Inner Loop Agents: LLMs Calling Tools Directly

Traditional LLMs require a client to parse and execute tool calls, but inner loop agents allow the LLM to parse and execute tools directly—a paradigm shift. The post explains how inner loop agents work, illustrating the difference between them and traditional LLMs with diagrams. The advantage is that LLMs can concurrently call tools alongside their thinking process, improving efficiency. Reinforcement learning's role in training inner loop agents and the Model Context Protocol (MCP)'s importance in supporting diverse tool use are also discussed. Ultimately, while LLMs can currently use tools, achieving optimal tool use requires specialized model training for best results.