through Augmenting Topologies (Stanley & Miikkulainen, 2002)

Age-Fitness Pareto Optimization (Schmidt & Lipson, 2010)

Automated Algorithm Design via Evolutionary Computation (Pappa et al., 2014)

Meta-Learning for Self-Improvement

Learning to Learn by Gradient Descent by Gradient Descent (Andrychowicz et al., 2016)

Model-Agnostic Meta-Learning for Fast Adaptation (Finn et al., 2017)

Meta-Learning: A Survey (Hospedales et al., 2020)

Learning to Optimize Neural Nets (Li & Malik, 2016)

Practical Implementation Frameworks

AutoGluon: AutoML for Image, Text, and Tabular Data

NASLib: A Modular and Flexible AutoML Framework

OpenAI Codex: Evaluating Large Language Models Trained on Code

GitHub Copilot: AI Pair Programming

Monitoring & Safety Tools

MLflow: A Machine Learning Lifecycle Platform

Weights & Biases: Experiment Tracking for ML

Seldon Core: MLOps Platform for Kubernetes

Kubeflow: Machine Learning Toolkit for Kubernetes

Research Communities & Organizations

Future of Humanity Institute (FHI)

Machine Intelligence Research Institute (MIRI)

Center for AI Safety (CAIS)

Anthropic: AI Safety Research Company

Governance & Policy Research

Partnership on AI: Tenets and Research

AI Governance: A Research Agenda (Dafoe, 2018)

The Malicious Use of Artificial Intelligence (Brundage et al., 2018)

AI Index Report: Measuring AI Progress

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