An AI agent is only as good as the tools you give it. Company data is a perfect agent tool: bounded, factual and easy to schema — as long as you resist the temptation to hand the model a free-text database.
Four tools, not one
Define search_companies, resolve_company, get_company and list_signals. Each has a narrow input schema and returns a small, typed object. Small tools make agent behaviour predictable and make failures debuggable.
Never expose a raw SQL tool to a customer-facing agent. It invites unbounded queries, leaks schema details and makes cost unpredictable.
Resolve before you reason
Force the agent through resolution: no enrichment without an identifier. If several candidates match, return them and let the agent ask the user which one, instead of silently picking the first.
Include the register, the identifier and the last-updated date in every tool response. Then instruct the agent to cite them. Users forgive a missing field; they do not forgive a confident wrong one.
Keep humans in the loop where it costs money
Reading data can be fully automated. Writing to a CRM, sending outreach or changing an owner should require confirmation until the agent has a track record.
Log every tool call with inputs, outputs and the resulting action. That log is both your debugging tool and your compliance story.
Frequently asked questions
Which agent frameworks work with this pattern?
Any framework with function or tool calling: OpenAI, Anthropic, Google, LangGraph, CrewAI or a custom loop. The tool contract matters far more than the framework.
How do I stop an agent from inventing company details?
Only allow it to state fields returned by a tool call, require a citation per claim, and have it say the data is unavailable when a field is missing.
