Fast Facts
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The agent now handles natural-language lookups: It finds a customer by name, lists their orders, retrieves totals, and identifies the largest—without hard-coded lookup steps.
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Three narrow, read-only tools keep database access constrained: Parameterised queries, result limits, and JSON outputs make actions easier to understand and test than a general SQL tool.
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A bounded tool-calling loop gives the agent flexibility—with a safety stop: It carries earlier tool results into each model turn and allows multiple calls, while an eight-turn limit helps prevent runaway loops.
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It’s a demo, not production-ready: Ambiguous names, incomplete results, unreliable answers, oversized context, and hidden application errors all need attention. The article recommends testing with known-answer questions and adding stronger safeguards in the next part.
From One Lookup to an Agent
A basic chatbot can answer one database question with one tool call. A multi-tool agent can handle a less tidy request: Find a customer’s largest order, even when the user does not know the order numbers.
With Python, SQLite and Ollama, the model works through the task. First, it finds the customer. Then it lists the customer’s orders and checks each total. In the sample, order 1002 leads at GBP 284.49. The model chooses each next step based on the results it receives.
Narrow Tools Make the System Easier to Control
The agent uses three focused tools rather than a general command that lets the model run any SQL query. That choice limits what the model can do. The tools also use read-only database access, parameterized queries and result limits.
Meanwhile, the program keeps the full conversation and tool results in context. That lets the model use earlier findings instead of guessing. A visible tool trace also helps developers check how the agent reached its answer.
Promising, With Clear Limits
An eight-turn cap can stop a runaway loop. However, it does not limit the size of a single tool result. Ambiguous requests, incomplete data or a mistaken tool call can still produce a poor answer. Developers should test known questions repeatedly and review both the answer and the tool trace.
This design offers a practical starting point for local AI projects. Still, it is a demonstration, not a production-ready system. Before adoption, teams should validate tool inputs, handle software failures clearly and require confirmation before any action changes data.
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