Charting our AI evolution: From Complex Workflows to Intelligent Agents

Like many technologies, AI frameworks and terminology continue to evolve. Tech professionals have long been working with complex AI systems, well before terms like “Compound AI Systems” and “AI Agents” became common. Often, when the challenge demanded it, we adopted the creative and sophisticated solutions that fall under these terms, even if the terminology wasn’t formalized yet.

In this post, we’re sharing our journey: how our approach progressed from building integrated, multi-model AI workflows to designing intelligent, adaptive agents.

Building compound AI systems (before we knew that’s what they were)

At the time, we were tackling challenges that often required AI solutions going beyond what a single model could handle. We found ourselves designing multi-step pipelines to ingest, transform, and analyse data, sometimes across modalities like text, audio, and images. Rather than relying on a one-size-fits-all model, we began integrating multiple AI components, including large language models (LLMs), to tackle specific parts of the process.

To orchestrate these intricate workflows, we leveraged AWS Step Functions, which allowed us to create modular, automated pipelines. Unbeknownst to us, we were effectively building what would later be recognized as Compound AI Systems, a combination of tools and models structured to work in sequence or parallel, each handling a distinct task within a broader operation.

One such project involved processing video content for a customer. The pipeline began with audio transcription, followed by segmenting the output into digestible parts, and concluded with using an LLM to enhance clarity and extract meaningful insights. This type of layered workflow demonstrated how powerful and efficient these compound systems could be in managing complexity.

The shift toward AI agents

With recent leaps in LLM capabilities, particularly in reasoning and planning, we began to see the limits of rigid, predefined workflows. It became possible to offload not just execution but also decision-making to AI systems.

That’s where AI Agents came in. These systems don’t just follow steps, they decide which steps to take based on context, goals, and feedback. To support this shift, we began working with LangGraph, a powerful framework for orchestrating agentic AI systems. LangGraph made it possible to construct dynamic, evolving workflows where agents could reason, adapt, and collaborate.

One of our recent agent-based solutions involved a system that autonomously enhanced and categorized data. This agent intelligently determined whether and when to call external tools, optimizing its own flow in response to the task at hand. This wasn’t just automation, it was adaptation.

What we’ve learned along the way

Our foundation in building Compound AI Systems gave us the modular thinking, orchestration experience, and reliability mindset we needed to evolve into agent-based architectures. These early projects taught us how to coordinate multiple models and services into cohesive systems, an essential skill when working with autonomous agents.

But agentic systems bring their own challenges. They must be resilient in unpredictable environments and capable of responding in real time. Error handling, feedback loops, and intelligent branching become much more important when the system is “thinking” for itself.

We’ve discovered that the sweet spot often lies in a hybrid approach, pairing the structured robustness of Compound AI Systems with the flexibility and intelligence of AI agents. This balance allows us to innovate without sacrificing stability.

What comes next

Looking ahead, we believe the future of AI orchestration lies firmly in agent-driven systems. Teams looking to innovate should lean on what they’ve learned from building compound architectures: modularity, scalability, and thoughtful design.

Frameworks like LangGraph will play a key role, as will a continued focus on systems that learn and adapt. We’re particularly excited about opportunities to push into use cases that require autonomous decision-making, from business process optimization to AI-native applications that previously seemed unimaginable.

Final thoughts

The field of AI is evolving faster than ever, and we’re committed to evolving with it. Our progression from Compound AI Systems to AI Agents reflects our pursuit of smarter, more adaptive technology.

By combining the reliability of structured pipelines with the autonomy of intelligent agents, we’re developing solutions that are not just automated, but genuinely intelligent.

Wondering how AI could transform your workflows or product? Let’s connect, we’d love to explore the possibilities with you.