Agents That Learn After Deployment: How Self-Learning AI Agents Improve from Every User Interaction Without Fine-Tuning

​Most AI agents stop learning the moment they're deployed.

​When agents fail, teams are forced to review conversations, update prompts, refine workflows, and redeploy improvements. The more users you have, the more manual work it creates.

​What if agents could improve themselves?

​Join CopilotKit engineers for a live deep dive into Self-Improvement, a new capability that enables agents to learn directly from production interactions through Continuous Learning from Human Feedback (CLHF).

​We'll show how we built a system that turns real user interactions into learning signals, allowing agents to continuously improve over time without traditional fine-tuning cycles.

​In this session, we'll cover:

• ​How agent self-improvement works under the hood

• ​How user interactions become learning signals

• ​Continuous Learning from Human Feedback (CLHF)

• ​Prompt Mutation and automated improvement loops

• ​Per-user adaptation and personalized agent behavior

• ​Why this approach eliminates traditional fine-tuning workflows

• ​How to request early access and start training agents on your own data

​Whether you're building customer-facing AI agents, internal copilots, or complex agentic workflows, you'll leave with a practical understanding of how agents can learn, adapt, and improve after deployment.