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Sara Ali
16 Sept 2025
What if the next purchase order could write itself, verify prices, make sure there were adequate supplies, bargain within certain parameters, place the order, and leave a clear audit trail without anybody ever asking?
When it comes to B2B eCommerce, these Agentic AI agents can understand things like brand preferences, product requirements, and company budgets. They can also do things like search for products, compare them, negotiate prices, place orders, and even ship things back on their own.
They are virtual procurement assistants who work around the clock and continuously learn from data. This technology promises a new era where the agent "on our behalf" can predict what we need, suggest things that are just right for us, and buy supplies or equipment whenever we need them.
In practice, agentic commerce agents are like supercharged digital assistants for procurement. They differ from basic bots in that they plan and execute tasks on their own. For example, Salesforce CEO Marc Benioff explains that agents will work side-by-side with humans and can even “negotiate with other agents on our behalf”.
The key is agency – the ability to take independent action within defined guardrails. As Mirakl puts it, agentic systems “don’t just recommend; they act independently, on behalf of humans, within defined parameters”.
Amazon’s pilot “Buy for Me” checkout is an early example: an AI agent completes an entire purchase from a brand’s site with no manual review, signaling that Agentic Commerce is moving from theory into reality.
Agentic Commerce in B2B eCommerce Today: Right now, most deployments are still experimental or limited to pilots. Early examples include AI helpers that answer product questions, trigger routine reorders, or suggest upsell bundles.
For instance, a B2B buyer might have an assistant who auto-generates a purchase order when inventory dips. Vendors like Reveation Labs (reveation.io) already market solutions to automate multi-step workflows. In our vision, we believe intelligent AI agents can handle multi-step tasks, freeing you to focus on strategy.
Market Adoption: AI is now a top priority for retailers and suppliers. Salesforce’s Connected Shoppers report finds that 43% of retailers are already piloting autonomous (agentic) AI and 53% are evaluating its use.
Only about 11% of companies in a recent survey had no AI plans for 2025. Even in B2B, legacy manufacturers and distributors are experimenting with smarter chatbots and predictive analytics as stepping stones to full agents.
Industry data shows B2B companies still lag B2C in AI adoption – only 31% of B2B firms were “achievers” deploying advanced AI, compared to 41% of B2C – but nearly all expect to invest soon.
Leading B2B Platforms: Major commerce platforms are cautiously integrating agentic features:
Taking a conservative stance. In mid-2025, Shopify began inserting code comments forbidding unauthorized “buy-for-me” bots that check out without review.
(Developers are directed to use Shopify’s official Checkout Kit for any AI-driven purchases.)
Shopify does have AI initiatives (like its OpenAI partnership), but for now, it blocks fully autonomous transactions.
Actively building agentic tools. In 2025, Adobe launched the Experience Platform Agent Orchestrator to help businesses create and manage AI agents across Adobe and partner ecosystems.
Adobe is pushing AI personalization and commerce assistants on Magento, and third parties are adding agentic plug-ins, showing Adobe is moving toward more autonomous features.
Embraced agentic agents heavily. At Dreamforce 2024, Salesforce unveiled Commerce Cloud Agentforce, introducing specialized agents for merchants and buyers that can autonomously run tasks.
For example, Salesforce’s Buyer Agent can find products, auto-reorder items at pre-negotiated prices, track orders, and handle “Where Is My Order?” queries without human help.
Salesforce research (via Gartner) indicates roughly 33% of enterprises will use agentic AI by 2028 (up from <1% today), implying rapid growth.
Integrating with its broad AI strategy. SAP’s Joule AI agents target enterprise workflows. SAP demos include sourcing agents that tailor procurement events and select optimal suppliers, and commerce agents that guide shoppers and auto-enrich catalogs.
Since many SAP customers use it for B2B order management, Joule’s autonomous bots are expected to play roles in B2B buying (e.g., auto-replenishment, personalized storefronts).
Composable platforms and API-first solutions, such as Kibo, BigCommerce, and Oracle CX, are being designed to be open.
Logicbroker notes that over 60% of commerce platforms plan multi-agent integrations by 2026, so exposing product/order APIs (e.g., via Model Context Protocol) is key.
Even marketplace giants are exploring agents: Amazon Business’s “Buy for Me” pilot brings agentic shopping to wholesale buyers. In sum, agentic commerce is entering mainstream platform roadmaps.
Even today, certain B2B industries are experimenting with agentic ideas:
Firms with huge catalogs of parts or MRO goods benefit from auto-reordering. An AI agent could watch an assembly line’s usage and reorder belts, lubricants, or fasteners just in time.
Cloudfy highlights that large distributors (tight margins, complex SKUs) see AI agents as ways to “anticipate customer needs, streamline procurement, and reduce friction”.
For example, if a machine part count falls below a threshold, the agent can generate a draft purchase order for approval, cutting weeks of manual follow-up.
Hospitals and healthcare systems must never run out of critical supplies. Early tests show AI agents can proactively refill PPE, medications, or surgical kits based on usage patterns.
Cloudfy notes this space is emerging because “compliance, availability, and repeat purchasing are business critical” in medical supply chains.
An agent could ensure only approved items are ordered, check regulatory lists, and negotiate delivery to avoid shortages.
Distributors serving both business clients and consumer shops can leverage agentic personalization.
For instance, an electronics distributor might deploy an AI ‘shopping agent’ that answers technical questions and places complex orders via chat.
Cloudfy points out that hybrid B2B/B2C retailers “benefit from AI that adapts the experience” to each buyer type.
A parts buyer’s agent might automatically apply volume discounts and confirm credit terms, while a retail buyer’s agent focuses on sales promotions and quick checkout.
In all of these situations, agentic systems aid with everyday chores like quickly creating RFQs, comparing vendors, and completing orders. They take busy work off the hands of procurement teams and cut down on mistakes.
As Cloudfy summarizes, agentic AI “takes ownership of routine decision-making to remove friction and unlock efficiency”.
Today's buyers already see the promise of “24/7 procurement assistants” that learn their needs and act accordingly.
Looking ahead, analysts envision a dramatic expansion of agentic commerce by 2030:
We’ll move toward self-driving source-to-pay. AI agents will handle everything from need identification to payment.
DragonSourcing predicts that by 2030, “leading enterprises will operate digital procurement intelligence ecosystems where AI agents, suppliers, and buyers collaborate seamlessly”.
We might see “self-healing” supply chains that automatically reroute orders around disruptions.
In fact, Jaggaer notes trials for fully autonomous sourcing are already underway, with first real-world deployments possible by 2025.
By decade’s end, a single orchestration agent could coordinate specialized buyer, sourcing, and evaluation agents across the workflow, massively speeding up and digitalizing procurement.
Agents will become expert negotiators. Imagine buyer and supplier agents haggling over price, delivery, or contract terms in real time.
Agents will leverage analytics – forecast demand, calculate currency risks, factor in ESG criteria – to craft hyper-tailored contracts.
Salesforce envisions agents and humans working together to create “hyper-personalized, high-converting” deals.
As Benioff notes, next-gen agents will not just follow scripts but “make decisions and even negotiate with other agents on our behalf”.
By 2030, it’s likely that common contract terms (pricing tiers, volume discounts) will be continuously optimized by AI agents based on live data, maximizing value for both parties.
The ecosystem will become highly connected. New standards like Google’s Agent-to-Agent (A2A) protocol and the Model Context Protocol (MCP) are emerging to allow agents from different companies to communicate.
Today’s systems are siloed, but by 2030, open protocols should let agents “talk” across platforms. Research shows that over 60% of commerce platforms plan multi-agent integrations by 2026.
In practice, a buyer’s agent could discover a supplier’s pricing agent on another network, query it for quotes, and negotiate – all via standardized A2A messages.
Logicbroker emphasizes that companies must expose their catalog and order APIs (for example via MCP servers) so external agents can understand and act on their data.
This open “agentic mesh” will prevent vendor lock-in by letting a business use best-of-breed agents regardless of platform.
Ultimately, commerce transactions may be completed directly between AIs. By 2030, it’s estimated that about 25% of eCommerce (roughly $500 billion per year) could be processed by AI agents.
That means your procurement agent might autonomously locate a supplier agent, agree on a price, and pay digitally – no human click needed.
Achieving this will require robust trust frameworks (digital certificates, tokenized payments) so buyer agents can safely transact with unknown supplier agents.
It also demands machine-readable data: product specifications, inventory levels and prices must be readily parsed by AI.
The trend will blur lines between marketplaces and direct commerce, as agents hop between networks to find optimal deals.
Agents will constantly ingest live marketplace data. DragonSourcing forecasts “marketplace intelligence dynamically feeding AI agents real-time sourcing opportunities”.
Agents will monitor price indexes, shipping lead-times, supplier ratings and even geopolitical news. For example, if tariffs suddenly rise on a component, a buyer agent might switch to a local approved vendor immediately. Agents will also use common intelligence networks.
For example, if one company finds a new supplier that meets your needs, your agent may automatically analyze it. This data-driven flow will make hyper-personalized sourcing possible.
Each company's agents will consider things like cultural preferences, budget limits, and environmental goals when they work with suppliers.
Agentic commerce promises major impacts across operations and business outcomes:
Efficiency & Cost Savings: Agents can execute multi-step workflows in seconds. DragonSourcing notes AI agents will process RFQs, switch suppliers, or reroute orders almost instantaneously to respond to disruptions in real time.
By constantly analyzing costs, inventory levels, and logistics, agents will auto-select the most cost-effective options – often finding savings a human would miss.
Early trials already show faster replenishment and reduced downtime (e.g., being able to quickly find a new supplier if a shipment is late).
Overall, businesses will experience big increases in speed and cheaper prices for buying things as agents do the hard work.
Autonomous agents can proactively detect risks and enforce rules. They will monitor global signals (supplier financial health, geopolitical events, ESG risks) and adjust plans before crises hit.
DragonSourcing explains that autonomous procurement “detects risks – from supplier insolvency to regional instability – and takes preventive action”, reducing downtime and protecting continuity.
Agents can be set up with compliance guardrails (such as only working with recognized vendors, staying within budget, and meeting diversity standards) and keep extensive logs for audits.
For instance, if a supplier's items don't meet a certain standard, the agent can rapidly send orders to a backup that has been approved.
This predictive, rule-based monitoring will make sure that laws and regulations are followed and will cut down on disruptions by a huge amount.
For B2B buyers, the buying experience will become smoother. Agents provide instant, personalized support – answering technical questions, recommending products, checking availability, and even completing the checkout.
Salesforce expects that agents will “anticipate needs” and help create “hyper-personalized, high-converting shopping experiences”.
In one survey, 92% of shoppers who used AI assistants said their experience was better in one survey. This means that retailers will have more loyal customers and higher conversion rates.
Agentic features will also open up new ways to make money, such as automated replenishment subscriptions, AI-suggested targeted cross-sell packages, and micro-transactions on digital marketplaces.
Agents will handle basic sourcing and ordering, which will free up procurement and sales professionals to work on more important responsibilities.
DragonSourcing argues agents won’t replace people but augment them. Buyers will become “value architects” who set goals and strategies while supervising agents, rather than placing each order manually.
Staff will focus on exceptions (e.g., negotiating special contracts flagged by the AI) and on high-value relationship management.
Companies say that this change can help attract tech-savvy workers, as modern AI tools often inspire enthusiasm for their work. In general, jobs will change: there will be fewer clerical activities that are prone to mistakes and more analysis, creativity, and decision-making by people.
Every time you talk to an AI bot, it will leave behind a lot of data. Companies can see everything that happens with each inquiry, decision, and transaction.
Agents will learn from each order to make better predictions and tailor their services to each customer.
As time goes on, this "living data" will make pricing algorithms, demand planning, and risk models better.
Companies will learn more about how customers act, the best ways to place orders, and the weak points in their supply chains. This will help them make better plans for the future.
In a way, agentic commerce is a virtuous cycle: agents constantly send data back to the system while they work, which makes both the AI and the business smarter.
Despite the promise, several challenges must be addressed:
B2B systems are notoriously siloed (ERP, CRM, legacy portals). Agentic AI is only as good as its data. DragonSourcing cautions that AI “is only as good as the data it ingests”. In practice, that means product catalogs, pricing lists, and inventory data must be cleaned, unified, and machine-readable.
Many firms with custom SKUs and complex contracts will need to overhaul data feeds. Agents may have a hard time until catalogs and orders are made available through strong APIs or the new Model Context Protocol.
Bridging these integrations is a top priority; otherwise, agents won't be able to view the whole picture because of old hurdles.
Granting AI agents purchasing power introduces risk. A rogue agent or stolen credentials could place fraudulent orders.
Companies must tightly secure agent identities and communications (e.g. strong authentication, encryption). Agents should use tokenized payments and one-time tokens for transactions.
There is also a chance that "adversarial agents" or bogus sites will trick procurement bots. In high-stakes B2B, an autonomous mistake, such as ordering things that aren't allowed, could get around regular restrictions.
To stop exploitation, strong cybersecurity and multi-factor permission for big transactions will be very important.
Businesses won’t trust black-box bots easily. According to Salesforce/Emarketer data, 81% of retailers say they would only trust AI to act autonomously if adequate guardrails are in place.
That means every agent decision needs an audit trail and explainability. Users must be able to see why an agent chose a supplier or price. Firms may require manual review for significant purchases at first.
Clear policies about agent limits and human oversight will be critical. Until agents prove reliable, many organizations will keep a human in the loop for high-value actions.
AI agents could inadvertently embed biases. For example, an agent trained on past data might favor incumbent suppliers or overlook minority-owned businesses. DragonSourcing places a lot of focus on buying things.
AI selections must be "clear, explainable, and free of bias." Companies will have to make sure that agents rate suppliers fairly based on their merits. If they don't, they could face pressure or even laws.
This might mean making sure that AI models aren't biased and making explicit what the standards are for diversity and sustainability.
Ethical frameworks to guide agent decision-making will become a significant governance concern.
The agent ecosystem is still not fully connected. Without industry standards, each vendor's agent works on its own.
The future depends on open protocols (A2A, MCP) and data standards. As Logicbroker notes, merchants must expose product/order APIs “in ways that agents can understand”.
Until these frameworks mature, companies risk building agentic capabilities that only talk within one platform.
Industry consortia and tech alliances will need to push unified formats so that an agent can seamlessly transact across different networks.
Automated commerce introduces novel legal issues. Who is liable if an agent signs a bad contract or violates trade rules? Data privacy laws may apply to what information agents collect on buyers.
Compliance becomes trickier when decisions are automated. Companies will need to monitor evolving regulations around AI in procurement.
Likely, we will see guidelines on agent accountability (e.g., requiring a human “approval authority” on contracts) and on digital signatures by AI.
Even as agentic commerce unfolds, B2B firms can start laying groundwork today:
Clean up your product, pricing, and inventory data now. Adopt an API-first commerce platform (headless or composable) so AI can access records.
As experts advise, expose your catalog and order APIs (for example, via an MCP server) so that agents “can understand, use, and act” on your offerings.
The goal is machine-readable SKUs, live inventory feeds, and standardized attributes. With good data in place, agents will be able to accurately ingest and act on your information.
Unlock the future of digital trade with B2B eCommerce Consulting, guiding businesses to adopt agentic commerce strategies that bridge today’s practices with 2030 innovations.
Start small with narrow use-cases to gain confidence. For example, you could make a simple chatbot to answer common inquiries from customers or an automatic reorder assistant for a single line of products.
Bots that follow rules might also be useful. To establish the case for the business, keep track of things like how much time is saved, how many mistakes are made, and how happy users are.
Check out Kibo's method: they have a "Shopper Agent" who can answer questions about products, add items to the cart, and handle returns.
Research on early adopters shows that organizations learn better when they do things in stages, like starting with a buyer-side agent and then adding more. Reveation Labs even provides modular agent tools to simplify early adoption.
Prepare for interoperability by supporting emerging standards. Investigate platforms that plan for Agent-to-Agent (A2A) communication or that integrate with external AI services.
Keep an eye on Google’s and others’ developments in A2A/MCP.
Even if you’re not ready to link systems across companies yet, designing your architecture with these in mind (open data models, API-first workflows) will prevent vendor lock-in later.
The key is a “one-to-many” strategy so your agents can connect to any supplier’s agent in the future.
Define clear rules for your agents. Determine what autonomy they have (e.g. spending caps, approved vendor lists).
Set up human-in-the-loop checkpoints for big decisions. Invest in AI explainability tools so humans can audit an agent’s reasoning.
(Shopify’s recent code change is a cautionary tale; they will soon require proof that any AI “buy-for-me” flow includes a proper human review step.)
Train your procurement and IT teams on how to monitor and override agents. Good governance – knowing what your agents shouldn’t do – will build trust in these systems.
Bolt down your e-commerce security. Encrypt all agent communications and use secure token exchanges for payments.
Plan for strong authentication (e.g., device or certificate-based) on agent credentials. Work with legal to outline liability for agent-initiated orders (For example, clauses about wrong shipments).
Make sure your contracts with trading partners clearly include transactions that use AI. As with any new technology, you need to be careful.
Do penetration testing on agentic flows and make sure that agents follow the same trade rules as people.
Keep up with what's new in agentic AI. The top IT companies, including SAP's Joule, Salesforce's Einstein/Agentforce, and Adobe's AI platform, are changing quickly.
For instance, SAP keeps adding new Joule Agent skills, such as helping shoppers find what they want and adding to the catalog. Salesforce is also adding to its Einstein features.
When it's possible, think about working with these vendors or AI companies, like Reveation Labs. Also keep an eye on working groups and consortia that set standards.
Getting in touch with the proper AI partners early on will offer you an edge.
As businesses move toward intelligent automation, choosing the right B2B eCommerce solutions becomes essential to support agentic commerce systems that drive scalability and smarter decision-making.
Prepare your organization culturally. Emphasize that agents are assistants, not replacements.
Give teams training so they know how to set goals for agents and understand what those goals mean.
Tell them the good news: agents can undertake the boring stuff while staff work on more important things.
Leaders suggest a tiered roadmap that includes figuring out what you need, making a plan for your AI approach, testing modest solutions, training people, and making changes based on feedback.
Cloudfy says that the change to agentic commerce is not only technical but also conceptual. Platforms will go from being tools to being partners in business.
As this evolution unfolds, keep in mind: Agentic Commerce in B2B eCommerce is emerging fast. The companies that experiment and adapt early are likely to gain a major advantage.
As Cloudfy concludes, B2B commerce will soon be “building platforms that think, act, and adapt on behalf of users”.
The firms that act now will help set the pace in the coming decade of AI-driven B2B commerce.
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