5 Proven Design Patterns for Building Smarter Google AI Agents

Google engineers analyzed the entire Agent Skill ecosystem, from Anthropic codebases to internal Google guidelines, and extracted five recurring design patterns that help developers build more effective AI agents.

Tool Wrapper : Enables an agent to become an expert on any codebase by packaging API conventions into a Skill, loading context only when needed.

Generator : Enforces consistent output by using templated "fill‑in" processes, with separate assets/ for templates and references/ for style guides.

Reviewer : Separates "what to check" from "how to check" by storing modular scoring criteria in review-checklist.md, allowing automated code‑review or security audits.

Inversion : Reverses the usual flow; the agent acts as an interviewer, gathering required context before any generation, using strict gate instructions to prevent premature output.

Pipeline : Defines a hard‑checkpoint workflow where each stage must be approved before proceeding, ensuring no step is skipped in complex tasks.

Pattern 1: Tool Wrapper

This pattern provides on‑demand context about a specific codebase, avoiding hard‑coded API details in system prompts. The Skill monitors user prompts for keywords, dynamically loads files from references/, and applies them as absolute truth.

Example: a wrapper that teaches the agent to write FastAPI code, loading conventions.md only when the agent starts reviewing or writing code.

Pattern 2: Generator

The Generator solves inconsistent document structures by orchestrating a fill‑in process. It loads templates from assets/, reads style guides from references/, asks the user for missing variables, and produces predictable outputs such as API docs or commit messages.

Pattern 3: Reviewer

The Reviewer decouples the checklist from the prompt. Scoring rules are stored in review-checklist.md, allowing the agent to load different checklists (e.g., Python style or OWASP security) without changing the Skill code.

Pattern 4: Inversion

Inversion forces the agent to act as an interviewer, asking structured questions and waiting for answers before any generation. Gate instructions like "do not build until all stages are completed" ensure the agent gathers full context first.

Pattern 5: Pipeline

Pipeline enforces a strict, multi‑step workflow with hard checkpoints. The Skill defines diamond‑gate conditions that require user approval before moving to the next stage, keeping the context window clean by loading only needed assets at each step.

These patterns are not mutually exclusive; they can be combined—for example, a Pipeline Skill may end with a Reviewer step, or a Generator may start with an Inversion step to collect variables.

By leveraging the Agent Development Kit’s progressive disclosure and SkillTools set, agents consume only the tokens required for the active pattern, avoiding brittle monolithic prompts.

Developers are encouraged to adopt these patterns to create modular, reliable, and context‑aware agents using the open‑source Agent Skill specification.