Designing Agentic Loops
7 hours ago
- #AI Agents
- #LLMs
- #Coding Tools
- Coding agents like Claude Code and OpenAI’s Codex CLI represent a significant advancement in using LLMs for writing and executing code.
- Designing agentic loops is a critical skill for leveraging coding agents effectively, involving clear goals and iterative tools.
- YOLO mode allows agents to run commands without approval, increasing productivity but posing risks like data loss or exfiltration.
- To mitigate risks, options include running agents in secure sandboxes, using third-party environments like GitHub Codespaces, or accepting the risk.
- Choosing the right tools for the loop is essential, with shell commands being particularly effective for coding agents.
- Tightly scoped credentials should be issued to limit potential damage, preferably for test or staging environments with budget limits.
- Agentic loops are best suited for problems requiring trial and error, such as debugging, performance optimization, and dependency upgrades.
- Automated tests significantly enhance the value of coding agents by providing clear success criteria.
- The field of designing agentic loops is very new, with Claude Code released in February 2025, and there is much to explore for optimal use.