Self-Learning Agentic AI for Code Generation and Debugging

Self-Learning Agentic AI merges autonomous agents with reinforcement learning and contextual decision-making to enable dynamic, goal-driven behavior. These agents can independently plan, reason, and act in software environments, continuously improving through self-learning methods like RLHF and online fine-tuning. Rather than just executing commands, they deconstruct complex problems, explore codebases, and iteratively adapt their strategies. This ebook focuses on applying these capabilities to code generation and debugging, one of the most challenging domains in modern software development.

Autonomous Decision-Making: Agentic AI agents assess goals, plan tasks, and operate independently in evolving software contexts.
Self-Improving Architecture: Leveraging techniques like reinforcement learning from human feedback (RLHF), agents evolve without manual reprogramming.
End-to-End Code Intelligence: From analyzing stack traces to generating, testing, and patching code, agents manage the full debugging lifecycle.
Context-Aware Adaptation: Agents use runtime data, prior outcomes, and prompt refinements to enhance reasoning and performance over time.