AI Agents were hard UNTIL I learned this

Introduction

Introduction

I've been diving into the complexities of AI agents and have identified three major trends that encompass what I call "Agentic Friction". These trends include dependency hell, never-ending integrations, and skill issues.

Agentic Friction

1. Dependency Hell: Working with multiple model providers, each with its own API, can be a nightmare. Open source libraries might introduce breaking changes, and startups or vendors could do a rug pull.
2. Never-Ending Integrations: Every new framework comes with a learning curve. This constant tweaking and hacking can be exhausting and make it hard for professionals to maintain a good work-life balance.
3. Skill Issues: The educational path for agentic engineering isn't well mapped out. While starting with agents is relatively straightforward, achieving full autonomy in production environments is still murky. The behavior of agents is inherently unpredictable, making it a challenge to develop reliable ones.

Introducing the AMU Pattern

To address these challenges, I've developed a pattern called AMU, which stands for Adapters, Metadata, and Use Cases. This pattern aims to simplify the development process and increase reliability.

Adapters

• Primary Adapters: Handle events, requests, and commands directed at agents.
• Secondary Adapters: Manage interactions with other AI runtimes, data stores, and vendor-specific technologies.

Metadata

Encapsulates schemas and ensures type safety, which helps mitigate the unreliability and hallucination issues associated with workflows.

Use Cases

Map out the actual business process flow. By breaking down the process into manageable steps, it becomes easier to adapt and evolve.

Real-World Application

I've applied the AMU pattern to a project where an AI agent repurposes YouTube content for different social media platforms. The architecture follows a modular monolith style:

• Adapters: Handle input and output interactions.
• Metadata: Defines the structure and type safety for data.
• Use Cases: Implement the logic for the agent's tasks.

This pattern makes the codebase more organized, easier to understand, and faster to adapt to changes.

Practical Example

In a specific implementation, a YouTube URL is submitted to a queue, picked up by an agent, processed through various steps, and finally posted on different platforms like Twitter and LinkedIn. The AMU pattern isolates different components, making it easy to tweak each part without affecting the entire system.

AWS Specific Implementation

In this setup, I use Amazon Bedrock for model invocations. The agent's workflows are segmented into reusable functions, which makes swapping out implementations (like switching from AWS to OpenAI) straightforward.

Conclusion

The AMU pattern is an effective way to manage the complexities of developing reliable AI agents. For those looking to dive deeper, I offer additional resources and hands-on help in my community platform.

Keywords

• AI Agents
• Adapters
• Metadata
• Use Cases
• AMU Pattern
• Dependency Hell
• Integration
• Skill Issues
• Reliability
• AWS
• Amazon Bedrock
• OpenAI

FAQ

Q: What is Agentic Friction? A: Agentic Friction refers to the collective challenges of dependency hell, never-ending integrations, and skill issues in AI agent development.

Q: What is the AMU pattern? A: AMU stands for Adapters, Metadata, and Use Cases. It's a design pattern aimed at simplifying AI agent development by modularizing components.

Q: How do adapters function in the AMU pattern? A: Primary adapters handle events and commands directed at the agent, while secondary adapters manage interactions with other runtimes, data stores, and vendor technologies.

Q: Why is metadata important in agent development? A: Metadata ensures type safety and helps mitigate the unreliability and hallucination issues that can arise in agent workflows.

Q: How does the AMU pattern improve codebase organization? A: By isolating different components (adapters, metadata, use cases), the pattern makes it easier to understand, adapt, and maintain the codebase.

Q: Where can I find more resources on implementing the AMU pattern? A: Additional resources and hands-on help are available in my community platform, where I offer guides, code snippets, and personalized support.