At Predyktable, we spend our time inside the systems that keep businesses running: supply chains, workforce schedules, asset maintenance, demand forecasting. They’re engineered with rules and guardrails so things stay predictable. But the real world doesn’t behave so neatly. A spike in demand can ripple through logistics, create staffing bottlenecks, and end up as a financial headache. That’s where Agentic AI for supply chain planning comes in.
Where we are at
Right now, our platform lets clients explore “what-if” scenarios in plain English. Instead of wrestling with spreadsheets or tweaking models by hand, someone can simply ask:
- “What happens if we can improve our productivity by 10%?”
- “How would a heatwave impact demand patterns in our warehouse?”
Our agent reads the question, maps it onto the structured models we’ve built, and adjusts the relevant parameters. Then it runs the simulations, side by side — base case vs. scenario — so the impact is immediately clear. This process is enhanced by utilising Agentic AI for Supply Chain Planning.
What used to take hours or days of manual rework now takes minutes. And decision-makers don’t just get a black box output — they see the reasoning and trade-offs behind it.
Where are we going?
I was at a workshop recently with one of our partners, ThoughtSpot. They shared an interesting observation: when they first guided customers on improving their AI agents, they recommended lots of reference questions — almost like training wheels to keep the agents on track. But after a few months of real-world use, the data told a different story. The agents actually performed better when they were given more context and less handholding.
At Predyktable, we see the same principle in labour planning and other structured systems. You can try to script every possible decision path, but it’s impossible to capture the full range of real-world complexity. Instead, the smarter approach is to give agents rich, contextual understanding of the system they’re working within and tools to use, and then let them reason dynamically inside it.
This is exactly what we’re building: a contextual labour planning system where agents don’t just follow a set of predefined adjustments, but interact with the system in a way that mirrors how a skilled planner would; guided by context, but not micromanaged.
Working as a team
At the moment, our agent works inside the boundaries we’ve set: fixed parameters, predefined constraints. That’s deliberate, because structure matters. But we’re already looking at the next step: giving the agent freedom to challenge those boundaries.
For example, instead of only testing “what happens if deliveries are reduced by 5%,” an agent could reason that shifting fleet capacity between regions might require relaxing delivery windows by two hours, and then test that idea. It’s a shift from running guided simulations to making autonomous adjustments inside structured systems.
But what is next? We are excited about what happens when agents don’t just reason within one system, but start coordinating across many.
Imagine a Predyktable demand agent spotting a surge. Instead of just flagging it, it nudges a logistics agent to unlock extra fleet capacity. That logistics agent passes the signal to a workforce agent to reshuffle staffing, while a finance agent checks the budget impact. Each agent stays true to its own domain constraints, but together they adapt in real time.
That’s not just scenario modelling anymore, that’s a network of SME agents making decisions across the enterprise.
The impact
When systems can talk to each other through agents, businesses gain resilience. Disruptions in one area get absorbed across the network instead of piling up in silos. Trade-offs become clearer because every action comes with an explanation of the ripple effects. And as new systems come online, you don’t have to rebuild from scratch, you just plug in another agent.
At Predyktable, the we’re already running scenario modelling: one generative AI agent that turns natural language scenarios into concrete, testable outcomes. The future (a future we’re already working on) is a world where those agents can collaborate, cross boundaries, and make decisions as seamlessly as the people running the business.