Why Agent Frameworks Will Fail (and what to use instead)

Summary

In this video, Dave Abar critiques the use of complex agent frameworks in AI applications, arguing they are often overcomplicated and not robust enough for most business automation needs. Instead, he advocates for a simpler, more reliable approach by viewing AI workflows as data pipelines. By applying established design patterns and building from first principles, developers can create more efficient and understandable solutions. Dave showcases an example of a generative AI app using sequential steps in Python, demonstrating how to handle incoming tasks from different sources and process them effectively without relying on bloated frameworks.

Takeaways

• 😀 Agent frameworks like LangChain, Autogen, and Crew AI are becoming popular for automating tasks, but they can be too complex and not robust enough for real-world business needs.
• 😀 Most agent frameworks focus on creativity and flexibility, which can lead to unpredictability and are unnecessary for automating well-defined business processes.
• 😀 The core issue with agent frameworks is that they are built on abstractions, which make it difficult to understand what's going on behind the scenes, leading to unnecessary complexity.
• 😀 For most business automation tasks, a simpler, more reliable approach using data pipelines is recommended, as they offer more control and predictability.
• 😀 Data pipelines follow a clear, sequential flow of input → processing → output, similar to ETL pipelines, ensuring a more structured and maintainable solution.
• 😀 By using data pipelines, developers can leverage proven design patterns like directed acyclic graphs (DAGs), ensuring reliability and avoiding circular dependencies.
• 😀 A simple, structured approach is often better than adopting opinionated frameworks built by others. Developers should focus on understanding their own problems and solutions.
• 😀 The Chain of Responsibility design pattern is a useful tool for creating flexible, sequential data pipelines, where each step in the process can be easily adjusted or expanded.
• 😀 Dave demonstrates how a real-world email classification and response system can be built using a data pipeline, highlighting the simplicity and scalability of the approach.
• 😀 Using design patterns like Chain of Responsibility and Registry allows developers to structure data pipelines in a way that supports growth and adaptability without adding unnecessary complexity.
• 😀 The goal is to build AI applications from first principles, using simple, scalable solutions, and avoiding bloated frameworks that may obscure understanding and control.

Q & A

What is the main critique Dave Abar has regarding agent frameworks like LangChain, Crew AI, and Autogen?

Dave critiques agent frameworks for being overly complex and not robust enough for most real-world business automation tasks. He believes they add unnecessary complexity and are built on abstractions that obscure their inner workings, making it difficult for developers to fully understand and control the process.

Why does Dave suggest using a data pipeline approach instead of agentic workflows?

Dave suggests using a data pipeline approach because it is simpler, more reliable, and easier to understand. He draws a parallel with traditional ETL (Extract, Transform, Load) pipelines, which have been around for years and follow well-established principles and patterns that are more suitable for automating business processes.

What is the difference between agentic workflows and data pipelines according to the video?

Agentic workflows are designed around chaining agents together in a non-sequential manner, allowing for creativity and reasoning. In contrast, data pipelines follow a sequential flow where data is processed step-by-step, which ensures clarity, reliability, and control over the workflow.

What design patterns does Dave recommend for building a simple and flexible automation system?

Dave recommends using design patterns such as the Chain of Responsibility and Registry patterns. These patterns allow for easy addition, removal, or modification of steps in the process, ensuring that the system remains modular, flexible, and maintainable.

What is the Chain of Responsibility pattern, and how is it applied in the example project?

The Chain of Responsibility pattern allows for the processing of tasks in a sequence, where each step handles part of the task and passes it along to the next step. In the example project, this pattern is used to process incoming emails and Instagram tickets, with each step handling classification and generating responses.

What is the role of the Registry pattern in the project example?

The Registry pattern is used to manage and organize different pipelines (e.g., email pipeline and Instagram pipeline) based on the source of the incoming data. It helps route the incoming requests to the appropriate pipeline by keeping track of the different processing steps required for each channel.

How does Dave's approach handle scalability in automation systems?

Dave's approach handles scalability by allowing for the easy addition of steps to the data pipeline. By using modular design patterns, new steps can be introduced without disrupting the overall flow, making the system scalable as new tasks or channels are added.

What is the advantage of using a sequential data pipeline over a circular agentic flow?

A sequential data pipeline ensures that each step is processed in order, with no looping or backtracking, which enhances reliability and predictability. This contrasts with circular agentic flows, where the process can loop back and forth between agents, making it harder to manage and debug.

What practical example does Dave provide to demonstrate his approach?

Dave provides the example of a ticketing system that processes incoming emails and Instagram messages. The system uses a sequential pipeline to classify the incoming messages and generate appropriate responses. This simple yet flexible approach illustrates how automation can be achieved without complex agent frameworks.

Why does Dave suggest breaking down business processes into simple, sequential steps?

Dave suggests breaking down business processes into simple, sequential steps because most business workflows can be clearly defined and automated in a step-by-step manner. If a process is too complex or doesn't fit into a simple flow, it may need to be simplified further or split into smaller, more manageable tasks.