What Are Compound AI Systems? Moving Beyond the Monolithic AI Model

In our fast-evolving AI era, we’re already moving beyond monolithic AI models in favor of a new approach: compound AI systems. This approach not only incrementally improves AI capabilities, but also changes how we develop and deploy AI solutions across various domains. Developers are increasingly building on the initial excitement of large language models (LLMs) by using compound AI systems to push the boundaries of what’s possible and drive business benefits to their organizations.

What are compound AI systems?

Compound AI systems are sophisticated architectures that tackle AI tasks using multiple interacting components. Unlike traditional AI models that operate in isolation, these systems apply a combination of AI models, retrievers, and external tools to solve complex problems more effectively. These systems draw upon the strengths of different components to achieve superior results.

Imagine an orchestra where each instrument represents a different AI capability. In a compound system, these instruments work in harmony, each contributing its particular strengths to create a symphony of intelligence far greater than any single component could achieve alone.

What are the key advantages of compound AI systems?

Several compelling advantages are driving the shift toward compound AI systems. These advantages allow them to consistently outperform their monolithic counterparts.

Improved performance — By combining multiple AI models and tools, developers can achieve superior results without solely relying on scaling up individual models. Compound AI systems show a performance improvement over monolithic systems.

Adaptability through dynamic data incorporation — Unlike static models, compound AI systems can integrate real-time data, making them much more adaptable to changing environments.

Enhanced control and trust — With multiple components, developers have more granular control over the system’s behavior, improving overall control. This allows for more fine-tuned outputs and better error handling, potentially increasing transparency and trustworthiness.

Flexibility in performance and cost tradeoffs — Compound AI systems allow for fine-tuning of resource allocation, optimizing for performance or cost as needed. This results in a significant flexibility advantage, reflecting their ability to be customized for various use cases and requirements.

It’s clear why many developers are turning to compound AI systems. While monolithic systems still have their place, the versatility and enhanced capabilities of compound systems make them an attractive option for tackling complex AI challenges.

How do compound AI systems differ from traditional AI models?

Traditional AI models operate in isolation, focusing on a single type of task, while compound AI systems integrate multiple AI components, each contributing its strengths.

Superior adaptability is one of compound AI systems’ key advantages. Here’s how the adaptability life cycle of each approach compares:

The key difference lies in the granularity of updates and the system’s ability to evolve continuously. Compound AI systems can adapt more quickly and efficiently to new data, changing requirements, or performance issues. For instance, in a compound AI system for content moderation, if new types of harmful content emerge, only the content classification component might need an update. In contrast, a monolithic model would require retraining the entire system, which is more time-consuming and resource-intensive.

This flexibility in adaptation is one of the primary reasons why compound AI systems are becoming increasingly popular in enterprise environments, where agility and rapid response to changing conditions are essential.

How do compound AI systems enhance security?

An often overlooked but crucial advantage of compound AI systems is their potential to significantly enhance security and reliability. This benefit stems from the inherent diversity and distributed nature of these systems, creating a form of self-policing through checks and balances—an AI democracy of sorts.

In a compound AI system, multiple AI components work together, each potentially producing its own output. This diversity acts as a natural safeguard against errors or malicious interventions in the following ways:

Consensus-based decision making — When multiple AI components contribute to a decision, an outlier result from one component, whether due to an error or a security breach, is likely to be overruled by the consensus.

Anomaly detection — Component diversity makes it easier to spot a result that significantly deviates from its peers so the component can be identified and isolated for investigation.

Reduced single points of failure — In monolithic systems, a failure in the core model could jeopardize the entire system; in a compound system, a breach in one component doesn’t necessarily compromise the whole.

Compound AI systems can also be more resilient against adversarial attacks through diverse security measures designed for different components, dynamic security that routes requests through different components or changes its configuration, and breach containment if one component is compromised.

How do compound AI systems address ethical concerns and biases?

The diversity within compound AI systems can also help address ethical concerns and mitigate biases. By incorporating diverse AI models, potentially trained on different datasets or using different methodologies, the system can provide a more balanced and nuanced output. Additionally, output discrepancies between different components can help identify potential biases, allowing for quicker detection and correction. Specific components can also be dedicated to ethical considerations, acting like an AI ethical committee that reviews other components’ outputs.

This type of feedback among diverse components creates opportunities for ongoing improvement, including self-evaluation, leading to continuous internal quality control, and adaptive learning, wherein the system dynamically adjusts its reliance on various components to optimize overall performance and security.

Compound AI systems therefore not only enhance performance and adaptability, but also create a more secure, reliable, and ethically sound AI ecosystem. This represents a significant step toward addressing the security and ethical concerns surrounding AI deployment in critical applications.

What are the challenges in developing compound AI systems—and what emerging solutions exist?

While the potential of compound AI systems is immense, their development comes with challenges, including¹:

Vast design space — With countless possible combinations of components, finding the optimal system architecture can be daunting.

Optimization complexity — Co-optimizing multiple components, some of which may be non-differentiable, requires new approaches that go beyond traditional machine learning techniques.

Operational hurdles — Monitoring and debugging these complex systems demand advanced tools and practices, especially when dealing with data quality and security concerns in multi-component architectures.

Resource allocation — Balancing resources between components is crucial for performance.

To address these challenges, several innovative approaches are emerging:

Composition frameworks — Tools like LangChain and LlamaIndex are simplifying the process of building applications with multiple AI components.

Automatic optimization — Frameworks like DSPy replace complex prompting techniques with straightforward, effective modules and optimizers designed to maximize the efficiency and effectiveness of language model programs.

Cost optimization — Solutions like FrugalGPT help manage costs and maximize quality within budget constraints by intelligently routing queries to different models.

Operational tools — New LLMOps and DataOps tools are evolving for monitoring, debugging, and evaluating complex AI systems, focusing on tracking intermediate outputs and correlating them with data pipeline quality.

What does the future of AI development look like with compound AI systems?

The rise of compound AI systems marks a new chapter in artificial intelligence. Examples of these groundbreaking systems already exist. They include AlphaCode2, which solves programming challenges and achieved results matching the 85th percentile of human competitors; AlphaGeometry, which tackles complex geometry problems on par with International Mathematical Olympiad medalists; Medprompt, a medical AI system that outperforms specialized medical models on various tasks; Google’s Gemini on MMLU; and ChatGPT Plus, which uses various plug-ins and tools for diverse tasks. These early examples showcase how compound systems push the boundaries of AI capabilities across industries.

By combining the strengths of various AI models and tools, these systems unlock levels of performance and flexibility previously unattainable with single models. This approach is rewriting the rules of what’s possible in AI development. Compound AI systems, through their multi-component architecture, offer improved performance, dynamic data incorporation, enhanced control, and flexibility in performance and cost tradeoffs.

Organizations can better position themselves to reap these benefits by partnering with AI experts who understand this exciting new approach and can help overcome the challenges inherent in developing compound AI systems— such as navigating the vast design space, optimizing multiple components, and managing operational hurdles.

The future of AI isn’t just about bigger models—it’s about smarter, more integrated systems. Compound AI systems represent a significant step forward, offering enhanced performance, adaptability, and security. The orchestra of AI is tuning up, and its forthcoming symphony promises to be nothing short of extraordinary.