# Adaptive Learning Pattern

## Overview

The **Adaptive Learning Pattern** enables AI agents to continuously improve their performance by learning from feedback, outcomes, and interactions. Unlike static systems that operate with fixed parameters, adaptive learning agents evolve over time, optimizing their behavior based on real-world results.

This pattern implements a feedback loop where agents collect data about their actions, analyze patterns of success and failure, adapt their strategies, validate improvements, and iterate continuously. The result is an agent that becomes progressively better at its tasks through experience.

## Why Use This Pattern?

Traditional AI agents operate with fixed prompts, parameters, and strategies. While they may work well initially, they cannot improve without manual intervention. This creates several challenges:

- **User needs evolve**: What works today may not work tomorrow as user preferences change
- **Domain drift**: Problem spaces evolve, requiring different approaches over time
- **Personalization gaps**: One-size-fits-all solutions fail to serve diverse user populations
- **Static performance**: No mechanism to get better from experience
- **Manual tuning overhead**: Requires human intervention to adjust and improve

Adaptive Learning solves these by:
- **Continuous improvement**: Performance improves automatically over time
- **Dynamic adaptation**: Adjusts strategies based on real-world feedback
- **Personalization**: Learns individual user preferences and patterns
- **Reduced manual effort**: Self-optimizes without constant human tuning
- **Data-driven decisions**: Uses empirical evidence to guide improvements
- **Resilience to drift**: Adapts as the environment changes

### Example: Customer Support Agent

```
Without Adaptive Learning (Static):
User: "How do I reset my password?"
Agent: [Uses same generic response for all users, regardless of technical level]
→ Some users find it too technical, others too simplistic
→ No improvement over time

With Adaptive Learning (Evolving):
Week 1:
User: "How do I reset my password?"
Agent: [Generic response]
Feedback: "Too technical" (3/5 rating)

Week 2 (after learning):
User: "How do I reset my password?"
Agent: [Simpler response learned from feedback patterns]
Feedback: "Perfect!" (5/5 rating)

Week 4 (personalized):
Technical User: [Detailed steps with CLI options]
Non-technical User: [Simple GUI-based steps with screenshots]
→ Agent learned to adapt based on user profile and feedback history
```

## How It Works

The Adaptive Learning pattern operates through a continuous cycle:

```
┌─────────────────────────────────────────────────────────────┐
│                    Initial State                             │
│                (Agent with baseline behavior)                │
└───────────────────────┬─────────────────────────────────────┘
                        ↓
              ┌─────────────────┐
              │  1. COLLECT     │ Gather feedback, outcomes, metrics
              │     FEEDBACK    │ (ratings, success/failure, behavior)
              └────────┬────────┘
                       ↓
              ┌─────────────────┐
              │  2. ANALYZE     │ Identify patterns, correlations,
              │     PATTERNS    │ success factors, failure modes
              └────────┬────────┘
                       ↓
              ┌─────────────────┐
              │  3. ADAPT       │ Adjust prompts, parameters,
              │     STRATEGY    │ strategies, examples, policies
              └────────┬────────┘
                       ↓
              ┌─────────────────┐
              │  4. VALIDATE    │ Test improvements, A/B testing,
              │     CHANGES     │ measure impact on metrics
              └────────┬────────┘
                       ↓
              ┌─────────────────┐
              │  5. ITERATE     │ Commit successful changes,
              │     & REPEAT    │ revert failures, continue cycle
              └────────┬────────┘
                       │
                       └──────────┐
                                  ↓
                    ┌──────────────────────────┐
                    │   Improved Performance   │
                    └──────────────────────────┘
```

### The Learning Cycle

1. **Feedback Collection**
   - Explicit feedback: User ratings, thumbs up/down, comments
   - Implicit feedback: Click-through rates, completion rates, time-on-task
   - Outcome tracking: Success/failure of agent actions
   - Performance metrics: Response time, accuracy, user satisfaction

2. **Pattern Analysis**
   - Correlation analysis: Which strategies correlate with success?
   - Failure mode identification: What patterns precede failures?
   - User segmentation: Different strategies for different user types
   - Temporal patterns: How do optimal strategies change over time?

3. **Strategy Adaptation**
   - Prompt evolution: Refine system prompts based on successful patterns
   - Few-shot learning: Add successful examples to context
   - Parameter tuning: Adjust temperature, top-p, max tokens based on outcomes
   - Policy updates: Modify decision-making rules and thresholds

4. **Validation**
   - A/B testing: Compare new strategy against baseline
   - Gradual rollout: Deploy to subset of users first
   - Metric monitoring: Track key performance indicators
   - Rollback capability: Revert if performance degrades

5. **Iteration**
   - Commit improvements: Make successful adaptations permanent
   - Continue learning: Never stop collecting and analyzing
   - Balance exploration: Try new approaches while exploiting known good ones
   - Prevent overfitting: Ensure adaptations generalize

## When to Use This Pattern

### ✅ Ideal Use Cases

- **User-facing conversational agents**: Chatbots that need to improve based on user satisfaction
- **Recommendation systems**: Learn user preferences and optimize suggestions
- **Content generation**: Improve output quality based on user feedback
- **Task automation**: Optimize workflows based on success rates
- **Decision support systems**: Learn which recommendations users accept
- **Customer support**: Adapt responses based on resolution success
- **Educational systems**: Personalize learning paths based on student progress
- **Code assistants**: Learn which suggestions users accept and modify
- **Search and retrieval**: Optimize ranking based on relevance feedback

### ❌ When NOT to Use

- **High-stakes, regulated decisions**: Where model drift could create compliance issues
- **Safety-critical systems**: Where unvalidated changes pose risks
- **Limited data scenarios**: When insufficient feedback for learning
- **Static requirements**: When behavior should never change
- **Real-time constraints**: When learning overhead exceeds time budget
- **Interpretability requirements**: When every decision must be explainable
- **Resource-constrained environments**: When storage/compute for learning unavailable

## Rule of Thumb

**Use Adaptive Learning when:**
1. System interacts with **many users over time** (enough data to learn)
2. User preferences or domain **evolves gradually** (learning is valuable)
3. You can **collect meaningful feedback** (explicit or implicit)
4. Performance **improvement justifies complexity** (ROI is positive)
5. You have **mechanisms for validation** (A/B testing, monitoring)

**Don't use Adaptive Learning when:**
1. Behavior must be **deterministic and unchanging** (regulatory/compliance)
2. Limited interactions mean **insufficient learning data**
3. Changes must be **manually reviewed** before deployment
4. System is already **performing optimally** (diminishing returns)
5. Learning overhead would **degrade user experience**

## Core Components

### 1. Feedback Collection System

Mechanisms to gather data about agent performance:

```python
class FeedbackCollector:
    def collect_explicit_feedback(self, interaction_id, rating, comment):
        """User-provided ratings and comments"""

    def collect_implicit_feedback(self, interaction_id, metrics):
        """Usage patterns: clicks, time, completion"""

    def track_outcome(self, interaction_id, success, reason):
        """Binary or multi-class success/failure"""

    def measure_performance(self, interaction_id, metrics):
        """Quantitative metrics: latency, accuracy, etc."""
```

### 2. Pattern Analyzer

Extracts insights from feedback data:

```python
class PatternAnalyzer:
    def identify_success_patterns(self, feedback_data):
        """What characteristics correlate with success?"""

    def identify_failure_modes(self, feedback_data):
        """What patterns precede failures?"""

    def segment_users(self, feedback_data):
        """Different strategies for different user types"""

    def detect_drift(self, feedback_data, time_window):
        """How are patterns changing over time?"""
```

### 3. Adaptation Engine

Modifies agent behavior based on insights:

```python
class AdaptationEngine:
    def evolve_prompts(self, successful_interactions):
        """Refine system prompts using successful examples"""

    def update_few_shot_examples(self, best_examples):
        """Add high-quality examples to context"""

    def tune_parameters(self, performance_data):
        """Optimize temperature, top-p, etc."""

    def adjust_policies(self, decision_outcomes):
        """Modify thresholds and decision rules"""
```

### 4. Validation Framework

Tests improvements before full deployment:

```python
class ValidationFramework:
    def ab_test(self, control_strategy, treatment_strategy, sample_size):
        """Statistical comparison of strategies"""

    def gradual_rollout(self, new_strategy, percentage):
        """Deploy to increasing user percentage"""

    def monitor_metrics(self, strategy, metrics, threshold):
        """Track KPIs and alert on degradation"""

    def rollback(self, strategy_version):
        """Revert to previous known-good version"""
```

### 5. Learning Loop Controller

Orchestrates the continuous learning cycle:

```python
class LearningLoop:
    def __init__(self, collector, analyzer, adaptor, validator):
        self.collector = collector
        self.analyzer = analyzer
        self.adaptor = adaptor
        self.validator = validator

    def run_cycle(self, iteration):
        # Collect feedback from recent interactions
        feedback = self.collector.get_recent_feedback()

        # Analyze patterns
        insights = self.analyzer.extract_insights(feedback)

        # Generate adaptations
        adaptations = self.adaptor.propose_changes(insights)

        # Validate through testing
        validated = self.validator.test_adaptations(adaptations)

        # Commit successful changes
        self.adaptor.apply_changes(validated.successful)
```

## Implementation Approaches

### Approach 1: Reinforcement Learning (Reward-Based)

Learn optimal policies through trial and error with reward signals:

```python
from typing import Dict, List, Tuple
import numpy as np

class MultiArmedBandit:
    """Optimize strategy selection using exploration vs exploitation"""

    def __init__(self, strategies: List[str], epsilon: float = 0.1):
        self.strategies = strategies
        self.epsilon = epsilon  # exploration rate
        self.counts = {s: 0 for s in strategies}
        self.rewards = {s: 0.0 for s in strategies}

    def select_strategy(self) -> str:
        """Choose strategy balancing exploration and exploitation"""
        if np.random.random() < self.epsilon:
            # Explore: random strategy
            return np.random.choice(self.strategies)
        else:
            # Exploit: best known strategy
            avg_rewards = {s: self.rewards[s] / max(self.counts[s], 1)
                          for s in self.strategies}
            return max(avg_rewards, key=avg_rewards.get)

    def update(self, strategy: str, reward: float):
        """Update knowledge based on outcome"""
        self.counts[strategy] += 1
        self.rewards[strategy] += reward
```

### Approach 2: Few-Shot Learning (Example-Based)

Improve by collecting and using successful examples:

```python
class FewShotLearner:
    """Learn from examples of successful interactions"""

    def __init__(self, max_examples: int = 10):
        self.examples = []
        self.max_examples = max_examples

    def add_successful_example(self, input: str, output: str,
                               feedback_score: float):
        """Add a successful interaction as an example"""
        example = {
            "input": input,
            "output": output,
            "score": feedback_score
        }

        # Insert sorted by score
        self.examples.append(example)
        self.examples.sort(key=lambda x: x["score"], reverse=True)

        # Keep only top examples
        self.examples = self.examples[:self.max_examples]

    def get_examples_for_prompt(self) -> str:
        """Format examples for inclusion in prompt"""
        examples_text = "\n\n".join([
            f"Example {i+1}:\nInput: {ex['input']}\nOutput: {ex['output']}"
            for i, ex in enumerate(self.examples)
        ])
        return f"Here are examples of excellent responses:\n\n{examples_text}"
```

### Approach 3: Parameter Tuning (Optimization-Based)

Optimize model parameters based on performance metrics:

```python
class ParameterOptimizer:
    """Tune model parameters using performance feedback"""

    def __init__(self, initial_params: Dict[str, float]):
        self.params = initial_params
        self.performance_history = []

    def update_parameters(self, metrics: Dict[str, float],
                         learning_rate: float = 0.1):
        """Adjust parameters using gradient-like updates"""

        # Track performance
        self.performance_history.append({
            "params": self.params.copy(),
            "metrics": metrics
        })

        # Simple adaptive tuning based on success rate
        if metrics.get("success_rate", 0) < 0.5:
            # Decrease temperature for more focused responses
            self.params["temperature"] = max(
                0.1,
                self.params["temperature"] - learning_rate
            )
        elif metrics.get("success_rate", 0) > 0.8:
            # Can afford to be more creative
            self.params["temperature"] = min(
                1.0,
                self.params["temperature"] + learning_rate * 0.5
            )

        return self.params
```

### Approach 4: Prompt Evolution (Meta-Learning)

Automatically refine prompts based on successful patterns:

```python
class PromptEvolver:
    """Evolve system prompts based on feedback"""

    def __init__(self, base_prompt: str, llm):
        self.base_prompt = base_prompt
        self.llm = llm
        self.prompt_versions = [base_prompt]
        self.version_performance = [0.0]

    def evolve_prompt(self, successful_interactions: List[Dict],
                     failed_interactions: List[Dict]) -> str:
        """Generate improved prompt based on feedback patterns"""

        evolution_prompt = f"""
        Current system prompt:
        {self.base_prompt}

        Successful interactions (high ratings):
        {self._format_examples(successful_interactions[:5])}

        Failed interactions (low ratings):
        {self._format_examples(failed_interactions[:5])}

        Based on this feedback, suggest an improved version of the system prompt
        that would increase success rate. Focus on patterns in successful examples
        and avoid patterns from failed examples.

        Return only the improved prompt text.
        """

        improved_prompt = self.llm.invoke(evolution_prompt).content

        self.prompt_versions.append(improved_prompt)
        self.version_performance.append(0.0)  # Will be updated

        return improved_prompt

    def _format_examples(self, interactions: List[Dict]) -> str:
        return "\n\n".join([
            f"Input: {i['input']}\nOutput: {i['output']}\nFeedback: {i['feedback']}"
            for i in interactions
        ])
```

## Key Benefits

### 🎯 Improved Performance Over Time

- **Continuous optimization**: System gets better with each interaction
- **Data-driven improvements**: Changes based on real-world evidence
- **Automatic discovery**: Finds effective strategies without manual tuning
- **Compounding gains**: Each improvement builds on previous ones

### 👤 Personalization

- **Individual preferences**: Learn what works for each user
- **Segment adaptation**: Different strategies for different user types
- **Context awareness**: Adapt based on usage patterns and history
- **Dynamic adjustment**: Change approach based on real-time signals

### 🔄 Domain Adaptation

- **Drift resilience**: Adapts as environment changes
- **Evolving requirements**: Handles new patterns and use cases
- **Transfer learning**: Apply insights across similar scenarios
- **Generalization**: Balance specific adaptations with broad applicability

### 📊 Measurable Impact

- **Quantifiable improvement**: Track learning rate and performance gains
- **A/B testing validation**: Empirically verify improvements
- **ROI calculation**: Measure value of learning system
- **Insight generation**: Understand what works and why

## Trade-offs

### ⚠️ Implementation Complexity

**Issue**: Adaptive learning adds significant complexity to system architecture

**Impact**:
- More components to build, test, and maintain
- Complex debugging of learning-related issues
- Need for data storage and processing infrastructure
- Coordination between learning and production systems

**Mitigation**:
- Start simple: Begin with basic feedback collection and manual analysis
- Use existing frameworks: Leverage tools like LangSmith for feedback
- Incremental rollout: Add learning components gradually
- Clear separation: Isolate learning logic from core functionality

### 📊 Data Requirements

**Issue**: Effective learning requires substantial amounts of quality feedback

**Impact**:
- Cold start problem: Poor initial performance before data accumulates
- Data collection overhead: Need instrumentation and storage
- Quality challenges: Noisy or biased feedback degrades learning
- Privacy concerns: Storing user interactions raises data governance issues

**Mitigation**:
- Seed with manual examples: Start with curated high-quality data
- Active learning: Strategically request feedback on uncertain cases
- Data cleaning: Filter and validate feedback before learning
- Privacy-preserving: Anonymize and aggregate data appropriately

### ⏱️ Convergence Time

**Issue**: Learning optimal strategy may take time

**Impact**:
- Delayed value: Benefits not immediate
- Suboptimal performance: During learning phase, performance may vary
- Exploration cost: Testing strategies consumes resources
- User patience: Users may not tolerate learning period

**Mitigation**:
- Strong baseline: Start with well-tuned initial configuration
- Transfer learning: Leverage knowledge from similar systems
- Accelerated learning: Use simulation or synthetic data
- Transparent communication: Set expectations about improvement timeline

### 🔄 Overfitting Risk

**Issue**: System may adapt too specifically to recent feedback

**Impact**:
- Poor generalization: Doesn't work on new cases
- Temporal bias: Overweights recent patterns
- User bias: Learns idiosyncratic preferences of active users
- Drift into local optima: Gets stuck in suboptimal strategies

**Mitigation**:
- Regularization: Limit adaptation magnitude
- Diverse data: Ensure feedback from varied sources
- Holdout validation: Test on data not used for learning
- Decay old learnings: Give more weight to diverse patterns

## Best Practices

### 1. Start with Strong Baselines

```python
class AdaptiveLearningSystem:
    def __init__(self, baseline_config):
        # Begin with well-tuned initial configuration
        self.config = baseline_config
        self.performance_threshold = baseline_config.expected_performance

    def adapt(self, proposed_changes):
        # Only accept changes that improve on baseline
        if self.validate(proposed_changes) > self.performance_threshold:
            self.config.update(proposed_changes)
            self.performance_threshold = self.current_performance
```

### 2. Implement Proper Validation

```python
class ABTestValidator:
    def validate_adaptation(self, new_strategy, control_strategy,
                           min_samples=100, confidence=0.95):
        """Statistical validation before deployment"""

        control_results = self.test_strategy(control_strategy, min_samples // 2)
        treatment_results = self.test_strategy(new_strategy, min_samples // 2)

        # Statistical significance test
        is_significant = self.statistical_test(
            control_results,
            treatment_results,
            confidence
        )

        is_better = treatment_results.mean() > control_results.mean()

        return is_significant and is_better
```

### 3. Balance Exploration and Exploitation

```python
def select_strategy(self, context):
    """Balance trying new approaches vs using known good ones"""

    # Epsilon-greedy: Explore with probability epsilon
    if random.random() < self.epsilon:
        # Exploration: Try random or novel strategy
        return self.sample_exploration_strategy()
    else:
        # Exploitation: Use best known strategy
        return self.get_best_strategy(context)
```

### 4. Monitor Learning Metrics

```python
class LearningMonitor:
    def track_metrics(self, iteration):
        metrics = {
            "learning_rate": self.calculate_learning_rate(),
            "performance_delta": self.current - self.baseline,
            "adaptation_success_rate": self.successful_adaptations / self.total_adaptations,
            "exploration_rate": self.exploration_count / self.total_count,
            "data_quality": self.feedback_signal_to_noise(),
        }

        self.log_metrics(iteration, metrics)
        self.alert_if_anomalous(metrics)
```

### 5. Implement Rollback Mechanisms

```python
class VersionController:
    def commit_adaptation(self, new_config):
        # Save current config before changing
        self.save_version(self.current_config, self.version_number)
        self.version_number += 1

        # Apply new config
        self.current_config = new_config

        # Monitor performance
        if self.performance_degrades():
            self.rollback(self.version_number - 1)

    def rollback(self, version_number):
        self.current_config = self.load_version(version_number)
        self.alert("Rolled back to version {version_number}")
```

## Performance Metrics

Track these metrics to evaluate adaptive learning systems:

### Learning Progress Metrics

- **Learning rate**: How quickly performance improves (Δ performance / Δ time)
- **Convergence time**: Time to reach stable optimal performance
- **Sample efficiency**: Performance gain per feedback sample
- **Improvement magnitude**: Total improvement over baseline

### Adaptation Quality Metrics

- **Adaptation success rate**: % of adaptations that improve performance
- **Performance delta**: Current performance - baseline performance
- **Generalization score**: Performance on holdout vs. training data
- **Stability**: Variance in performance over time

### Operational Metrics

- **Feedback collection rate**: Feedback samples per time period
- **Validation coverage**: % of adaptations validated before deployment
- **Rollback frequency**: How often changes must be reverted
- **Data quality score**: Signal-to-noise ratio in feedback

### Business Metrics

- **User satisfaction**: Ratings, NPS, sentiment over time
- **Task success rate**: % of user goals achieved
- **Engagement**: Return rate, session duration, feature usage
- **ROI**: Value of improvements vs. cost of learning system

## Example Scenarios

### Scenario 1: Chatbot Response Improvement

```
Initial State (Day 1):
User: "How do I cancel my subscription?"
Agent: [Uses generic corporate response]
User Rating: 2/5 ("Too formal and robotic")

After Learning (Week 2):
Pattern Identified: Users prefer friendly, empathetic tone
Adaptation: Evolved prompt to include warmth and empathy

User: "How do I cancel my subscription?"
Agent: [Friendly, helpful response with empathy]
User Rating: 5/5 ("Exactly what I needed!")

Continuous Improvement (Week 4):
Pattern Identified: Different responses work for different cancel reasons
Adaptation: Personalized based on user's stated reason

Frustrated User: [Gets immediate refund offer]
Cost-saving User: [Gets cheaper plan option]
Both achieve goals with high satisfaction
```

### Scenario 2: Recommendation System Tuning

```
Initial State:
Recommendation Strategy: Collaborative filtering with fixed parameters
Average Click-Through Rate (CTR): 3.2%

Learning Cycle 1 (Week 1):
Feedback: CTR varies by time of day and user segment
Adaptation: Tune parameters per time/segment

Result:
Morning commuters: More news content → CTR: 4.1%
Evening users: More entertainment → CTR: 4.5%
Average CTR: 3.8% (+0.6%)

Learning Cycle 2 (Week 3):
Feedback: Users engage more with diverse recommendations
Adaptation: Add diversity parameter to ranking

Result:
Increased session time: 7.2 min → 8.4 min
Higher satisfaction scores
Average CTR: 4.2% (+1.0% total)
```

### Scenario 3: Code Assistant Optimization

```
Initial State:
Assistant: Provides verbose explanations with every code snippet
Acceptance Rate: 62%

Pattern Analysis:
- Expert users: Skip explanations (45% acceptance)
- Beginner users: Read explanations (88% acceptance)
- Mid-level users: Mixed behavior (65% acceptance)

Adaptation:
Implemented user profiling based on:
- Code complexity patterns
- Explanation click-through rate
- Edit distance on suggestions

Result After Learning:
- Expert users: Concise code only → 78% acceptance (+33%)
- Beginner users: Detailed explanations → 91% acceptance (+3%)
- Mid-level users: Collapsible details → 79% acceptance (+14%)
- Overall: 82% acceptance rate (+20%)
```

## Advanced Patterns

### 1. Online Learning

Continuously update model with each interaction:

```python
class OnlineLearner:
    """Update model incrementally with each feedback"""

    def __init__(self, model, learning_rate=0.01):
        self.model = model
        self.learning_rate = learning_rate

    def process_feedback(self, input, output, feedback_score):
        """Immediate learning from single example"""

        # Calculate error/loss
        loss = self.calculate_loss(output, feedback_score)

        # Update model incrementally
        self.model.update(input, loss, self.learning_rate)

    def calculate_loss(self, output, feedback_score):
        # Convert feedback to loss signal
        return 1.0 - (feedback_score / 5.0)  # Assuming 1-5 scale
```

### 2. Meta-Learning (Learning to Learn)

Learn optimal learning strategies:

```python
class MetaLearner:
    """Learn which learning strategies work best"""

    def __init__(self):
        self.learning_strategies = [
            "aggressive_adaptation",
            "conservative_adaptation",
            "exploration_focused",
            "exploitation_focused"
        ]
        self.strategy_performance = {s: [] for s in self.learning_strategies}

    def select_learning_strategy(self, context):
        """Choose learning approach based on context"""

        # Analyze which strategy works best in this context
        context_type = self.classify_context(context)

        # Get historical performance for this context type
        performance = self.get_performance_by_context(context_type)

        # Select best strategy for this context
        return max(performance, key=performance.get)
```

### 3. Transfer Learning

Apply learning from one domain to another:

```python
class TransferLearner:
    """Transfer knowledge across related tasks"""

    def __init__(self):
        self.task_models = {}
        self.shared_knowledge = {}

    def train_on_task(self, task_id, feedback_data):
        """Learn from specific task"""

        # Extract task-specific patterns
        task_patterns = self.extract_patterns(feedback_data)

        # Identify generalizable patterns
        general_patterns = self.identify_general_patterns(task_patterns)

        # Update shared knowledge
        self.shared_knowledge.update(general_patterns)

        # Initialize new task with shared knowledge
        if task_id not in self.task_models:
            self.task_models[task_id] = self.shared_knowledge.copy()
```

### 4. Multi-Armed Bandit with Context

Context-aware exploration and exploitation:

```python
class ContextualBandit:
    """Learn optimal strategy per context"""

    def __init__(self, contexts, strategies):
        self.contexts = contexts
        self.strategies = strategies
        # Track performance for each (context, strategy) pair
        self.counts = defaultdict(lambda: defaultdict(int))
        self.rewards = defaultdict(lambda: defaultdict(float))

    def select_strategy(self, context, epsilon=0.1):
        """Choose strategy based on context and exploration rate"""

        if random.random() < epsilon:
            # Explore
            return random.choice(self.strategies)
        else:
            # Exploit: best strategy for this context
            avg_rewards = {
                s: self.rewards[context][s] / max(self.counts[context][s], 1)
                for s in self.strategies
            }
            return max(avg_rewards, key=avg_rewards.get)

    def update(self, context, strategy, reward):
        """Update knowledge for (context, strategy) pair"""
        self.counts[context][strategy] += 1
        self.rewards[context][strategy] += reward
```

## Comparison with Related Patterns

| Pattern | Focus | Learning Speed | Data Needs | Use Case |
|---------|-------|----------------|------------|----------|
| **Adaptive Learning** | Continuous improvement | Gradual | High | Long-term optimization |
| **Evaluation/Monitoring** | Performance tracking | N/A | Medium | Quality assurance |
| **Error Recovery** | Failure handling | Immediate | Low | Robustness |
| **Memory Management** | Context retention | N/A | Medium | Long conversations |

### When to Combine Patterns

- **Adaptive Learning + Evaluation**: Monitor metrics to guide learning
- **Adaptive Learning + Error Recovery**: Learn from failure patterns
- **Adaptive Learning + Memory**: Personalize based on conversation history

## Common Pitfalls

### 1. Insufficient Data

**Problem**: Learning from too few examples leads to overfitting

**Solution**:
- Set minimum sample sizes before adapting
- Use regularization techniques
- Bootstrap with synthetic or transferred data
- Be patient and accumulate data before aggressive learning

### 2. Poor Feedback Quality

**Problem**: Noisy or biased feedback degrades learning

**Solution**:
- Validate feedback sources
- Filter outliers and spam
- Weight feedback by reliability
- Use multiple feedback signals
- Cross-validate with objective metrics

### 3. Learning Too Fast

**Problem**: Rapid changes cause instability and confusion

**Solution**:
- Limit adaptation magnitude per cycle
- Use gradual rollout (e.g., 5% → 25% → 100%)
- Require statistical significance before changes
- Implement cooling periods between adaptations

### 4. Ignoring Context

**Problem**: Applying learnings inappropriately across contexts

**Solution**:
- Segment by user type, task type, time, etc.
- Maintain context-specific models
- Validate adaptations in each context
- Use contextual bandits or hierarchical models

### 5. Lack of Rollback

**Problem**: No way to recover from bad adaptations

**Solution**:
- Version all configurations
- Monitor performance continuously
- Set degradation thresholds for auto-rollback
- Maintain known-good fallback configurations

## Conclusion

The Adaptive Learning pattern enables AI agents to continuously improve through experience, feedback, and experimentation. By implementing systematic feedback collection, pattern analysis, strategy adaptation, and validation, agents can optimize their behavior over time without manual intervention.

**Use Adaptive Learning when:**
- System has many repeated interactions over time
- User needs or domain characteristics evolve
- Feedback collection is feasible (explicit or implicit)
- Performance improvement justifies complexity
- Validation and rollback mechanisms can be implemented

**Implementation checklist:**
- ✅ Design feedback collection mechanism (explicit and implicit)
- ✅ Build pattern analysis pipeline (success factors, failure modes)
- ✅ Implement adaptation engine (prompt evolution, parameter tuning)
- ✅ Create validation framework (A/B testing, gradual rollout)
- ✅ Set up monitoring and alerting (learning metrics, performance)
- ✅ Implement version control and rollback (safety mechanisms)
- ✅ Balance exploration vs exploitation (don't get stuck)
- ✅ Handle cold start with strong baseline (don't start poorly)
- ✅ Segment by context (personalization and appropriate adaptation)
- ✅ Prevent overfitting (regularization, holdout validation)

**Key Takeaways:**
- 🎯 Adaptive learning enables continuous performance improvement
- 🔄 Feedback loop: collect → analyze → adapt → validate → iterate
- 📊 Data quality and quantity are critical for effective learning
- ⚖️ Balance exploration (trying new things) and exploitation (using what works)
- 🧪 Always validate adaptations before full deployment
- 🛡️ Implement rollback mechanisms for safety
- 👤 Personalization through context-aware adaptation
- 📈 Track learning metrics to measure progress
- 🎓 Multiple learning approaches: RL, few-shot, optimization, evolution
- ⚠️ Trade-off: Better performance vs. complexity and data requirements

---

*Adaptive learning transforms static AI agents into evolving systems that get smarter with every interaction—continuously optimizing to serve users better while adapting to changing needs and environments.*

## Corporate SSL proxy note

If you're behind a corporate SSL-inspecting proxy, run examples with:

```bash
AGENTIC_DISABLE_SSL=1 bash run.sh
```

