From Compound AI Systems to AI Agents: our journey before the hype
Like many technologies, AI frameworks and terminology keep evolving. Tech professionals have been working with complex AI systems for a while when the challenge at hand required a more creative and advanced solution. Technologies like "Compound AI Systems" and "AI Agents" have been around and widely used before the terms became common. In this blog, we'll share our own journey: how our approach has grown from building integrated AI systems to working with AI agents.
From Compound AI Systems to AI Agents
Our work with Compound AI Systems began unintentionally through projects that required multi-step AI workflows. Early on, we faced challenges involving data ingestion and transformation processes that a single model couldn't handle efficiently. These scenarios led us to integrate multiple models, including large language models (LLMs), to analyze, interpret, and refine data—handling intricate tasks like text and image analysis. To manage these complex workflows, we turned to AWS Step Functions, which helped automate and streamline our processes and thus, effectively building what are now recognized as Compound AI Systems.
For example, when processing video content for Data Protection Institute, we designed a workflow that first transcribes the video's audio feed into text. Next, the transcribed text is segmented into manageable chunks. Finally, we used an LLM to augment and improve the textual content, enhancing readability and extracting key insights. This multi-step process showcases the power of Compound AI Systems in handling complex, layered tasks with efficiency.
Our approach to integrating multiple models and tools allowed us to solve complex problems flexibly and efficiently. It wasn't until much later that we realized our work perfectly aligned with the emerging concept of Compound AI. These systems enabled us to build applications that were not only robust but also adaptable to evolving data requirements, laying a critical foundation for what was to come.
However, the landscape of AI is rapidly evolving. Recent advancements in LLMs significantly enhanced their reasoning capabilities, making it possible for AI to move beyond predefined workflows. This shift paved the way for AI agents—systems capable of autonomous decision-making, planning, and adaptation. To harness this potential, we adopted LangGraph, an orchestration framework designed for building sophisticated agentic systems. LangGraph allowed us to create workflows where agents could interact, learn, and adapt in real time, pushing the boundaries of what our AI systems could achieve.
The transition from Compound AI Systems to AI agents introduced new opportunities and challenges. While Compound AI Systems excelled in handling multi-step processes, AI agents brought flexibility and autonomy, allowing for more dynamic responses to unforeseen challenges. This shift has not only improved our efficiency but also opened up new possibilities for innovative applications. For example, AI agents enabled us to develop systems that could dynamically adjust their strategies based on real-time feedback, resulting in smarter and more efficient outcomes. One such application that we’ve developed involved an agent responsible for augmenting and categorizing data. The agent autonomously determined whether to call external tools and the optimal order of those tool calls to achieve the best possible data augmentation, showcasing the agent's ability to adapt and optimize its workflow.
Lessons learned
Our experience with Compound AI Systems laid a strong foundation for our transition to AI agents. Mastering the integration of multiple models and tools taught us valuable lessons about orchestrating AI workflows, designing scalable systems, and ensuring robust performance. This expertise has proven invaluable as we build more dynamic, autonomous systems. We've learned that while automation can streamline processes, the real value lies in creating systems that can adapt and make decisions independently.
Transitioning to agent-based architectures, however, comes with its own set of challenges. Ensuring AI agents can operate reliably in unpredictable environments requires robust error handling and real-time adaptability. But with these challenges come opportunities—creating intelligent, responsive systems capable of handling tasks with minimal human intervention. We've found that combining the structured nature of Compound AI with the flexibility of AI agents leads to a powerful hybrid approach, balancing reliability with innovation.
What’s next?
Looking ahead, the future of AI orchestration lies in refining and expanding agent-based systems. Teams embarking on this journey should build on the principles of modular design and scalability learned from Compound AI Systems. Embracing frameworks like LangGraph and focusing on continuous learning and adaptation will be key to staying ahead in the evolving AI landscape. As AI technology continues to advance, we anticipate even greater opportunities to innovate, from enhancing business processes to creating entirely new applications that were previously unimaginable.
Conclusion
Even though AI technology is evolving quickly, our team is committed to keep up. Our journey from building Compound AI Systems to embracing AI agents reflects this ambition.
Early integration of Compound AI principles allowed us to build scalable, flexible systems, while our shift to AI agents has opened new ways for intelligent automation and adaptive reasoning.
AI will continue to transform industries an reshape the technological landscape. Let’s keep pushing the boundaries of what’s possible in AI innovation!
Curious how AI can transform your operations? Let’s get in touch!
