Building Effective AI Agents: Insights from Anthropic's Research

Two weeks ago, Anthropic released a relatively short but useful primer on how to build effective agents, delving into AI systems that use large language models (LLMs) to perform complex tasks. The piece distinguishes between simpler "workflows" and more complex "agents," emphasizing that simpler solutions should be tried first before moving to more advanced systems.

In this piece, we’ll take a look at the essay as a whole and distill the key takeaways for you—our beloved readers!

Let’s dive in.

What is Anthropic?

Anthropic is a leading AI research company founded by former OpenAI team members, and its LLM, Claude, is a powerful player in the same space as ChatGPT, Gemini, etc.

Part of what sets Anthropic apart from its rivals is its adherence to a "Constitutional AI" philosophy, which aims to create AI systems that are not only powerful but also safe, ethical, and transparent. The goal? An AI assistant that's less likely to produce harmful outputs and more easily steerable to achieve desired results.

In their paper, the authors stress three main principles for implementing agents:

1. Keep designs simple

2. Ensure transparency in the agent's processes

3. Carefully craft the interface between the agent and its tools.

Customer support and coding are highlighted as two particularly promising areas for AI agents.

Customer Support

• Support interactions naturally follow a conversation flow while requiring access to external information and actions;

• Tools can be integrated to pull customer data, order history, and knowledge base articles;

• Actions such as issuing refunds or updating tickets can be handled programmatically; and

• Success can be clearly measured through user-defined resolutions.

Coding agents

• Code solutions are verifiable through automated tests;

• Agents can iterate on solutions using test results as feedback;

• The problem space is well-defined and structured; and

• Output quality can be measured objectively.

Definition and Types of Agents

The essay distinguishes between two types of agentic systems:

1. Workflows: Systems where LLMs and tools follow predefined code paths.

2. Agents: Systems where LLMs dynamically direct their own processes and tool usage.

• Workflows are better for tasks needing consistency and predictability.

• Agents excel in scenarios requiring adaptability and model-driven choices.

• Often, a well-optimized single LLM call with proper context is sufficient.

Implementation Strategies

While acknowledging various frameworks, the essay suggests:

• Beginning with direct LLM API usage for simplicity.

• Understanding the underlying mechanisms when using frameworks.

• Avoiding unnecessary complexity in system design.

Key Patterns for AI Systems

The essay outlines several important structures:

1. Augmented LLM: The foundational unit with added capabilities like retrieval and memory.

2. Prompt Chaining: Breaking tasks into sequential steps for better accuracy.

3. Routing: Classifying inputs to direct them to specialized follow-up tasks.

4. Parallelization:

   • Sectioning: Dividing tasks into independent subtasks.

   • Voting: Running identical tasks multiple times for diverse outputs.

5. Orchestrator-Workers: A central LLM coordinating tasks among worker LLMs.

6. Evaluator-Optimizer: One LLM generates responses while another provides feedback.

7. Agents: More independent systems that plan and act based on environmental feedback.

In Conclusion

“Building Effective Agents" offers valuable perspectives for those involved in AI development or exploration. By focusing on simplicity, intentional design, and selecting the appropriate level of complexity, it lays out practical advice for building capable AI systems.

These principles are especially relevant as we continue to unlock and discover the potential of large language models, helping ensure AI remains both effective and accessible.

Random Fun Fact of the Week

Did you know NVIDIA, the most valulable compnay in the world, was launched in…a Denny’s?

That’s right.

The story goes that NVIDIA was indeed conceived at a Denny’s restaurant.

In 1993, the company's co-founders—Jensen Huang, Chris Malachowsky, and Curtis Priem—met at a Denny’s to discuss their vision for creating a company that would “revolutionize graphics processing technology.”

The informal and humble setting of a Denny’s contrasts with the monumental success NVIDIA has achieved.

Contact us at NorthLightAI.com to learn how we can help you build a stronger data foundation for your AI future.