The Unreasonable Effectiveness of an LLM Agent Loop with Tool Use
a year ago
- #AI Programming
- #LLM Agents
- #Automation
- The article discusses the effectiveness of a simple LLM (Large Language Model) agent loop with tool use in an AI Programming Assistant called Sketch.
- The core loop involves sending user input to an LLM, which can return output or tool calls (like bash commands), and handling those tool calls or prompting for new user input.
- Using just one general-purpose tool (bash), current models like Claude 3.7 Sonnet can solve many problems efficiently, sometimes in 'one shot.'
- The assistant can handle tasks like esoteric git operations, git merges, and type checker errors, adapting to different environments and tools.
- Additional tools beyond bash improve quality, speed, and developer workflows, though text editing tools can be tricky.
- Agent loops are expected to be incorporated into more day-to-day automation tasks that are too specific or unstable for traditional automation.
- The author envisions more custom, ad hoc LLM agent loops being used for various automation tasks.