Why Agent Frameworks Will Fail (and what to use instead)
Science & Technology
Introduction
In the rapidly evolving landscape of data and AI, various frameworks and workflows have emerged, particularly with the rise of large language models (LLMs). However, my stance is that many of these so-called agent frameworks are overly complex for most applications and may not provide the robustness needed for practical use cases.
The Current Landscape of Agent Frameworks
As a point of reference, notable agent frameworks such as Autogen, Crew AI, and LangChain have gained popularity in recent times. These frameworks largely center on the idea of chaining agents that can interact with one another to reason through workflows. For instance, in LangChain, agents use LLMs (language models) to determine the next actions in a process, with similar objectives seen in Crew AI, where agents are designed with specific roles and backstories, enabling multi-step reasoning.
While the creativity fostered by these frameworks is impressive, they often complicate simple processes. Many real-world business applications require well-defined automated workflows rather than creative and flexible responses. If a process isn’t clearly defined and requires excessive creativity, then it necessitates refinement before automation can be properly achieved.
Simplifying the Process
After years of building applications utilizing LLMs since the introduction of GPT-3.5, I've come to recognize a simpler approach. I liken the flow of a generative AI application to a data pipeline, akin to the established ETL (extract, transform, load) frameworks in traditional data processing.
This approach uses a sequential, directed acyclic graph (DAG) design, which enhances reliability and clarity. Each step has a predetermined order, allowing for more straightforward management and fewer uncertainties in processing. The focus should be on clearly mapping out a process that can be delineated visually, which ensures effective problem-solving.
A Practical Example: Ticket Response System
One practical project I’m developing involves automating email responses through a ticketing system – classifying incoming queries and generating appropriate responses. The application’s architecture is modular, allowing seamless addition, removal, or adjustment of steps.
Using design patterns like the Chain of Responsibility, I’ve structured the application such that each module processes data in a defined manner. The system first classifies an email, then generates a response, leveraging a library capable of validating the output. This sequential system provides confidence in the model's reasoning and outputs.
Advantages of a Data Pipeline Approach
This approach mitigates the risks of building atop complex abstractions from external frameworks you may not fully understand. Instead, a straightforward data pipeline can accommodate both simple and complex requirements without unnecessary overhead. By focusing on first principles—understanding the core needs of the application—you can build a more maintainable and reliable solution tailored to specific business processes.
In conclusion, while agent frameworks like LangChain or Autogen are intriguing and may serve unique circumstances, the complexity they introduce may not suit most automation tasks in business today. Embracing simplicity—through frameworks founded on proven data pipeline designs—will reward developers with clearer paths to building robust applications that effectively leverage LLMs.
If you're interested in exploring simplified approaches to building generative AI solutions, stay tuned for additional content and resources I'll be sharing.
