Automate agent development lifecycle with Gemini Enterprise Agent Platform

A practical tutorial guides developers through building an 'Industry Watch' agent, a sector-intelligence analyst for semiconductor stocks, by reconciling company press releases with SEC filings. The process covers six stages: setup, build, deploy, govern, evaluate, and publish, all managed through prompts to a coding agent.

The Agents CLI installs lifecycle skills, such as scaffolding, deployment, and evaluation, into general-purpose coding agents. Coupled with the Developer Knowledge MCP, it allows agents to access current platform documentation, ensuring accuracy beyond training data.

Developers can describe the desired agent and its tools in prompts, and the coding agent scaffolds the project and writes Python functions for tools like fetching SEC disclosures and reconciling claims against filings. The `reconcile_claims_vs_disclosures` tool specifically joins data, categorizes records, and scores materiality without model intervention.

The developed agent can be run locally to test its functionality, such as querying changes for specific companies. The process includes opening a playground for follow-up interactions, demonstrating the agent's ability to call tools and return reconciled data.

The Gemini Enterprise Agent Platform is designed to simplify the development of production-ready AI agents by integrating the entire lifecycle, from setup to publishing, within a coding agent using the new Agents CLI skills. This aims to prevent AI projects from getting stuck in prototype mode by reducing the need to switch between multiple tools and consoles.

Building an agent like 'Industry Watch' requires a specific architecture that uses deterministic tools to fetch live data from sources like SEC filings and news feeds, then reconciles this information. This approach ensures accuracy and prevents the model from fabricating details, unlike a standard chatbot which may rely on outdated training data.