Sequencing the tools an agent calls for multi-step reasoning

Multi-stage reasoning applications (agents) rely on a defined toolset—retrievers, Unity Catalog functions, APIs—that an LLM can select and sequence to gather knowledge or take actions before producing a final answer. Designing these systems means writing clear tool names/descriptions/parameters so the model can correctly choose and order calls, and structuring the chain so each tool's output feeds the next reasoning step.

1 · Learn the must-know

• 1Each tool should have a precise name, description, and input/output schema, since the LLM chooses tools based solely on this metadata, not the underlying code.
• 2Common tool types in Databricks solutions include vector search retrievers for knowledge lookup, Unity Catalog functions (SQL or Python) for structured data/actions, and external API wrappers for real-time or transactional tasks.
• 3Ordering matters: retrieval or lookup tools are typically invoked before generation or action tools so the model has grounded context before reasoning or writing a response.
• 4Agent frameworks (e.g., LangChain agents, Mosaic AI Agent Framework) let the LLM dynamically decide tool order at runtime via function/tool calling rather than a fixed hardcoded sequence, based on the user query and intermediate results.
• 5Poorly written or overlapping tool descriptions cause the agent to select the wrong tool or loop indefinitely, so tools should be scoped narrowly and non-redundant.
• 6For multi-hop tasks, intermediate tool outputs should be passed back into the model's context so later tool calls or the final answer can condition on earlier results.

2 · Check your understanding