Yanfei Zhang proposes Agent-as-Tool, an architectural shift for agentic systems:
Existing agents conflate the tool invocation process with the verbal reasoning process. This tight coupling leads to several challenges:
- The agent must learn tool selection, input construction, and reasoning jointly, which increases training difficulty and noise
- Reasoning often proceeds over noisy, unstructured outputs returned directly from external tools, which degrades answer quality.
For a while, the dominant model has been a single agent doing everything: reasoning, planning, and then directly using a tool like web search. This paper argues that this approach is fundamentally inefficient. It forces the agent to juggle two very different kinds of tasks, the high-level conceptual work of reasoning and the low-level, messy work of interacting with a tool and parsing its raw output. This creates noise and makes the entire process harder to train and less reliable.
This paper offers a simple division of labour. The proposed "Agent-as-tool" framework splits the monolithic agent into a hierarchy of two specialised agents: a "Planner" and a "Toolcaller". The Planner is the strategist; it thinks, breaks down the problem, and decides what information it needs. It then delegates the actual tool-using task to the Toolcaller, which acts as a dedicated specialist. The Toolcaller's only job is to execute the request and return a clean, structured answer.
By decoupling reasoning from execution, the Planner can operate on a cleaner, more abstract level, which, as the results show, significantly improves performance on complex, multi-step problems.