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Self-Improving Agents

Meta-Learning for Agents

Implement meta-learning for agents that adapt to new tasks quickly

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What is Meta-Learning?

Meta-learning is "learning to learn"—training agents on multiple tasks so they develop the ability to quickly adapt to new, unseen tasks with minimal examples. A meta-learned agent exposed to 100 different customer support scenarios can handle the 101st with just 5 examples, while standard training needs 1000+.

The Adaptation Challenge

❌ Standard Agent
• 10,000+ examples per task
• 2-4 weeks training time
• Doesn't transfer knowledge
• Expensive retraining
• Poor on novel tasks
✅ Meta-Learning Agent
• 5-50 examples per task
• 5-30 minutes adaptation
• Transfers across tasks
• Rapid deployment
• Strong generalization

Interactive: Learning Speed Comparison

Compare how different learning approaches adapt to new tasks:

🐌

Standard Learning

Train from scratch on each new task. Requires thousands of examples and weeks of training.

Examples Needed
10,000
Adaptation Time
2-4 weeks

Real-World Applications

→
Multi-Domain Support: Agent trained on 50 support domains (tech, healthcare, finance) adapts to new domain (legal) with 10 examples in 15 minutes.
→
Personalization: Agent meta-trained on 1000 users adapts to new user's communication style and preferences after 5 interactions.
→
Task Automation: Agent exposed to 100 different workflows can automate new workflow with 3-5 demonstrations instead of full training.
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Key Insight

Meta-learning inverts the training paradigm. Instead of training one agent per task, you train one agent on many tasks. Upfront cost is higher (need diverse training tasks), but deployment cost drops dramatically. Result: 200x faster adaptation for new tasks after initial meta-training investment.