As AI Agent applications rapidly evolve, developers are moving from “Prompt Engineering” toward more structured system design. Google’s latest Cloud Tech guide introduces five major design patterns to establish more reliable and reusable development frameworks for AI Agent skills.
Authored by Shubham Saboo and Lavi Nigam, this sharing highlights that as SKILL.md becomes the standard adopted by over 30 tools, the focus has shifted from “how to package” to “how to design internal logic,” marking a new engineering phase in AI development.
With SKILL.md becoming the standard, AI Agent skills are moving toward modularization
The concept of Agent Skills was first proposed by Anthropic and has now developed into an open-source standard. Its core is to enable AI Agents to load capabilities on demand through modular folder structures and SKILL.md files.
SKILL.md not only contains commands and metadata but can also reference external resources, allowing Agents to adopt a “progressive disclosure” approach during task execution, avoiding excessive context inflation and improving efficiency and accuracy.
Currently, more than 30 tools, including Claude Code, Gemini CLI, and Cursor, have adopted this standard, demonstrating its rapid adoption as the foundational architecture for AI Agent development.
From Prompt Hacks to Design Patterns: Analyzing Five Core Architectures
Google Cloud Tech points out that many developers still focus excessively on YAML structures and directory design, but the real key lies in “internal skill logic.” To address this, the team proposes five reusable design patterns to help developers build stable and predictable AI systems.
Tool Wrapper: Making AI an Expert Instantly
Tool Wrapper is the most basic pattern, encapsulating specific tools or frameworks as skills so that AI can quickly invoke specialized knowledge when needed.
For example, when developing with FastAPI, API specifications and best practices can be placed in the references/ directory, loaded only when relevant tasks are triggered, preventing the main prompt from becoming overly bloated.
Generator: The Key Engine for Standardized Output
The Generator pattern is suitable for scenarios requiring consistent output, such as API documentation, automatic commit messages, or project template generation.
Its core is to place templates in assets/ and combine them with style guides in references/, with skills responsible for filling in content. This approach ensures outputs are both standardized and flexible.
Reviewer: Establishing Quantifiable Checking Mechanisms
The Reviewer pattern separates “checking standards” from “execution logic.” Developers can create checklists in references/, such as code quality or security standards.
AI evaluates based on these standards and outputs structured results. For example, replacing with OWASP security guidelines can quickly turn it into a vulnerability review tool, especially useful for automated PR reviews.
Inversion: From Respondent to Questioner
Inversion pattern overturns the traditional AI process of directly generating answers, forcing the Agent to first perform structured questioning.
By imposing a “must complete before proceeding” restriction, AI must gradually gather complete requirements, making it especially suitable for project planning and other contexts requiring high levels of understanding, effectively avoiding errors caused by insufficient information.
Pipeline: The Central Control for Complex Tasks
The Pipeline pattern is designed for multi-step tasks, enforcing execution order and checkpoints, with optional user confirmation mechanisms.
For example, in document generation workflows, confirming the docstring must happen before final assembly. This pattern ensures each stage meets expectations, preventing errors from skipped steps.
Modular Composition: Advanced Ways to Combine AI Agent Skills
These five patterns are not isolated but can be flexibly combined. For example:
- Pipeline can embed Reviewer for self-verification
- Generator can be paired with Inversion to first gather parameters
Google’s Agent Development Kit (ADK) offers native support, using SkillToolset to load only necessary modules at runtime, further optimizing token usage.
Additionally, the official decision tree helps developers choose appropriate patterns based on application scenarios, significantly lowering the design barrier.
AI Development Enters the Engineering Era: Reliability Is Key
Google Cloud Tech emphasizes: “Stop trying to cram complex and fragile instructions into a single system prompt.”
This statement highlights a major shift in AI development—from early trial-and-error prompt hacks to structured engineering design methods with principles, similar to the evolution of software engineering toward design patterns.
Community reactions on X (Twitter) have been enthusiastic, with many developers calling this “the starting point for AI system design,” and even saying these patterns can effectively prevent Agents from becoming unmanageable “spaghetti.”
Currently, the Agent Skills standard is fully open source, and Google’s ADK provides comprehensive documentation and examples (google.github.io/adk-docs) to help developers get started quickly.
This article, “Google Cloud Launches Five Major Design Patterns: Building Highly Reliable AI Agent Skills, Bidding Farewell to Prompt Hack Era,” first appeared on ABMedia.
