AI applications are shifting from monolithic large language models to modular, multi-agent systems—a transformation that enhances performance, flexibility, and maintainability.
In my talk about AI Agents last September, I said AI agents would become increasingly popular. Today, we see this shift happening across industries. By breaking down complex tasks into specialised components, engineers can design smarter, more scalable AI workflows.
Analogy: Think of multi-agent systems like a well-coordinated orchestra. Each musician (agent) has a specific role, and together, they create a harmonious performance. In software, this means dividing complex problems into manageable, specialised parts that work in concert.
In this guide, we'll explore four key multi-agent patterns, using travel booking as an example. You'll learn how to choose the right pattern for your application, implementation strategies, and error-handling techniques to build robust multi-agent AI systems.
Agentic systems, where multiple AI agents collaborate through decision-making and handoffs, shine in specific scenarios but add operational complexity.
In this post, we'll explore the scenarios where agentic systems are most effective and the challenges you may face when using them.
The goal of creating something "predictable," reliable, and consistent is a shared principle across all the teams I've worked with throughout my career.
Knowing that the same code would always return the same output when given the same inputs was the foundation of everything we built.
We aimed for no surprises, no matter how complex a workflow might be.
Whether implicitly or explicitly using finite state machines, this determinism enabled us to build testable, monitorable, maintainable, and, most importantly, predictable workflows.
We read and shared ideas at conferences, promoting patterns and principles like SOLID and DRY to create functional, composable, and extensible software.
Having lived through the era of a "new JavaScript framework every week," we now find ourselves in the gold rush of the AI agent framework space.
New frameworks appear daily, each claiming to be the 'ultimate' solution for building AI agents, often backed by YouTubers enthusiastically promoting demoware and usually their own library, framework, or SaaS offering. Unfortunately, this enthusiasm can lead companies to uncritically adopt these tools without considering the long-term implications.
