Why Agent Frameworks Will Fail (and what to use instead)
TLDRIn this video, Dave Abar, founder of Data Lumina, critiques agent frameworks for AI solutions, suggesting they are overly complex and not robust enough for most business automation needs. He advocates for a simpler approach, using data pipelines and design patterns like the chain of responsibility, which are more reliable and easier to understand. Abar demonstrates building a generative AI app with a clear input-processing-output flow, using Python as an example, and recommends avoiding complex frameworks in favor of first principles.
Takeaways
- 🧐 Dave Abar, founder of Data Lumina, discusses his views on agent frameworks and their potential shortcomings.
- 📈 Agentic workflows and frameworks have gained popularity with the rise of large language models.
- 🔗 Frameworks like Autogen, Crew AI, and Lang Chain are built around chaining agents for reasoning and workflow management.
- 🤖 These tools often involve complex agent interactions with goals, backstories, and tasks, which may exceed the needs of many real-world automation processes.
- 🛠 Dave suggests that most business processes require clear, defined steps rather than the creativity that agent frameworks often introduce.
- 🔄 He recommends a simpler approach, using data pipelines and first principles to build AI applications, avoiding unnecessary complexity.
- 🔧 Data pipelines follow a structured input-processing-output model, which is more aligned with traditional ETL processes and less prone to errors.
- 🔗 The chain of responsibility pattern can be effectively used to create a sequence of processing steps in AI applications.
- 🔧 Dave demonstrates a Python project template for a generative AI system that uses a data pipeline approach to process emails and generate responses.
- 🔄 The use of the Instructor Library is highlighted for validating outputs and reducing 'hallucinations' in AI-generated content.
- 🔗 A sequential, directed acyclic graph (DAG) approach is advocated for building reliable AI systems, ensuring data flows in one direction without loops.
Q & A
What is the main argument of Dave Abar in the video titled 'Why Agent Frameworks Will Fail'?
-Dave Abar argues that agent frameworks are likely to fail because they are too complex for most use cases and not robust enough. He suggests that for most business automation needs, simpler solutions based on data pipelines and first principles are more appropriate.
Who is Dave Abar and what is his background?
-Dave Abar is the founder of Data Lumina, where he has been building custom data and AI solutions for the past 5 years. He also creates educational content to help others learn to do the same and start freelancing.
What is the core concept behind agent frameworks according to the video?
-The core concept behind agent frameworks is to use language models as reasoning engines to determine a sequence of actions within a workflow, often involving chaining agents together that have specific goals, backstories, and tasks.
What are some examples of agent frameworks mentioned in the video?
-Some examples of agent frameworks mentioned in the video are Autogen, Crew AI, and Lang Chain, which all provide ways to build agents with different levels of complexity and features.
Why does Dave Abar believe that most real-world processes do not require the creativity that agent frameworks offer?
-Dave Abar believes that most real-world processes for business automation are clearly defined and require a straightforward sequence of steps rather than the creative problem-solving that agent frameworks are designed for.
What alternative approach does Dave Abar recommend instead of using agent frameworks?
-Dave Abar recommends building applications from the ground up using a data pipeline approach, which is simpler, more structured, and follows a directed acyclic graph (DAG) design principle to ensure reliability.
How does Dave Abar describe the typical flow of applications built with large language models?
-Dave Abar describes the typical flow of applications built with large language models as having inputs, a processing layer that may involve one or multiple LLM calls or external API calls, and an output which is typically generated by the model.
What is the 'chain of responsibility pattern' mentioned in the video and how is it used?
-The 'chain of responsibility pattern' is a design pattern where a sequence of objects is responsible for fulfilling a request. In the context of the video, it is used to define sequential steps in a data pipeline, allowing for easy addition, removal, or modification of steps.
How does Dave Abar's approach to building AI applications differ from using agent frameworks?
-Dave Abar's approach focuses on simplicity and clarity by building applications as data pipelines with sequential steps, avoiding the complexity and potential for 'hallucinations' that can come with agent frameworks. He emphasizes understanding the underlying processes and using proven design patterns.
What is the 'instructor Library' mentioned in the video and how does it relate to building AI applications?
-The 'instructor Library' is a tool used to patch large language models and validate outputs by defining a response model. It is mentioned as a powerful way to change how applications are built around large language models by adding validation and reasoning capabilities.
Outlines
🤖 Agent Frameworks Critique and Alternatives
Dave Abar, founder of Data Lumina, introduces his skepticism towards agent frameworks for AI solutions. He believes these frameworks, which include tools like Autogen, Crew AI, and Langchain, are overly complex and not robust enough for most practical applications. Abar argues that these tools are based on chaining agents together for workflow automation, but real-world business processes often require clear definitions and straightforward automation steps. He suggests that using large language models (LLMs) directly for specific steps in a workflow is more effective than the agentic approach, which can lead to unpredictable outcomes due to its creative nature.
🧠 Simplifying AI Application Development
Abar recommends a simpler approach to building AI applications, focusing on first principles and clear, defined processes. He advises against relying on complex, abstracted frameworks that are still being developed and refined by the community. Instead, he suggests viewing AI applications as data pipelines, which have well-established principles and patterns. This approach involves a linear, directed acyclic graph (DAG) workflow, ensuring data flows in one direction without loops, which enhances system reliability. Abar emphasizes the importance of understanding the underlying processes and avoiding over-complication.
🔌 Practical Implementation of Data Pipelines
Dave Abar demonstrates the practical application of data pipelines in AI projects, using a Python project template as an example. The template is designed to handle incoming data, process it through defined steps, and output results, without the need for complex frameworks. He explains the use of design patterns like the chain of responsibility pattern to create a flexible, modular system where steps can be easily added or removed. Abar shows how to set up a pipeline for processing emails, using the Instruct library to classify the email and generate a response, while also incorporating reasoning and confidence scores to enhance output quality.
🔗 Conclusion and Freelancing Resources
In the final paragraph, Abar concludes by reiterating the benefits of a simple, data pipeline approach to AI application development. He also provides a resource for developers interested in freelancing, offering a video on how his company can help find clients. Abar encourages viewers to like the video and subscribe for more content, and he suggests watching a follow-up video for a deeper dive into building reliable systems with large language models using the Instruct library.
Mindmap
Keywords
💡Agent Frameworks
💡Large Language Models (LLMs)
💡Chaining Agents
💡Backstories, Roles, Goals
💡Data Pipeline
💡Directed Acyclic Graph (DAG)
💡Chain of Responsibility Pattern
💡Penicillin Models
💡Generative AI
💡Orchestration
Highlights
Agent Frameworks are becoming popular with the rise of large language models.
Frameworks like Autogen, Crew AI, and Lang Chain allow building agents for workflows.
Most agent frameworks are too complex and not robust enough for many tasks.
Agents are typically chained together to reason and determine the next step in a workflow.
Crew AI allows designing agents with backstories, roles, goals, and tasks.
Real-world business processes often don't require creativity but defined steps.
Large language models are useful for solving specific steps in a process chain.
Generative AI applications usually follow an input-processing-output flow.
Agentic frameworks introduce a manager or orchestrator between agents.
Agentic workflows can involve complex interactions between multiple agents.
The speaker recommends avoiding complex frameworks for most business automation.
Simple, grounded approaches based on first principles are often more effective.
Data pipelines are a more established method for building applications.
Directed Acyclic Graph (DAG) is a recommended approach for pipeline design.
Viewing AI problems as data pipelines simplifies the coding process.
The chain of responsibility pattern is useful for creating sequential processing steps.
The video provides an example of building a system to classify and reply to emails.
The use of the instructor Library is highlighted for validating model outputs.
The video concludes with a pitch for the speaker's company's services for freelancers.
The speaker suggests that understanding the problem and building from the ground up is key.
