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Agentic Orchestration: The Next Business Advantage
Why Orchestration Matters More Than Ever
In 2023, Unilever began quietly re-engineering its global supply chain with the help of AI agents. The goal was not just task automation, it was business adaptability on many dimensions. Today a network of specialized agents predicts factory slowdowns, adjusts shipments, and reroutes logistics, all while coordinating with human operators.
Deploying a single AI agent to perform a specific task is relatively straightforward with today’s tools and frameworks. Deploying multiple agents that each perform different tasks is a greater achievement. But coordinating a network of agents, each with its own goals, logic, and limitations toward a shared business outcome? That’s a complex endeavor.
How did Unilever do it? How can such a system be designed, scaled, and aligned to real business goals? And it’s precisely where orchestration becomes indispensable: as both a concept and a technological discipline that enables alignment, coordination, and control. The orchestration layer behind the scenes doesn’t just keep everything moving, it makes the system resilient, responsive, and ready for change. Unilever’s story isn’t about robots replacing humans. It’s about orchestration enabling intelligence at scale.
You’ve probably heard that AI agents are coming for tasks. What happens when those tasks need to talk to each other? What if the intelligence is there but in silos? If your business has deployed AI but still struggles with process hand-offs or coordination at scale, orchestration is likely your missing link.
Most organizations aren’t short on AI tools, they’re short on coordination. You may have agents that perform individual tasks well, but what happens when those tasks need to talk to each other? That’s where orchestration steps in: it’s the connective tissue that transforms siloed capabilities into cohesive, scalable solutions.
As AI adoption grows, orchestration becomes a key differentiator, not just between tools, but between companies that thrive and those that struggle with fragmented automation. After all, humans have been orchestrating intelligence at scale for centuries, across organizations, projects and societies. Orchestration is not just for AI. It is how complex work gets done when individual effort is not enough.
Understanding Agency in Simple Terms
Every complex task can be broken down into simpler tasks. And those, in turn, are composed of even smaller, more atomic actions. This is how both humans and machines approach problem solving: by building up complexity from simple, understandable units.
In AI systems, this layered structure is especially visible:
- Atomic agents: Perform micro-actions (e.g. extract a field from a document).
- Task agents: Group atomic actions into meaningful, reusable tasks (e.g. “process invoice”).
- Composite orchestration: Coordinates multiple task agents to fulfill complex business goals.
This structure mirrors how humans organize work. In fact, it reflects how teams function in business: individuals handling details, managers coordinating tasks, and leadership steering the whole.
For business leaders, this has two major implications:
- Easier maintenance: You can upgrade parts without disrupting the whole.
- Modularity: You can swap agents in or out based on performance, cost, or compliance.
Take a moment to think about the following: where in your business are tasks still stitched together manually, by email, spreadsheet, or Microsoft Teams message? Could they be done by an AI agent? That’s where agentic orchestration can quietly revolutionize your operations, in those small tasks that can be potentially done by agents.
Agency, after all, isn’t new. We, as humans, have it too. In that sense, orchestrating AI agents is simply extending how we’ve always scaled intelligence by working together. Agency is the capacity to act purposefully on a given context.
Visual vs. Implicit Orchestration
Now that we’ve broken down how tasks are structured, the next question is: how do we coordinate them all? This is where orchestration comes into play, not as a single method, but in different styles suited to different needs.
Orchestration can be visual or implicit.
Visual orchestration, think of BPMN diagrams or flow-based editors, is all about clear, predefined structures with known limits. Every decision path is laid out, every task defined. You can do that with agents too. This is especially helpful in environments where compliance, auditability, traceability, and stakeholder alignment are critical. You can see the process. You can show it to others. You can govern it. But like any approach, it has limitations: you might lose some degree of flexibility due to the nature of visual tools.
Implicit orchestration, by contrast, happens under the hood. It’s embedded in agent logic, prompt chains, or coordination code. It’s more flexible, more dynamic, and often faster to experiment with, but also less transparent. This form suits innovation labs, fast-moving teams, or scenarios where the logic may evolve rapidly.
The key is this: smart orchestration meets your people and your processes where they are. Not every stakeholder will trust a black-box system, and not every workflow needs a flowchart. Start by mapping one process you know. Then ask: what could an agent handle, and what still needs a human touch?
Here’s a simple guide:
- Use visual models when you need transparency, traceability, or regulatory oversight.
- Use implicit orchestration for speed, flexibility, and iteration.
- Combine them: for example, a BPMN model that calls LLM-powered agents as tasks.
Orchestration, in the end, is not about control for its own sake, it’s about coordination for impact.
Context Is King
For an agent to make a relevant decision or perform the right task, it needs to understand what has already happened, what’s supposed to happen next, and what the overall objective is. In other words, it needs context.
Humans gather context from many sources: our environment, our experiences, instructions, cultural norms, even subtle signals like body language or tone. This is second nature to us but agents don’t have that built-in understanding. Agents need orchestration systems to provide that context explicitly.
Here’s what orchestration provides in business-relevant terms:
- Awareness of current state: Like a project manager knowing the exact status of each task.
- Sequencing and logic: Like a team lead assigning the right order of steps to reach a goal.
- Coordination with people: Making sure agents know when to wait for or involve a human.
- Error handling and recovery: Built-in responses for when something doesn’t go to plan.
Without orchestration, agent behavior can become fragmented or erratic, just like a team with no shared plan. With orchestration, you enable agents to work together intelligently, reliably, and in alignment with your business outcomes.
Structures of Orchestration and the Role of Governance
Not all orchestration looks the same. Depending on your business context, you may need more control, more flexibility, or a mix of both. That’s where orchestration structures: centralized, decentralized, and hierarchical, come in.
Let’s start with logistics. UPS’s ORION platform (On-Road Integrated Optimization and Navigation) is a masterclass in centralized orchestration. It coordinates 60,000 delivery routes daily using AI agents that take into account weather, traffic, and volume, within clear operational parameters. This highly structured model has saved UPS over $300 million per year.
Now contrast that with Microsoft’s Semantic Kernel integration with ServiceNow. Here, a hierarchical orchestration model is in play. A manager-agent oversees sub-agents, delegates tasks, monitors outcomes, and calls in a human only when necessary. It’s layered coordination with oversight.
In more experimental or dynamic environments, you’ll often find decentralized orchestration. Agents operate with more autonomy, collaborating peer-to-peer. It’s like a network of smart assistants sharing information to reach a goal.
Each model requires its own flavor of governance, which becomes more critical the more autonomy you give:
- Checkpoints for human review: For business-critical moments.
- Retry and fallback mechanisms: When agents fail or encounter ambiguity.
- Audit trails and logging: For accountability, compliance, and trust.
- Autonomy is only powerful when paired with responsibility. Governance is how you keep agentic systems safe, scalable, and aligned with business goals.
So ask yourself: where does your business need orchestration to be tight and controlled and where can it be more adaptive? Understanding your orchestration structure is the first step toward building systems that don’t just act but act wisely.
Where is Orchestration Heading?
We’re seeing four major trends:
- Platform ecosystems: End-to-end orchestration suites that combine visual tools, agents, and governance
- Agent hierarchies: Multi-level planners directing lower-level agents, a new digital org structure
- Invisible orchestration: Business users define goals; systems build the flow behind the scenes
- Human-AI symbiosis: Orchestration integrates scheduled human input to stay on track
The question isn’t whether orchestration will evolve, it’s how you’ll evolve with it. Which process will you orchestrate differently tomorrow?
Before you close this tab, ask yourself: what’s the one process in your company that most needs orchestration, and what would it take to start small today?
If you’re curious about how orchestration could transform your operations reach out to us: https://jit.at/kontakt/
Our team helps organizations move from isolated automation to cohesive, scalable orchestration strategies. Let’s explore what intelligent collaboration could look like in your context.
