A Complete Guide for AI Developers and Tech Enthusiasts
Artificial intelligence agents are transforming how software systems automate complex tasks. By combining large language models (LLMs) with structured workflows, developers can build systems that not only respond to user requests but reason, plan, and take action independently.
In this guide, we explore how to implement Anthropic’s agent design patterns using the Google Agent Development Kit (ADK). We will break down the concepts, show example implementations, and explain when and why to use each pattern in your own AI projects.
Table of Contents
What Are Agent Design Patterns?
Agent design patterns describe architectural strategies for building intelligent systems in which AI agents act autonomously or collaborate to complete tasks. These patterns help developers organize components such as reasoning, tool use, memory, and workflow control in scalable and maintainable ways. (Google Cloud Documentation)
Anthropic’s article “Building Effective Agents” introduces several core patterns that distinguish interactive, dynamic agents from more static workflows. The difference is key: agents can direct their own reasoning and tool usage, whereas workflows follow predefined, deterministic code paths. (haruiz.github.io)
Google’s ADK is an open-source framework that makes implementing these patterns practical with real code. It provides structured tools for building agent architectures, orchestrating LLM interactions, managing state, and composing multi-agent systems. (Google Cloud)
Understanding Google ADK
Google Agent Development Kit (ADK) is a Python-based framework designed for building, defining, and running AI agents empowered by large language models. It abstracts common tasks such as:
- Managing agent state and persistent memory. (google.github.io)
- Orchestrating communication between sub-agents in a system. (google.github.io)
- Integrating tools and external capabilities like APIs. (Google Cloud)
ADK supports modular architectures so you can focus on patterns of interaction and reasoning rather than boilerplate integration code.
Core Agent Design Patterns With Examples
Below are key design patterns from Anthropic’s conceptual model, implemented with ADK, along with practical use cases and insights.
1. Prompt Chaining
Purpose
Break a complex task into sequential subtasks, processed one step at a time.
How it Works
Each agent handles a part of the task, passing its output on to the next. This reduces reasoning load per LLM call and improves accuracy.
When to Use It
Prompt chaining fits tasks that are logically sequential, such as step-by-step document generation, outline production, or staged reasoning. (haruiz.github.io)
Example (Python snippet)
# Create a series of agents that generate text step by step.
# Each agent refines or transforms the output of the prior one.
The pattern trades some latency for higher precision and control over intermediate results.
2. Routing Agent
Purpose
Direct incoming tasks to the most appropriate specialized agent based on the user input.
How it Works
A central router evaluates the prompt, classifies it, and selects one of several sub-agents designed for specific task types.
Use Case
Customer support systems can route inquiries to different sub-agents for FAQ, troubleshooting, or escalation, improving response relevance. (haruiz.github.io)
3. Parallelization
Purpose
Run multiple agent tasks at once to increase throughput or gather diverse perspectives.
How it Works
You can either:
- Section tasks into independent chunks that different agents process concurrently, or
- Voting by having several models generate answers and choosing the best result.
When to Use It
Complex analysis tasks and aggregated responses benefit from parallel operations. (haruiz.github.io)
4. Orchestrator–Worker
Purpose
Enable dynamic task delegation within a system, where an orchestrator assigns tasks to worker agents based on goals and context.
How it Works
The orchestrator interprets the problem and uses the worker agents to fulfill it without human-defined control flow.
Use Case
Automated workflows where goals are complex, such as multi-stage data extraction or automated content pipelines. (haruiz.github.io)
5. Evaluator–Optimizer Loop
Purpose
Iteratively refine outputs based on structured feedback.
How it Works
One agent evaluates the output of another and provides feedback that drives improvement over multiple rounds.
When to Use It
Tasks requiring quality improvement or optimization, like refining draft content or reranking generated recommendations. (haruiz.github.io)
6. Autonomous Agent
Purpose
Empower an agent to plan and execute a series of actions without predefined workflow code.
How it Works
Agents decide their next steps and use tools or workflows dynamically as needed.
Use Case
Interactive, long-running tasks such as personal assistants that gather data, summarize outcomes, and take actions autonomously. (haruiz.github.io)
Putting It All Together
Using these patterns, you can build powerful AI systems such as:
- Intelligent customer support assistants
- Automated writer bots with quality checks
- Multi-agent orchestration for business operations
- Scalable workflows tied to company data and tools
Deploying via ADK also means leveraging production grade structure, observability, and memory, making your systems enterprise-ready. (Google Cloud)
Best Practices for Developers
To get the most from these patterns:
Define your problem first.
Understanding whether your task benefits from sequential, collaborative, or autonomous reasoning determines which pattern fits best. (Google Cloud Documentation)
Keep prompts clear and purposeful.
Good prompt design ensures agents select correct tools and workflows, improving consistency. (Anthropic)
Test with diverse cases.
Parallelization and voting patterns especially benefit from diverse edge case tests.
Monitor and log agent behavior.
Tracing tool use and memory evolution helps refine agent reliability over time.
Conclusion
Anthropic’s agent design patterns offer a structured way to build intelligent, autonomous systems. When implemented with Google ADK, these patterns move from conceptual blueprints into real, production-ready software. By mastering these patterns, developers can build AI agents that not only respond intelligently but reason effectively and act autonomously, opening new possibilities in automation and AI-driven user experiences.
More: Implementing Anthropic Agent Design Patterns with Google ADK