Understanding the True Hierarchy of Multi-Agent Orchestration
Why precision in AI terminology matters more than you think
In the rapidly evolving world of AI, terminology often becomes currency. And like any valuable currency, it's frequently counterfeited. Today, we're witnessing this phenomenon with "Multi-Agent Orchestration" (MAO) a term that's being stretched, diluted, and repurposed to describe everything from simple chatbot chains to complex agentic AI systems.
This isn't just semantic pedantry, it has real business consequences. As Stephen Klein, CEO of Curiouser.AI, recently noted in his LinkedIn post: "The 'agentic' AI narrative is driven by self-interest, not reality." When we blur the lines between fundamentally different AI approaches, we create a dangerous gap between expectation and reality. The risk becomes businesses investing in AI solutions expecting orchestrated precision but receiving something closer to coordinated chaos because they've chosen based on the loudest voice in the room rather than selecting the right tool.
What we're saying here is NOT that 'agentic AI' or any other type of AI is inherently bad or flawed, but rather that understanding distinctions matters crucially for AI implementation success.
The Orchestra Analogy: Why Precision Matters
Think of Multi-Agent Orchestration as both the conductor of an orchestra and the composer of the music. The composer creates the plan (the musical score), while the conductor ensures each musician plays the right notes at precisely the right moment to create harmony. Remove either element, and you lose the orchestration.
Yet in today's AI landscape, what we're seeing is people calling everything from a solo pianist to a garage band an "orchestra." Klein's critique hits at the heart of this problem: much of what's marketed as "agentic AI orchestration" consists of brittle prompt chains and hard-coded workflows dressed up in fancy language. The result? Organisations implement the wrong type of AI for their operational challenges and wonder why they're not achieving the transformational results they expected.
The Architecture vs. Orchestration Confusion
This terminology blur becomes even more apparent when we examine how the industry discusses "AI agent ecosystems." A recent LinkedIn post outlined a detailed technical blueprint for building "agentic systems" with multiple layers: input/output processing, orchestration frameworks, reasoning models, data integration, and agent interoperability protocols.
Whilst technically sophisticated, this architectural approach exemplifies the fundamental confusion between building agent systems and achieving agent orchestration. The post describes creating multiple specialised agents that "share information and coordinate their work seamlessly," but coordination isn't orchestration, and having multiple agents doesn't automatically create optimal outcomes.
This represents exactly what Klein criticised: taking existing multi-agent coordination concepts and rebranding them as "agentic AI" to align with current trends. The result is technically impressive systems that may coordinate effectively but fall short of the mathematical optimisation that true multi-agent orchestration demands.
The Core Technical Distinction
This confusion stems from a fundamental misunderstanding of how different AI approaches solve different types of problems. As we've often said, AI is not a thing, but rather a field. Different types of AI are uniquely positioned to solve different types of problems. Use a system that works on statistical modelling when advanced reasoning is required, and you'll have the wrong solution, and vice versa.
Most "agentic AI" systems being marketed today are built on probabilistic foundations, using machine learning models that predict the "next best action" based on statistical patterns in training data. As Dr. Flanagan explains in his research, these systems "operate on statistical principles" and "predict outcomes based on data, often with remarkable accuracy." However, the challenge arises when the complexity of the planning operation increases. Individual decisions may appear correct, and on small-scale problems, statistical methodologies may find correct answers. However, the likelihood of a wrong decision increases with more complex problem spaces. These problems are then compounded by the fact that later decisions rely on them. Therefore, as soon as the system makes a mistake, it has a snowball effect on everything else. Before you know it, the entire solution is completely invalid. For problems that require end-to-end multi-agent planning, such outcomes are almost guaranteed, no matter how well the system is trained.
True Multi-Agent Orchestration, by contrast, employs deterministic logical reasoning approaches like SAT-solving. These systems don't predict optimal solutions—they mathematically prove them. The key difference is that when a decision is made, the effect of that decision is checked across the entire solution. The distinction isn't academic: when every action in your operation has a cost, and suboptimal coordination compounds those costs across your entire system, the difference between "probably good" and "provably optimal" becomes financially critical.
It's also worth noting that logical-reasoning systems aren't just pre-defined algorithms. There exist pre-defined algorithms that solve multi-agent planning problems (although none that can do so with more than a few agents), but these systems do not provide optimal results. Instead, they follow a predetermined set of steps to arrive at a conclusion, and the quality of this conclusion is the same each time, irrespective of the task requirements. Think of it like solving a Rubik's Cube with one of the many algorithms available. Each will get you to a solved cube, but none consider the number of turns required. In contrast, logical-reasoning AI systems start by analysing the problem at hand and work tirelessly to find the absolute best solution each time.
The Market Reality
Klein's assessment reveals a troubling pattern: "Follow the money... GenAI vendors need fresh narratives to keep funding flowing." This pressure creates what he calls "Hype-as-a-Service" (HaaS), where marketing terminology evolves faster than the underlying technology.
This creates a harsh reality where it pits all AI against one another regardless of their use case, because the misuse of terminology has warped perceptions.
The Hierarchy That Brings Clarity
To cut through this confusion, we've developed what we call the "Hierarchy of Multi-Agent Orchestration", a framework that maps the progression from basic AI agents to true orchestrated intelligence.
Layer 1: Agents
"Anything that can meaningfully affect change in an environment"
At the foundation, an agent is simply something capable of purposeful action within its context. This could be a forklift in a warehouse, a software bot processing data, or a human completing tasks. The key insight? Agents don't need to be artificial or intelligent, they simply need the capability for meaningful action.
Layer 2: Multi-Agent Systems
"Any system using more than one agent to collaboratively get something done"
When multiple agents work together toward a common goal, you have a multi-agent system. Think of a logistics operation where drivers, warehouse staff, and management software all contribute to shipping products. Collaboration is optional and rarely optimised. Much of what we're seeing being called multi-agent orchestration sits at this level. We're hearing more about MAO being a system that simply selects an AI agent, out of a list of possible agents, for a job. Useful, for sure, but not true orchestration. It's akin to selecting a violinist from a room of musicians to play a solo, rather than using all musicians to play a symphony.
Layer 3: Multi-Agent Planning
"Systematically coordinating all agent actions to optimally achieve objectives"
Here's where AI-powered planning enters the picture. Rather than hoping agents coordinate effectively, the system methodically determines the optimal sequence of actions across all agents. It's the difference between a group of musicians playing arbitrarily at the same time and following a carefully composed score.
Layer 4: Multi-Agent Orchestration
"Planning, dispatching, monitoring, and autonomously adjusting in real-time"
True orchestration transcends planning. It doesn't just create optimal plans, it executes them dynamically, continuously monitoring conditions and adapting in real-time. When disruptions occur, the system doesn't just alert human operators; it autonomously recalculates and implements new optimal strategies.
Why the Distinction Matters
The Technology Challenge: The difference between simply allocating tasks and planning is significant. You can have a system that correctly picks an agent for any given task. However, the real challenge is understanding how that impacts the entire system. In situations that require advanced planning, precisely coordinating every action is far more relevant and complex than agent selection. It becomes even more complicated when you have multiple agents of the same type. Choosing the correct one is no longer about understanding their capabilities, but rather knowing everything they have done up to that point and everything they're likely to do afterwards.
As Dr. Flanagan notes in his research, this is particularly problematic for "full planning" scenarios: "machine learning approaches that work well for reactive, single-step decisions often fail spectacularly when applied to full operational planning." The reason is mathematical: probabilistic systems compound uncertainty across multiple decisions, whilst deterministic logical reasoning maintains mathematical guarantees throughout the entire planning sequence.
Klein's warning applies directly here: regardless of how sophisticated the underlying architecture, probabilistic approaches cannot deliver the mathematical certainty that operational planning demands.
The Business Impact: Organisations expecting Layer 4 orchestration but receiving Layer 2 often see minimal ROI from their AI investments. They're left managing the complexity manually whilst paying for automation that doesn't truly automate.
The Strategic Risk: As Klein warns in his post, AI systems, including the latest generative models, are agents; however, they are not exhaustive definitions of agents. Yet they're being marketed as if they are, leading to systemic misallocation of resources and strategic planning based on false premises.
Educated Decision-Making
The solution isn't to avoid agentic AI or any other form of AI to adopt, instead it's to approach it with a clear understanding of what different approaches can and cannot deliver. But this starts even before technology selection, and it begins with a fundamental concept; ‘proper problem articulation.’
As we explored in our previous article, "The Art of Problem Articulation: Why Most AI Implementations Fail", the success of any AI implementation doesn't start with choosing the right technology, it actually starts with articulating the right problem. Organisations that rush to implement "agentic AI" without first atomising their operational challenges often end up with sophisticated solutions to problems they don't actually have.
For Reactive Decisions: When you need AI to make rapid, context-dependent choices (like customer service responses), machine learning approaches excel. These systems are "reactive" and "predict the next token or action based on training data", perfect for their intended use case.
For Operational Planning: When you need optimal coordination across multiple resources with clear objectives, logical reasoning approaches using technologies like SAT-solving provide mathematical guarantees that statistical models cannot match.
For True Orchestration: When your operations require continuous adaptation to changing conditions while maintaining optimal performance, you need systems specifically designed for dynamic replanning and autonomous adjustment, not "glorified wrappers" or "task orchestrators stitched together from APIs."
The Real Orchestration
True Multi-Agent Orchestration represents more than technological advancement, it's a fundamental shift from managing complexity to orchestrating simplicity. It transforms the overwhelming challenge of coordinating multiple resources into an elegant, automated process that adapts faster than human operators could manually adjust.
The key is understanding not just what these systems can do, but how they achieve it. Mathematical optimisation through logical reasoning provides guarantees that statistical approximations cannot match. When every action in your operation has a cost, and suboptimal coordination compounds those costs across your entire system, the precision of true orchestration isn't just valuable, it's essential.
The Choice Ahead
As AI continues to evolve, we face a choice: accept the blurred terminology and risk implementing solutions that don't match our needs, or demand clarity about what different approaches actually deliver.
Understanding the hierarchy from agents to orchestration is crucial, but it's only half the battle. The other half is clearly articulating your operational challenges before any technology discussion begins. When you combine precise problem articulation with a clear understanding of the Multi-Agent Orchestration hierarchy, you create the foundation for AI implementations that deliver genuine transformation rather than expensive technical novelty.
The future belongs to organisations that understand these distinctions—those who can cut through the marketing noise to identify technologies that truly transform operations rather than simply digitise existing inefficiencies.
Multi-Agent Orchestration, properly understood and implemented, doesn't just coordinate resources—it orchestrates intelligence. The question isn't whether your organisation will adopt AI, but whether you'll choose technologies that truly transform or merely automate.
This article builds on our previous exploration of problem articulation in AI implementations. Together, these pieces provide a framework for both understanding AI capabilities and defining problems that AI can meaningfully solve.