Why orchestration is key to scaling AI agents in procurement

In the age of large language models (LLMs), building AI agents has become deceptively simple. With a well-structured prompt, anyone can create an intelligent-seeming system capable of generating text, analysing documents, or triggering API calls. But in procurement, as in any complex enterprise function, what distinguishes a successful AI deployment isn't the cleverness of an individual agent. It's how well it can interact with its environment.

Agentic AI systems promise to act with autonomy and intent. Unlike traditional automation or simple chat assistants, true AI agents go beyond passive responses. They interpret their environment, make decisions, and execute tasks with minimal human input.

But autonomy is not the same thing as isolation. A lonely robot is a sad robot.

Like their human counterparts, AI agents need context, structure, and collaboration to truly function in enterprise settings. This is where the orchestration challenge begins.

From intelligence to orchestration

In procurement, agents are expected to handle complex workflows such as supplier onboarding, contract compliance, or fraud detection. These are not isolated tasks. They require data from multiple systems, interaction with human stakeholders, and seamless integration into broader business processes.

This means orchestration is critical on three fronts:

1. Access to systems and data: An agent cannot act intelligently if it lacks the permissions or integrations to retrieve the information it needs. For example, a risk review agent evaluating supplier profiles must have secure, auditable access to ERP systems, supplier databases, legal documents, and external regulatory feeds. Without this, its assessments are blind guesses, not informed decisions.
2. Output pathways: Just as important as inputs are the mechanisms by which agents deliver outputs. Whether generating a recommendation, escalating a compliance concern, or triggering a workflow, agents must do more than suggest - they must act. This requires integration into orchestration platforms that can support downstream automation and multi-agent collaboration.
3. Exception handling and escalation: Not every task can or should be resolved autonomously. Like human agents, AI agents must be able to escalate ambiguous or risky issues to the right stakeholders. This ensures that human judgment remains at the heart of critical decisions, particularly where compliance or legal exposure is at stake.

What's easy (and what's not)

In a technical sense, building an agent today is easy. In the LLM world, an agent is often little more than a prompt or instruction set wrapped in a decision framework. But deploying agents responsibly and at scale within an enterprise ecosystem is much harder.

Security, governance, and accountability cannot be afterthoughts. Role-based controls, audit trails, and policy-aware decision boundaries must be built into every agentic deployment. Similarly, agents must be trained not only to perform tasks but to collaborate. They need to hand off work, provide status updates, and learn from feedback loops that include human colleagues.

This orchestration complexity mirrors the real-world structure of procurement teams. In traditional environments, workflows are carried out by human agents with defined roles, escalation paths, and access rights. The shift to AI agents does not eliminate these needs.

Enabling the orchestration layer

Imagine an AI agent designed to assess supplier risk. It's been carefully configured, primed with a sophisticated prompt, and tested in isolation. In a sandbox environment, it performs beautifully, pulling insights from financial documents, flagging anomalies, and even suggesting next steps. But the moment it's placed in a live procurement setting, it stalls. Why? Because it cannot access the supplier master data. It cannot push updates to the compliance system. And when it encounters a borderline case, it has no defined path to escalate the issue to a human decision-maker.

This is the orchestration gap.

For AI agents to function in procurement (and in general), they must be embedded in a connected, well-governed ecosystem. It is not enough for agents to "know" what to do. They must be able to act. And action requires infrastructure.

That infrastructure is the orchestration layer.

This layer acts as the connective tissue between agents, systems, and people. It ensures that each agent has secure, role-based access to the data it needs, whether it lives in ERP, CLM, sourcing platforms, or external risk databases. It governs how decisions are made and logged, providing audit trails and controls to meet regulatory and enterprise requirements. And it enables escalation: when agents hit a wall, the orchestration layer ensures that humans are brought into the loop quickly and with full context.

Just as importantly, this layer allows agents to collaborate, not only with people, but with one another. An intake management agent can pass a purchasing request to a supplier vetting agent. A fraud detection agent can flag anomalies that a contract compliance agent investigates further. Together, they can drive a process from initiation to resolution, each operating within their domain while contributing to a larger objective.

Ultimately, the orchestration layer is what transforms agents from isolated automations into team players. It makes it possible for procurement teams to scale their use of AI without losing control, compromising security, or fragmenting their workflows. And as agents become more capable and more autonomous, this orchestration becomes a critical enabler of enterprise success.

Two paths: tactical or strategic?

Agentic AI has the potential to transform procurement. It's not just the latest automation tool, but has the potential to fundamentally reshape how decisions are made, how compliance is maintained, and how value is created. But this potential will only be realised if we look beyond the agent itself and focus on the broader orchestration challenges that enable agents to thrive.

In this new paradigm, the question is not "Can we build an agent?" That part is easy. The real question is: "Have we built the ecosystem in which agents can operate securely, collaboratively, and at scale?" For procurement leaders, answering this question will determine whether AI is a tactical enhancement or a strategic revolution.