Everyone’s talking about AI agents, but few are asking what they actually need to deliver on their promise. In supply chain, it’s not enough for agents to move fast or automate tasks. To drive real business value, they have to orchestrate and that takes more than just data. It takes optimization, real-time signals, and cross-functional coordination.
Agentic AI refers to autonomous software entities that can perceive, reason, and act. And while the term has gained traction fast, the reality is still catching up to the marketing.
What’s often missing in these conversations is a clear understanding of what orchestration truly requires. It’s not just about speed or autonomy. Real orchestration means making better decisions across traditionally siloed functions – demand, supply, sourcing, logistics – in real time, all at once.
And that takes more than agents sitting on top of data. It takes agents connected to optimization tools, scenario engines, and dynamic inputs from across the business. It takes concurrent orchestration, not sequential automation.
The Limitations of First-Generation Agents
The promise of agentic AI is compelling: systems that can self-monitor, self-adapt, and self-correct. But many so-called agents today are still workflow bots – reactive and bounded in scope. They move fast, but they don’t always move smart. Speed without structure isn’t orchestration. It’s just faster failure. True agentic orchestration doesn’t just replicate decisions more quickly. It improves decision quality across the supply network.
Why Agents Need More Than Just Data
Too often, “orchestration” just means putting an agent on top of static data. That might trigger alerts or launch predefined workflows, but it doesn’t help with real trade-offs. These agents often miss operational realities like lead times, capacity constraints, and service-level impacts because they lack the depth to compute meaningful options.
To move from alerting to action, agents need more than access to data. They need optimization engines, heuristics, and models that can simulate different scenarios and recommend the best path forward. And they need real-time inputs not just from ERP systems, but from sourcing, logistics, and production.
Most agent architectures fall short because the intelligence isn’t in the agent itself. It’s in what the agent can tap into.
From Sequential Planning to Concurrent Orchestration
Most supply chain planning still happens in a sequence: demand hands off to supply, then to logistics. It’s slow, siloed, and often disconnected from what’s actually happening on the ground.
Concurrent orchestration changes that. It allows agents and humans to collaborate across planning functions continuously and in real time. That alignment doesn’t just save time. It improves margins, reduces risk, and builds resilience.
Redefining the Human Role in AI-Driven Orchestration
This isn’t about removing humans. It’s about repositioning them.
The future isn’t about faster alerts or slicker dashboards. It’s about embedding action, accountability, and optimization into the flow of decisions. As AI handles more orchestration, humans will define priorities, weigh risks, and guide ethical trade-offs. They remain accountable for what gets executed even if they didn’t initiate the action.
Built-in guardrails ensure that agentic systems stay aligned with business strategy and stakeholder expectations.
Outcome-First: What Orchestration Really Requires
The future of orchestration isn’t agent-first or human-first. It’s outcome-first. And delivering on that future requires:
- Agents connected to optimization engines
- Decisions made concurrently across planning functions
- Real-time inputs from across the network
- Human accountability built in by design
The vendors who deliver this full stack, not just the interface, will define the next era of supply chain performance.
by Andrew Bell, Chief Product Officer – As chief product officer, Andrew oversees the Kinaxis’ engineering, development and product management teams, as well as the company’s continued investment in AI and machine learning, with over 50 issued and pending patents. Andrew joined Kinaxis in June 2012 from Alcatel-Lucent, a global provider of digital age networking, communications and cloud solutions, where he managed the Customer Premise Equipment product line for North America. Andrew holds a Bachelor of Engineering (Computer Systems) degree from Carleton University. He lives in Ottawa, Ontario.
