The evolution of AI applications has progressed from simple prompt-response models to complex agentic systems. Initially, developers relied on single LLM calls. The introduction of function calling allowed models to interact with external tools. This evolved into prompt chaining, where developers sequence LLM calls to handle multi-step tasks. The latest advancement is the agent, which embeds an LLM within a loop, empowering the model to autonomously decide when to stop.

Agents operate on a perceive-reason-act-observe cycle. The model receives the current state, reasons about the next action, triggers an action (like a tool call or a handoff to another agent), and observes the result before iterating. This closed loop enables the model to adapt based on real-time feedback, a departure from fixed workflows. This autonomous decision-making is the core differentiator, enabling AI to move from answering questions to actively performing tasks.

Open-source efforts like PR-AF demonstrate this potential, achieving high rankings in code review benchmarks by employing parallel reviewer agents and verifying findings against source code. This approach is significantly more cost-effective than closed-source alternatives. The development of agents represents a shift towards AI systems that can manage their own execution flow, leading to more sophisticated applications in software development and beyond.