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Self-Improving Agents

Build agents that learn from experience and improve over time

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Building Self-Improving Agents

Four core components enable self-improvement: Memory Store (save interactions), Evaluator (assess quality), Learning Engine (update from experience), Monitor (track progress). Here's production-ready code for each component.

Interactive: Code Explorer

Explore implementation code for each component:

class ExperienceMemory:
    def __init__(self, max_size=10000):
        self.memory = []
        self.max_size = max_size
    
    def store(self, interaction):
        """Store interaction: query, response, outcome"""
        self.memory.append({
            'query': interaction.query,
            'response': interaction.response,
            'outcome': interaction.outcome,  # success/fail
            'feedback': interaction.feedback,
            'timestamp': time.time()
        })
        if len(self.memory) > self.max_size:
            self.memory.pop(0)  # Remove oldest
    
    def sample(self, batch_size=32, balanced=True):
        """Sample batch for training"""
        if balanced:
            # Equal success/fail samples
            successes = [x for x in self.memory if x['outcome'] == 'success']
            failures = [x for x in self.memory if x['outcome'] == 'failure']
            batch = random.sample(successes, batch_size//2) + \
                    random.sample(failures, batch_size//2)
        else:
            batch = random.sample(self.memory, batch_size)
        return batch

Integration Pattern

Connect all four components into a complete self-improving agent:

# Initialize components
memory = ExperienceMemory(max_size=10000)
evaluator = OutputEvaluator(llm=gpt4)
learner = ContinuousLearner(agent, memory, evaluator)
monitor = ImprovementMonitor()

# Agent interaction loop
async def run_agent_with_learning(query):
    # 1. Generate response
    response = await agent.run(query)
    
    # 2. Evaluate quality
    eval_result = await evaluator.evaluate(query, response)
    
    # 3. Store in memory
    memory.store({
        'query': query,
        'response': response,
        'outcome': 'success' if eval_result['passed'] else 'failure',
        'feedback': eval_result
    })
    
    # 4. Track metrics
    monitor.track_interaction({
        'outcome': 'success' if eval_result['passed'] else 'failure',
        'response_time': response.time
    })
    
    return response

# Background learning loop (runs every hour)
asyncio.create_task(learner.improve_continuously(interval=3600))

Best Practices

→
Start Small: Begin with 100-500 interactions before first learning cycle. Prevents overfitting to sparse data.
→
Balanced Sampling: Equal success/failure examples in training batches. Prevents bias toward dominant outcome.
→
Monitor Metrics: Track accuracy, satisfaction, response time. Alert on degradation (indicates bad learning).
→
Incremental Updates: Small, frequent updates (hourly) beat large, rare updates (weekly). Faster adaptation, safer rollback.
→
Versioning: Save agent snapshots before updates. Roll back if performance drops > 5%.
💡
Production Tip

Run learning in staging first. Deploy to 10% of users. Monitor metrics for 24 hours. If improvement > 5% and no issues, roll out to 100%. This A/B testing approach catches problems before they impact all users. Typical improvement cycle: 3-7 days from staging to full deployment.

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