Beyond single-model AI: How architectural design drives reliable multi-agent orchestration

AI is evolving rapidly. It’s not just about building a single super smart model. The real power and exciting frontier lies in bringing together multiple professional AI agents. Think of them as a team of professional colleagues. Each has its own unique skills. One analyses the data, the other interacts with customers, and the third is managing logistics. To collaborate seamlessly with this team, as envisaged by discussions from various industries and made possible by modern platforms, is where magic happens.

But let’s be real: coordinate a bunch of independent, sometimes quirky AI agents difficult. It’s not just about building cool individual agents. It is a messy intermediate bit – orchestration – that you can create or break a system. If you have agents that depend on each other and potentially fail asynchronously, independently, then you don’t just build software. You’re doing complicated orchestras. This is where solid architectural blueprints appear. You need patterns designed for reliability and scale from the start.

Knot problems in agent collaboration

Why is the orchestra’s multi-agent system becoming such a challenge? Well, first of all:

1. They are independent: Unlike functions called programmatically, agents often have their own internal loops, goals, and states. They don’t just wait patiently for instructions.
2. Communication becomes complicated: Agent A is not just talking to Agent B. Agent A may be bothered by information agents C and D, but Agent B is waiting for a signal from E before transmitting f.
3. They must have a shared brain (state): How do they agree with the “truth” of what is going on? If Agent A updates a record, how does Agent B know about it? surely and Quick? Old or competing information is a murderer.
4. Failure is inevitable: Agent crashes. The message is lost. The external service calls a timeout. When part of the system falls, you don’t want everything to stop or worse yet, to do the wrong thing.
5. Consistency can be difficult: How do you ensure that a complex, multi-step process involving multiple agents will actually reach an effective final state? This is not easy if the operations are distributed and asynchronous.

Simply put, adding agents and interactions explodes with the complexity of the combination. Without solid planning, debugging can become a nightmare and the system feels fragile.

Select an orchestration playbook

The way agents decide to coordinate their work is perhaps the most basic architectural choice. Here are some frameworks:

• Conductor (Level): This is like a traditional symphony orchestra. There is a main orchestrator (conductor) who directs the flow, telling a particular agent (musician) that he is going to perform his work and bring it all together.
  • This allows you to: Clear workflow, easy to track and run, easy control. Smaller or fewer dynamic systems are easier.
  • Take care: Conductors can be bottlenecks or single point of failure. This scenario is less flexible when agents need to react dynamically or work without constant monitoring.
• Jazz Ensemble (Federation/Decentralization): Here, agents coordinate more directly, just like improvising musicians based on shared signals and rules, based on cues and common themes with each other. There may be shared resources or event streams, but there is no central boss to micromanute all the notes.
  • This allows for resilience (if one musician stops, other musicians can often continue), scalability, adaptability to changing conditions, and more urgent action.
  • Things to consider: Understanding the overall flow can be difficult. Debugging is tricky (“Why did that agent do that?” after that? ”) And carefully designed to ensure global consistency.

Many real-world multi-agent systems (MAS) are hybrid. Perhaps a high-level orchestrator will set the stage. The groups of agents within that structure are then settled and adjusted.

Management of AI agents’ collective brain (shared state)

Agents often need a shared view of the world, or at least a part related to their tasks, to collaborate effectively. This could be the current status of a customer order, the shared knowledge base of product information, or collective progress towards a goal. It is difficult to make this “collective brain” consistent and accessible across distributed agents.

Architectural patterns we are leaning:

• Central Library (Intensive Knowledge Base): A single authoritative place where all shared information resides (such as a database or a dedicated knowledge service). The agent checks out (reads) the books and returns them (writes).
  • Pro: A single source of truth, easy to enforce consistency.
  • CON: It could be hammered on a request, slowing things down, or chokepoint. It must be seriously robust and scalable.
• Distributed Notes (Distributed Cache): Agents maintain local copies of frequently needed information at a speed backed by the Central Library.
  • Pro: Faster reading.
  • CON: How do you know if your copy is up to date? Cache invalidation and consistency become a critical architectural puzzle.
• Scream for updates (pass message): Instead of constantly asking the library (or other agents), the library (or other agents) screams, “Hey, this information has changed!” via message. Agents are concerned about updating their notes and listen to updates they update.
  • Pro: Agents are isolated. This is suitable for event-driven patterns.
  • CON: Make sure everyone receives the message and handles it correctly. Add complexity. What happens if the message is lost?

The right choice depends on how important modern consistency is and how much performance is needed.

Building for when things go wrong (error handling and recovery)

It’s not the case that the agent fails. Your architecture should predict this.

Think about it:

• Watchdog (Director): This means that you have a component that is simply a job to watch other agents. If an agent gets quiet or starts something strange, the watchdog can restart it or alert the system.
• Try again, but be smarter (retry and discernment): If an agent’s action fails, it often requires retrying. However, this only works if the action is iDempotent. This means that doing it five times will produce exactly the same results as doing it once (such as setting a value). If the action is not equal, RETRIRES can cause chaos.
• Confusion Cleanup (Comment): If Agent A does something fine, but Agent B (the step after the process) fails, you may need to “undo” Agent A’s work. Saga-like patterns help you adjust these multi-step compensated workflows.
• Know where you are (workflow state): It helps to maintain a persistent log of the entire process. If the system goes down midway through the workflow, you can pick it up from the last known good step rather than starting over.
• Building a Firewall (Circuit Breaker and Bulkhead): These patterns prevent one agent or service from failing and prevent other agents or crashes, including damage.

Ensure that the job is performed correctly (consistent task execution)

Even with the reliability of individual agents, you need to be confident that the entire collaborative task will be completed correctly.

Consider:

• Atomic-style operation: True acid transactions are difficult with distributed agents, but you can use patterns like Sagas to design your workflow to work as close to atomic as possible.
• The Unchanging Logbook (Event Sourcing): Record all important actions and state changes as events in an immutable log. This gives you a perfect history, makes state rebuilding easier, and is perfect for auditing and debugging.
• Consensus: Important decisions may need to be agreed to by agents before proceeding. This includes simple voting mechanisms or more complex distributed consensus algorithms when trust and coordination are particularly difficult.
• Work verification (verification): After the agent completes the task, builds steps in the workflow to validate the output or state. If something is wrong, trigger a settlement or correction process.

The best architecture requires the right foundation.

• Post offices (message queues/brokers like Kafka and Rabbitmq): This is absolutely essential to detaching agents. They send messages to the queue. Agents interested in those messages will pick them up. This allows asynchronous communication, handles traffic spikes and is key to a resilient distributed system.
• Shared filing cabinet (knowledge store/database): This is where your shared nation lives. Select the appropriate type (relational, noSQL, graph) based on the data structure and access pattern. This requires performance and is very available.
• X-ray machine (observability platform): Logs, metrics, traces – these are required. Debugging distributed systems is notoriously difficult. Being able to see exactly what all agents were doing, when and how they interacted is unnegotiable.
• Directory (Agent Registry): How do agents find each other and find the services they need? A central registry helps you manage this complexity.
• Playgrounds (containerization and orchestration like Kubernetes): This is a way to ensure that all these individual agent instances are deployed, managed and expanded.

How do agents chat? (Choosing communication protocol)

The way an agent speaks affects everything, from performance to how tightly they are combined.

• Standard phone (REST/HTTP): This is easy, works anywhere and is suitable for basic requests/responses. However, it can feel a bit chatty and can be less efficient in bulk or complex data structures.
• Structured Conference (GRPC): It uses efficient data formats, supports a variety of call types, including streaming, and is type-safe. It’s ideal for performance, but you need to define a service agreement.
• Breaking News Board (Message Queue – protocols such as AMQP, MQTT, etc.): Agents post messages to topics. Other agents subscribe to topics that interest you. It is asynchronous, very scalable, and completely separates the sender on the receiver.
• Direct Line (RPC – not common): Agents call functions directly on other agents. This is fast, but creates a very tight coupling. Agents need to know exactly who they are calling and where they are.

Select the protocol that matches the interaction pattern. Is that a direct request? Broadcast event? A stream of data?

Put it all together

Building a reliable, scalable multi-agent system is not about finding a magic bullet. It’s about making smart architecture choices based on your specific needs. Do you tip more layers for control or allied for resilience? How do you manage that important shared state? What is your plan if your agent goes down (or not)? What are the non-negotiable infrastructure pieces?

Yes, it’s complicated, but by focusing on the blueprints of these architectures, you can tame complexity and build robust, intelligent systems that drive the next wave of enterprise AI by tuning interactions, managing shared knowledge, planning failures, ensuring consistency, and building on the foundations of solid infrastructure.

Nikhil Gupta is AI Product Management Leader/Staff Product Manager Atlassian.