We’re seeing AI evolve fast. It’s no longer just about building a single, super-smart model. The real power, and the exciting frontier, lies in getting multiple specialized AI agents to work together. Think of them as a team of expert colleagues, each with their own skills — one analyzes data, another interacts with customers, a third manages logistics, and so on. Getting this team to collaborate seamlessly, as envisioned by various industry discussions and enabled by modern platforms, is where the magic happens.

But let’s be real: Coordinating a bunch of independent, sometimes quirky, AI agents is hard. It’s not just building cool individual agents; it’s the messy middle bit — the orchestration — that can make or break the system. When you have agents that are relying on each other, acting asynchronously and potentially failing independently, you’re not just building software; you’re conducting a complex orchestra. This is where solid architectural blueprints come in. We need patterns designed for reliability and scale right from the start.

Why is orchestrating multi-agent systems such a challenge? Well, for starters:

Simply put, the combinatorial complexity explodes as you add more agents and interactions. Without a solid plan, debugging becomes a nightmare, and the system feels fragile.

How you decide agents coordinate their work is perhaps the most fundamental architectural choice. Here are a few frameworks:

Many real-world multi-agent systems (MAS) end up being a hybrid — perhaps a high-level orchestrator sets the stage; then groups of agents within that structure coordinate decentrally.

For agents to collaborate effectively, they often need a shared view of the world, or at least the parts relevant to their task. This could be the current status of a customer order, a shared knowledge base of product information or the collective progress towards a goal. Keeping this “collective brain” consistent and accessible across distributed agents is tough.

Architectural patterns we lean on:

The right choice depends on how critical up-to-the-second consistency is, versus how much performance you need.

It’s not if an agent fails, it’s when. Your architecture needs to anticipate this.

Think about:

Even with individual agent reliability, you need confidence that the entire collaborative task finishes correctly.

Consider:

The best architecture needs the right foundation.

The way agents talk impacts everything from performance to how tightly coupled they are.

• Agents post messages to topics; other agents subscribe to topics they care about. This is asynchronous, highly scalable and completely decouples senders from receivers.
• Agents call functions directly on other agents. This is fast, but creates very tight coupling — agent need to know exactly who they’re calling and where they are.

Choose the protocol that fits the interaction pattern. Is it a direct request? A broadcast event? A stream of data?

Building reliable, scalable multi-agent systems isn’t about finding a magic bullet; it’s about making smart architectural choices based on your specific needs. Will you lean more hierarchical for control or federated for resilience? How will you manage that crucial shared state? What’s your plan for when (not if) an agent goes down? What infrastructure pieces are non-negotiable?

It’s complex, yes, but by focusing on these architectural blueprints — orchestrating interactions, managing shared knowledge, planning for failure, ensuring consistency and building on a solid infrastructure foundation — you can tame the complexity and build the robust, intelligent systems that will drive the next wave of enterprise AI.

Nikhil Gupta is the AI product management leader/staff product manager at Atlassian.