Agentic Loops
Agentic loops are a design pattern in AI-assisted software development where an AI agent iteratively performs tasks, evaluates results, and refines its approach based on feedback. Rather than executing a single instruction and stopping, the agent enters a cycle of action and reflection. This allows the agent to correct errors, handle unexpected outcomes, and progressively improve its output toward a defined goal.
Core Mechanism
The pattern operates through repeated cycles of execution and evaluation. An agent performs an action—such as writing code, running tests, analyzing errors, or implementing fixes—then assesses the outcome against success criteria. Based on this evaluation, the agent either continues toward the goal, backtracks to try a different approach, or requests clarification from the user. The loop terminates when the goal is achieved or a stopping condition is met.
Practical Applications
Agentic loops are commonly used in code generation and debugging workflows. When an AI generates code and tests fail, the loop allows the agent to read error messages, identify issues, and revise the implementation without human intervention between iterations. This reduces the need for manual back-and-forth debugging and enables more complex problem-solving than single-pass generation.
Related Techniques
The “Ralph” technique is an example of a method that implements agentic loops for specific coding tasks. Similar iterative approaches are used across AI agent frameworks and autonomous systems where self-correction and adaptive problem-solving are required.
Source Notes
- 2026-04-07: AI Guided Software Development Leveraging Claude Code Agent Skills for · ▶ source
- 2026-04-08: Open Source AI Agents Revolutionizing Development Workflows and · ▶ source
- 2026-04-10: LiteParse LlamaIndexs Agentic Document Processing Solution for LLMs · ▶ source
- 2026-04-29: Optimizing LLM Agent · ▶ source