The Web Search Your Agent Inherited Isn't Good Enough

Summary

An agent that keeps a company’s market view current must combine Databricks-hosted internal account records with external web signals such as funding rounds, leadership changes, product launches, and hiring surges. The initial implementation duplicates enrichment logic across Claude Code, Codex, and a direct model API, with inconsistent search results and no shared cost, governance, or audit trail. Omnigent consolidates the agent definition, model, tools, policies, limits, and model-call governance above those harnesses, while letting the engineer select a common search provider. Nimble’s Search API supplies live web data, while its Web Search Agents orchestrate multi-source retrieval and extraction, cross-check sources, return citations, and adapt to difficult sites and recurring retrieval paths. In Nimble’s testing, adding its search increased LLM benchmark accuracy from 46% to 71% and halved web search costs; the proposed Databricks setup then connects internal records and external context under one governance surface.

Takeaways

1. 1

   Omnigent places one agent definition and governance surface above Claude Code, Codex, and direct API execution, eliminating separate enrichment implementations and centralizing model-call cost and audit capture.

2. 2

   Nimble’s Web Search Agents can cross-check multiple sources, return citations, and retrieve data behind JavaScript, filters, and pagination that an ordinary crawler may miss.

3. 3

   Nimble reports that its search raised LLM benchmark accuracy from 46 percent to 71 percent while cutting web search costs in half in its testing.

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