Single-purpose AI agents are useful. An agent that handles customer support tickets, another that summarises meeting notes, a third that monitors data pipelines — each one solves a specific problem reasonably well.

But the moment you have more than a few agents running in your organisation, you hit a new problem: how do they coordinate? How do you prevent conflicts when two agents try to update the same system? How do you maintain visibility into what dozens of autonomous processes are doing? How do you debug failures that cascade across agents?

This is the orchestration problem, and it’s spawning an entirely new category of software platform.

Why Orchestration Is the Next Bottleneck

Think about what happens in a mid-size company that’s deployed AI agents across different functions. Marketing has an agent generating content briefs. Sales has one qualifying leads. Operations has one managing inventory forecasting. Finance has one reconciling expenses.

Each agent was probably built or deployed by a different team, possibly using different frameworks. They might share data sources but don’t communicate with each other. When the marketing agent generates content about a product that the inventory agent knows is being discontinued, nobody catches the contradiction until it’s published.

This isn’t a hypothetical. It’s happening right now in organisations that moved quickly on AI agent deployment without thinking about the coordination layer. And the problem scales non-linearly — twice as many agents doesn’t create twice as many potential conflicts, it creates exponentially more.

The Platform Players

Several categories of company are competing to become the orchestration standard.

The cloud hyperscalers. Amazon Web Services has been building out multi-agent orchestration capabilities within Bedrock. Google Cloud’s Vertex AI Agent Builder has similar ambitions. Microsoft’s Copilot ecosystem is arguably the most mature, since it already has agents embedded across Office 365, Dynamics, and Azure. The advantage these players have is integration with existing enterprise infrastructure. The disadvantage is vendor lock-in and the usual enterprise cloud complexity.

The AI-native startups. Companies like CrewAI, LangGraph (from LangChain), and Autogen (from Microsoft Research, though operating somewhat independently) are building frameworks specifically for multi-agent orchestration. These tend to be more flexible and developer-friendly but require more technical capability to deploy and maintain. CrewAI in particular has gained traction with its role-based agent framework that makes it intuitive to design agent teams.

The workflow automation incumbents. Companies like Zapier, Make, and n8n are adding AI agent capabilities to their existing workflow automation platforms. Their pitch is that orchestration is really just a sophisticated version of what they’ve always done — connecting different tools and managing workflows between them. There’s truth in that, though the complexity gap between triggering a Slack notification and coordinating autonomous AI agents is substantial.

The enterprise AI consultancies. This is where firms that build custom agent solutions come in. AI agent development specialists are designing bespoke orchestration architectures for businesses that need agents tailored to their specific operational workflows. In Australia, companies like Team400 are building these systems for organisations where off-the-shelf platforms don’t fit — particularly in industries with unique regulatory requirements or complex legacy system landscapes. The advantage of the consultancy approach is that orchestration gets designed around the actual business process rather than the other way around.

The Technical Challenges

Orchestration sounds straightforward in theory. In practice, several hard problems remain unsolved.

State management. When multiple agents are working on related tasks, they need shared context. But shared state creates concurrency problems. If two agents read the same data, make decisions independently, and then both try to update it, you get conflicts. Traditional software engineering solved this decades ago with databases and transaction management, but AI agents don’t operate in neat transaction boundaries.

Failure handling. What happens when one agent in a chain fails? Does the whole workflow stop? Does it retry? Does another agent pick up the task? The answer depends on the specific workflow, which means the orchestration layer needs flexible, configurable failure policies. Most current platforms handle this crudely — retry three times, then alert a human.

Observability. When an agent makes a bad decision, you need to understand why. That requires logging not just what it did, but the reasoning chain that led to the decision. Multiply this across dozens of agents and you’ve got a serious observability challenge. The emerging standard is some form of trace logging — similar to distributed tracing in microservices — but purpose-built for AI reasoning chains.

Security and permissions. Each agent needs access to certain systems but not others. The orchestration layer needs to manage agent identities, permissions, and audit trails. This is especially critical in regulated industries like healthcare and finance, where you need to demonstrate exactly which agent accessed what data and why.

What This Means for Businesses

If you’re running AI agents in your organisation — or planning to — the orchestration question is going to become urgent within the next twelve months. A few practical recommendations.

Don’t wait for the perfect platform. The market is immature and no single solution does everything well. But doing nothing means accumulating technical debt as your agent fleet grows.

Start with visibility. Before you orchestrate, you need to know what’s running. Audit your existing AI agents, document what they do, what data they access, and how they interact with other systems. Many organisations are surprised by how many ad-hoc agents have been deployed by individual teams.

Think about governance early. Agent orchestration isn’t just a technical problem — it’s a governance problem. Who decides which agents get deployed? Who’s responsible when an agent makes an error? What’s the escalation path? These questions are easier to answer now than after an incident.

The orchestration platform market will consolidate over the next two to three years. Some of today’s startups will be acquired, some of the hyperscaler offerings will mature, and a few clear standards will emerge. But the organisations that start thinking about orchestration now — even imperfectly — will be better positioned than those that wait for the dust to settle.