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Advanced Reasoning Techniques
Self-Improving Agents
Build agents that learn from experience and improve over time
Your Progress
0 / 5 completedWhy Self-Improvement Matters
Traditional AI agents are static—they perform the same way on day 1 and day 1000. Self-improving agents learn from every interaction, recognize patterns in failures, and automatically optimize their strategies. Result: Accuracy improves 20-40% over time without manual intervention.
The Improvement Gap
❌ Static Agent
• Same performance forever
• Repeats mistakes
• Requires manual updates
• No adaptation to users
• Fixed strategies
✅ Self-Improving Agent
• Improves with usage
• Learns from failures
• Auto-optimizes strategies
• Personalizes to users
• Discovers new patterns
Interactive: The Improvement Cycle
Explore the four stages of continuous improvement:
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Execute
Agent performs tasks and generates outputs
Example
Agent answers user queries, completes tasks, makes decisions
Cycle repeats:Execute → Evaluate → Reflect → Learn → Execute...
Real-World Impact
→
Customer Support Agent: Learns from 10K conversations. Accuracy: 72% → 91% in 3 months. Avg resolution time: 8min → 3min.
→
Code Review Agent: Identifies patterns in bugs. False positive rate: 35% → 12% after reviewing 5K PRs. Learns project-specific conventions.
→
Research Agent: Discovers which sources are most reliable. Citation accuracy: 81% → 96%. Learns domain expertise through repeated research tasks.
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Key Insight
Self-improvement isn't about replacing human oversight—it's about reducing manual intervention. Agents learn optimal strategies from data (successful interactions) instead of requiring developers to hardcode every edge case. Result: Agents that improve 10x faster than manual updates allow.