Automate your agent development lifecycle using any coding agent

With Agents CLI skills, you can go through the different phases of the entire agent lifecycle without ever leaving your coding agent. What we're building today: Industry Watch agent This tutorial helps guide a developer on how to build a real Industry Watch agent, a sector-intelligence analyst for semiconductor stocks that reconciles what companies say in the press against what they file with the SEC. We'll walk through the six stages of building this agent end-to-end: Setup: Teach your coding assistant platform skills.

Build: Scaffold the agent and create deterministic data tools. Deploy: Host on a managed runtime with persistent memory. Govern: Lock down identity and screen for prompt injection.

Evaluate: Run automated pass/fail tests for grounding and accuracy. Publish: Make the available agent to Gemini Enterprise. The coding agent produces the commands and code shown in each section.

Stage 1: Teach your Agent Platform Skills A general-purpose coding agent writes fine Python. But it doesn't know ADK's agent classes, the flags to deploy to a managed runtime, or how to attach a security template, and guesses about a fast-moving platform go stale fast. The Agents CLI (an opinionated set of skills and tools for steering the full agent lifecycle) closes that gap.

Install it and run setup: That installs the lifecycle skills into your coding agent: scaffolding, deployment, evaluation, and publishing. The Developer Knowledge MCP lets the agent look up current platform docs instead of relying on training data. Roll both into a single prompt: The coding agent runs the setup, wires up the MCP, and confirms the skills are installed.

Stay in us-central1 throughout, since the code-execution sandbox you'll use later is us-central1 only. Architecture: Why this needs an agent, not a chatbot Every Monday, a competitive-intelligence analyst asks the same question: what materially changed in the semiconductor sector last week, and why does it matter to us? Answering it means holding two stories side by side - what companies say in press releases and news, and what they're required to disclose in SEC filings.

The signal is the gap between them. A plain chatbot can't do this honestly. "Last week" is past its training cutoff, so it invents filing dates and 8-K item numbers.

The answer depends on two live sources that have to be fetched fresh and joined, not recalled. Every claim has to be traced to a real accession number or URL. And press releases are attacker-influenceable text, so a model with no tool boundary has nothing to stop a poisoned headline.