Anthropic Architecture Design and Practice Guide: Secrets to Building Efficient AI Agents

Anthropic’s AI Agent design philosophy emphasizes simplicity and practicality, with core points including:

• Simple First: Avoid over-engineering and choose solutions that fit the needs.

• Composable Patterns: Build Agents using simple, composable patterns instead of relying on complex frameworks.

• Flexibility and Efficiency: Choose workflows or autonomous Agents based on task requirements.

• Workflow: Suitable for scenarios with clear tasks and fixed steps, providing predictability and consistency.

• Autonomous Agent: Suitable for scenarios requiring flexibility and model-driven decision-making, ideal for complex and open-ended problems.

• Optimize Single LLM Call: For many applications, optimizing a single LLM call (such as retrieval and context examples) is often sufficient.

Basic Building Module: Enhanced LLM

• Retrieval, Tools, and Memory: Enhance the capabilities of LLMs through retrieval, tools, and memory.

• Model Context Protocol: Integrate with third-party tools through a simple client implementation.

Common Workflow Patterns

1. Prompt Chaining:

   Break down tasks into multiple steps, with each LLM call processing the output of the previous step.

   Applicable Scenarios: Marketing copy generation, document outline writing, etc.

2. Routing:

   Classify inputs and direct them to specialized tasks.

   Applicable Scenarios: Customer service query classification, routing questions to different models.

3. Parallelization:

   Break tasks into independent sub-tasks for parallel execution or obtain diverse outputs through multiple calls.

   Applicable Scenarios: Content review, code vulnerability checks, etc.

4. Orchestrator-Workers:

   A central LLM dynamically decomposes tasks and assigns them to worker LLMs.

   Applicable Scenarios: Multi-file code modifications, complex search tasks.

5. Evaluator-Optimizer:

One LLM generates responses while another provides evaluation and feedback, forming an iterative optimization loop.

Applicable Scenarios: Literary translation, complex search tasks.

• Characteristics: Autonomously plan and execute tasks, use tools, and adjust based on environmental feedback.

• Applicable Scenarios: Open-ended questions, complex tasks (such as code modification, computer operations).

• Considerations: High costs, potential error accumulation, and need for extensive testing in a sandbox environment.

• Keep It Simple: Avoid unnecessary complexity.

• Transparency: Clearly display the planning steps of the Agent.

• Tool Documentation and Testing: Carefully design tool interfaces to ensure usability and reliability.

• Customer Support: Combine chat interfaces with tool integration to handle customer queries and operations.

• Code Agent: Solve GitHub issues through automated testing and iterative optimization.

• Tool Format Selection: Choose formats that are easy for LLMs to understand, avoiding complex formatting.

• Tool Documentation: Provide example usage, boundary conditions, and input format requirements.

• Testing and Iteration: Test tool usage through a large number of examples to optimize tool definitions.