From Compound AI Systems to AI Agents: our Journey Before the hype
As with many emerging technologies, AI frameworks and terminology are constantly evolving. For quite some time, tech professionals have been developing complex AI solutions, especially when tackling challenges that demand more creative and advanced approaches. Concepts such as “Compound AI Systems” and “AI Agents” have been in use long before these terms became mainstream.
In this blog, we’ll share our own journey: how we progressed from building integrated AI systems to embracing AI agents, illustrating the evolution of our approach along the way.
From Compound AI Systems to AI Agents
Our journey with Compound AI Systems started somewhat unintentionally, through projects that demanded multi-step AI workflows. Early on, we encountered challenges in data ingestion and transformation that a single model couldn’t efficiently address. This led us to integrate multiple AI models, including large language models (LLMs), to analyze, interpret, and refine data, handling complex tasks like text and image analysis. To manage these intricate workflows, we employed AWS Step Functions, which automated and streamlined processes, effectively creating what are now known as Compound AI Systems.
For instance, in a project for the Data Protection Institute, we developed a workflow to process video content by first transcribing the audio into text, then segmenting that text into manageable chunks. Finally, an LLM was used to enhance the textual content, improving readability and extracting key insights. This multi-step process illustrates the strength of Compound AI Systems in efficiently managing complex, layered tasks.
Our approach of combining multiple models and tools gave us the flexibility and efficiency needed to solve challenging problems. It was only later that we realized this approach aligned perfectly with the emerging concept of Compound AI. These systems allowed us to build applications that were robust and adaptable to evolving data needs, laying a solid foundation for what was to come.
However, the AI landscape is rapidly evolving. Recent advances in LLMs have significantly improved their reasoning capabilities, enabling AI to move beyond rigid, predefined workflows. This evolution opened the door to AI agents, autonomous systems capable of decision-making, planning, and adapting dynamically. To leverage this potential, we adopted LangGraph, an orchestration framework designed for building sophisticated, agentic AI systems. LangGraph empowered us to create workflows where agents interact, learn, and adapt in real time, expanding the horizons of what our AI could achieve.
The shift from Compound AI Systems to AI agents brought new opportunities and challenges. While Compound AI Systems excelled at structured, multi-step tasks, AI agents introduced autonomy and flexibility, enabling more dynamic responses to unforeseen situations. This transition not only boosted our operational efficiency but also unlocked innovative possibilities. For example, we developed an agent tasked with augmenting and categorizing data autonomously. This agent decided independently when and in what order to call external tools to maximize data enrichment, demonstrating its capacity to adapt and optimize workflows on the fly.
Lessons Learned
Our work with Compound AI Systems provided a solid foundation for moving toward AI agents. Through mastering the integration of multiple models and tools, we gained crucial insights into orchestrating AI workflows, designing scalable architectures, and maintaining robust performance. This expertise has been essential as we develop more dynamic and autonomous systems. While automation helps streamline processes, we’ve learned that true value emerges from building systems capable of independent adaptation and decision-making.
That said, transitioning to agent-based architectures introduces new challenges. Ensuring AI agents perform reliably in unpredictable environments demands strong error handling and real-time adaptability. Yet, these challenges bring exciting opportunities, enabling the creation of intelligent, responsive systems that can manage complex tasks with minimal human oversight. We’ve discovered that combining the structured reliability of Compound AI Systems with the flexibility and autonomy of AI agents creates a powerful hybrid approach, effectively balancing stability with innovation.
What’s Next?
Looking forward, the future of AI orchestration will focus on refining and expanding agent-based systems. Teams venturing into this space should leverage the modular design principles and scalability lessons gained from working with Compound AI Systems. Adopting frameworks like LangGraph and prioritizing continuous learning and adaptation will be essential for staying competitive in the rapidly evolving AI landscape. As AI technology advances, we foresee vast opportunities for innovation ranging from optimizing existing business processes to developing entirely new applications that were once beyond reach.
Conclusion
While AI technology continues to evolve rapidly, our team remains dedicated to staying at the forefront of innovation. Our journey, from developing Compound AI Systems to embracing AI agents, illustrates our commitment to advancing intelligent automation.
By integrating Compound AI principles early on, we built scalable and flexible systems. Transitioning to AI agents has since unlocked new possibilities for adaptive reasoning and autonomous decision-making.
AI will keep transforming industries and reshaping the technological landscape. Together, let’s push the boundaries of what’s possible in AI innovation!
Curious about how AI can revolutionize your operations? Get in touch! We’d love to explore the possibilities with you.
