AI agents increasingly operate in long-horizon environments where they call tools, observe failures, and adapt across multiple turns. Yet building an agent, deploying it reliably, and improving it from experience are often separate workflows, with different abstractions and infrastructure at each stage. We present an end-to-end agent learning loop that connects these stages.

Starting from an open-source Strands agent, we deploy the same agent on Amazon Bedrock AgentCore and use scalable, isolated execution to collect feedback-rich trajectories for evaluation and post-training. We then show how recent research can make better use of this experience. MURPHY, our NeurIPS 2026 work, extends GRPO to multi-turn code generation by constructing feedback-conditioned rollout trees and assigning retrospective credit to failed attempts whose feedback enables later success. We also present RAD, a reward-aligned on-policy distillation method that uses task reward to determine when and how strongly teacher guidance should shape the student, rather than treating every teacher token as equally useful.

Through a live coding-agent workflow, attendees will see one agent move from Strands prototype to AgentCore deployment, rollout collection, feedback-aware training, evaluation, and redeployment. The AgentCore RL Toolkit provides the bridge between production agent code and scalable post-training, illustrating a practical path toward agents that iteratively improve from feedback collected in the environments where they operate.