Agentic AI is changing how people think about what technology can do.
It can reason through context. It can synthesize information. It can explain outputs. It can help users move from a question to a recommendation, or from a signal to a next step.
That opens up exciting possibilities for supply chain planning.
It also raises an important question: should every problem become an agent problem?
The answer is no.
And that is a good thing.
Choosing the right kind of work
One of the most valuable parts of the Kinexions Maestro Remix Agent Hackathon is not just seeing what customers and partners build in Maestro Agent Studio. It is seeing how they decide where agents can create real value.
The strongest opportunities are not always the biggest workflows or the most complex processes. Often, they are the high-friction moments where planners need to interpret changing conditions, connect multiple signals, and decide what should happen next.
That is where agents can be especially useful.
A planning problem may be a strong fit for an agent when the work involves judgment, synthesis, or interpretation. When the same question appears in slightly different ways each time. When the user needs context from multiple sources. When an explanation is just as important as the answer.
These are familiar moments in supply chain planning. A late purchase order is not just a late purchase order. Its impact depends on demand, inventory, customer priority, timing, alternatives, and tradeoffs. An agent can help bring that context together so a planner can decide what to do next.
When automation may be better
There are also problems where an agent may not be the best answer.
If the work is purely deterministic, and the same input should always produce the exact same output, traditional automation may be simpler and more effective. If the problem is rare, it may not justify the design effort. If the required data does not exist or cannot be accessed, an agent cannot create value from context it does not have.
That distinction matters.
Agentic AI should not be used just because it is new. It should be used where its strengths match the work: reasoning across context, handling variability, generating explanations, and supporting decisions.
Better questions, better agents
This is part of what makes Remix so interesting.
Teams are not only learning how to build agents. They are learning how to ask better questions about agentic AI.
- What should the agent help with?
- What context does it need?
- Where should the user stay in control?
- What kind of output would actually help someone make a better decision?
Those questions are the beginning of good AI design.
As Remix continues, we will see many different ideas take shape. Some may support reporting. Some may compare scenarios. Others may surface risks or recommend next steps.
The most valuable ones will start with the same decision: choosing the right problem to solve.
Over the coming weeks, we’ll follow Remix teams as they turn ideas into working agents and show where agentic AI delivers real value in supply chain planning.
