The term “agentic” is having a moment. If you’ve scrolled LinkedIn lately, you’ve likely run into a headline touting AI systems that can “do it all.” The hype is real, but so is the confusion. What exactly is an agentic system? And more importantly: what does it mean for business?

Agentic AI isn’t magic. It’s a way of architecting AI systems to sense, decide, and act autonomously across multiple modalities, environments, or organizational functions. This isn’t your typical automation, where predefined rules trigger a purchase order if inventory dips below a set threshold. Agentic systems are context-aware, outcome-oriented, and able to reason in real time.

Before we define these agents further, let’s begin with what they’re not. Agentic systems aren’t all-powerful tools and they’re definitely not chatbots. Deceptively, most systems marketed as “agentic” today are really just automation dressed up with a buzzy new name — trigger-based responses to predefined rules. If inventory drops, reorder. If it’s 9 a.m., send the email. Automation follows rules.

True agentic systems, by contrast, pursue goals. They’re built to sense, decide, and act autonomously across modalities, environments, and organizational functions. They’re context-aware, outcome-oriented, and capable of real-time reasoning. Rather than executing a script, they dynamically orchestrate actions to achieve objectives and adapt as conditions change. That’s what makes them agentic. They have agency. 

One area where the potential for transformation is especially clear is in procurement. Often seen as a routine back-office function, the procurement process is a perfect candidate for reimagination using agentic systems. In this new model, intelligent agents don’t just automate reorders. They interpret demand signals, negotiate terms, select vendors, and trigger fulfillment. Eventually, these systems will do this without waiting for human approval. This shift from reactive transactions to autonomous, goal-driven coordination across the supply chain is what we are calling “Agentic Buying.”

To see what this looks like in practice, let’s imagine how it might work at a company everyone knows: Starbucks.

How Agentic Buying Could Work at Starbucks

It’s fall in the Midwest. Demand for Pumpkin Spice Lattes is surging. A cold snap has people reaching for warm drinks, and local TikTok influencers are fueling the craze. Historically, it might take a few days for regional managers to notice the spike and adjust inventory orders.

In an agentic system, however, demand sensing can start before human involvement. 

Sensors are everywhere, embedded in everything from refrigerators and point-of-sale systems to mobile apps and employee wearables. Agentic systems tap into this infrastructure to form what’s called a sensory mesh: a dynamic, distributed network that continuously captures signals from the physical and digital environment.

These devices don’t merely observe, but also generate structured commerce intents. That means machine-readable requests specifying things like SKU, quantity, budget ceilings, sustainability constraints, and delivery windows. It’s not just, “We’re low on stock.” It’s, “Order 500 pounds of nutmeg from a supplier with 95% on-time delivery and a sub-$X/lb price, to arrive at the Chicago distribution warehouse by Wednesday.”

This structured intent is routed through Starbucks’ agent orchestration layer which kicks off the discovery phase.

Step 1: Agent Discovery

In the agent economy, vendors are discovered by metadata. Agents crawl supplier marketplaces and B2B platforms like SAP Ariba and Alibaba Cloud. Some companies who sell niche ingredients may use their own Application Programming Interfaces (APIs), which are sets of rules and specifications that allow different software systems to communicate and interact with each other.

The suppliers who rise to the top aren’t those with flashy branding but those who:

• Provide transparency into inventory levels and product catalogs in real time
• Share clean ESG scorecards and delivery SLAs
• Provide APIs with uptime logs, historical performance, and even emissions data

While marketing to humans may be more of an art than a science, with agents it’s all about the data. Agents that are easily accessible, parsable and searchable will be the ones with the largest advantage in the space. 

Step 2: Shortlisting and Negotiation

Let’s say three nutmeg suppliers make the shortlist. Supplier A is inexpensive but has a history of delayed shipments. Supplier B is more costly but boasts a 99% on-time delivery rate. Supplier C offers the most sustainable option, but falls outside the required delivery radius.

Starbucks’ procurement agents weigh trade-offs in real time, running simulations to balance cost, speed, and ESG alignment. They might:

• Accept a time-sensitive bulk discount offer
• Bundle orders across regions to unlock volume pricing
• Trigger a fallback vendor if delivery risks emerge

In this phase, negotiation is algorithmic. Starbucks’ agents field offers and counter-offers directly with their suppliers’ own agents. In some cases, agents act like swarms, to work across different countries to find niche vendors or new ingredients. These agents take into account millions of data inputs and run simulations to ensure Starbucks achieves the best possible price and product. 

Step 3: Execution and Delivery

Once terms are accepted, agents execute the purchase:

• Purchase orders are sent
• Contracts are signed via smart-contract protocols
• Payment schedules adjust dynamically based on vendor performance

Then comes the logistics. But here’s the catch: if a courier doesn’t relay estimated time of arrival data through an API, it simply won’t be selected. Agents rely on transparency to function. They need reliable, real-time information to coordinate decisions and adjust plans as conditions change. In the agent economy, opacity isn’t just inconvenient. It disqualifies you. The system will route around it.

Agents keep tabs on deliveries by pulling in data from port feeds, carrier APIs, IoT sensors, and even the weather. If something’s running late, they can shift inventory from an alternate warehouse to keep things on track. If a storm is heading toward Chicago, they’ll test out alternate routes using digital twins to avoid disruption.

Step 4: Feedback & Learning: 

After delivery, agents perform post-mortems:

• Did the supplier deliver on time?
• Were quality standards met?
• How did consumer sentiment shift?

Feedback loops help the system get smarter over time. They can change how suppliers are ranked, kick off new pricing discussions, or even retrain the negotiation models themselves. Starbucks’ agents actually learn how to make better decisions the next time around.

In a broader agentic network, these systems might start rating each other, building a kind of reputation system machines can read. A logistics agent that performs well in one ecosystem could earn priority status in another.

Strategic Implications for Business Leaders

This isn’t just a Starbucks story. Every company, especially those with sprawling supply chains, needs to start thinking about how they show up in a machine-mediated world. Agentic systems change the rules. They don’t wait for a quarterly review to optimize. They’re making decisions on the fly, in real time. That means your business needs to be ready for agents to see it, understand it, and trust it.

To participate in this new ecosystem, companies will need to add “agent surfaces” across their operations. That could mean installing IoT sensors in warehouses, using mobile apps that track demand signals, or deploying wearables that flag issues on the factory floor. These inputs allow agents to monitor what’s happening and decide on a course of action. 

On these agent surfaces, metadata becomes critical. If your packaging specs or delivery performance aren’t structured in a way agents can understand, you won’t even make it into the shortlist. Imagine two suppliers. One offers verifiable sustainability and performance metrics through a clean API feed. The other sends a PDF. Which one would you select?

Agentic Buying is not just another feature or tech upgrade. It represents a shift in how commerce is coordinated. It creates a new layer of intelligence that spans the business, constantly observing, deciding, and acting. And in this new environment, success depends on whether agents can find you, trust your data, and choose you over the competition.