How agentic AI in commerce is reshaping platforms, ads, and partnerships

Agentic AI in commerce is moving from lab demos to live systems that buy, sell, advertise, and assist customers. This shift affects platforms, merchants, and regulators. Therefore business leaders must rethink risk, partnerships, and product strategy now. In the paragraphs that follow, I unpack five fast-moving stories that show why agentic AI in commerce matters today, what it breaks, and what it can build next.

## Agentic AI in commerce: Platform ad risk and the Meta wake-up call

A startling report showed that Meta is earning billions from ads tied to scams, and internal documents reveal the company is aware of the problem. However, instead of simply removing bad actors, Meta has used penalties like higher ad rates for some scammers, according to Reuters coverage cited by Marketing Dive. Therefore platforms are finding that scale creates incentives that can lock in problematic behavior. Platforms make money from advertising, yet they must simultaneously police fraud. This creates a difficult trade-off for boards and trust teams.

For platform partners and enterprise customers, that trade-off matters in two ways. First, brands that advertise on these networks may see their reputations exposed if their ads appear next to or are amplified by scam activity. Second, compliance and legal teams must factor platform policy and enforcement gaps into risk models. Consequently, advertisers and platforms must cooperate more closely on verification, reporting, and shared accountability.

Looking ahead, expect platforms to adopt more automated detection that links agentic behaviors with fraud flags. Additionally, regulators and advertisers will press for clearer transparency about ad revenue sources and enforcement actions. For enterprises, the impact is immediate: audit your ad channels, insist on enforcement metrics, and update vendor risk assessments to reflect agentic AI-driven ad dynamics.

Source: Marketing Dive

Agentic AI in commerce: Legal fault lines — Amazon vs. Perplexity

The legal clash between Amazon and Perplexity highlights a new frontier: who is accountable when an AI assistant acts like a buyer, seller, or agent. Initially a dispute over one assistant’s behavior, the case is turning into a test of rules for agentic AI in commerce. Therefore the outcome could define platform liability, content rules, and how AI assistants mediate transactions.

For enterprises that build or rely on agentic agents, the stakes are clear. If platforms are held strictly liable for agents’ actions, companies may need tighter controls, audits, and assurance that their agents follow legal and contractual boundaries. However, if liability shifts toward upstream model providers or implementers, contracts and vendor relationships will change. Either way, procurement, legal, and product teams must prepare for new contractual clauses, indemnities, and compliance checks.

This legal standoff also signals a strategic moment for enterprises. Vendors will compete on trust features: provenance, logging, reversible actions, and human-in-the-loop gates. Additionally, buyers of AI services should ask for explicit behavior controls and verifiable audit trails. Consequently, companies that fail to demand these capabilities risk exposure.

Finally, regulators will watch this case as a precedent. Therefore enterprises should engage proactively with policymakers and industry groups to shape realistic rules that allow innovation while protecting customers and commerce. The near-term outlook is legal uncertainty, but the mid-term outcome will create clearer standards that everyone can build to.

Source: Digital Commerce 360

Agentic AI in commerce: Enterprise partnerships race to scale agentic offerings

ServiceNow and NTT DATA recently expanded a strategic partnership to co-develop and sell agentic AI solutions worldwide. This move shows that large enterprises believe agentic AI in commerce can scale across complex business processes. Additionally, the partnership aims to embed agentic capabilities into platforms that handle service workflows, procurement, and customer operations. Therefore the playbook is clear: combine a software platform’s customer reach with a systems integrator’s implementation muscle.

For enterprise buyers, this trend has practical implications. First, packaged agentic solutions reduce time-to-value because they come with pre-built connectors, governance templates, and industry workflows. Second, customers can expect more turnkey services that promise productivity gains across large teams. However, this also increases dependency on vendor roadmaps and shared accountability models. Consequently, procurement and IT need to negotiate implementation SLAs, data handling terms, and upgrade paths.

From a market perspective, these partnerships signal a commercialization wave. Firms that previously piloted agents will now see partners offering production-grade solutions. Additionally, co-developed products tend to standardize best practices and compliance features, which reduces fragmentation. Therefore enterprises should update their vendor evaluation criteria to include partnership models, joint support frameworks, and clear plans for scaling agentic behavior safely.

In short, the partnership era for agentic AI in commerce will accelerate deployments. However, success will depend on clear contracts, shared governance, and a focus on measurable business outcomes.

Source: CX Today

Shopify leans into agentic AI in commerce and B2B growth

Shopify’s recent strategy update shows the company is positioning agentic AI in commerce as central to merchant operations. The company plans to make AI the backbone of how merchants sell and scale, with B2B commerce identified as a key growth area. Therefore merchants should expect tools that automate quoting, buying, inventory decisions, and personalized selling at scale.

For B2B sellers, agentic AI can reduce friction in large, complex deals. For example, agents can handle repetitive quote requests, check contract terms, and recommend cross-sell opportunities. Additionally, these agents can free human teams to focus on negotiation and relationships. However, B2B settings add complexity: contracts, custom pricing, and compliance rules must be encoded into agent behavior. Consequently, vendors and merchants must ensure agents respect business rules and provide auditable trails.

Shopify’s push also opens business model opportunities for partners. Agencies, app developers, and systems integrators can build specialized agentic modules for vertical workflows. Therefore companies that invest early in trustworthy, domain-aware agent features may capture market share as merchants upgrade their tech stacks.

Finally, operational readiness will matter more than ever. Merchants must audit data quality, product catalogs, and pricing logic before deploying agentic capabilities. Additionally, they should pilot agents on low-risk flows, measure ROI, and expand gradually. The outcome will likely be faster commerce workflows, but only if governance and merchant trust are prioritized.

Source: Digital Commerce 360

Snapchat, Perplexity, and the consumer turn of agentic agents

Snapchat plans to integrate Perplexity AI into its Chat experience, enabling users to ask questions directly in messaging. This move shows that consumer platforms are bringing agentic agents into everyday interactions. Consequently, agentic AI in commerce will not remain confined to enterprises; it will touch how consumers discover products, get recommendations, and make purchase decisions.

For marketers and platform partners, this matters in two ways. First, consumers will expect conversational discovery and instant assistance inside apps they already use. Therefore brands must adapt their creative and measurement strategies to agent-driven interactions. Second, brand safety and ad placement issues reappear here. If agents recommend products, platforms must ensure recommendations are accurate, transparent, and free from manipulation. Additionally, platforms and partners should clarify when an AI is acting autonomously versus providing sourced content.

This consumer integration also invites new commerce formats. For example, an agent could ask follow-up questions, refine product matches, and even initiate purchases. However, trust is essential: users must know how recommendations are generated and whether they are sponsored. Consequently, disclosure, provenance, and simple opt-outs will be important features.

In summary, embedding Perplexity into Snapchat signals a broader mainstreaming of agentic experiences. Therefore brands and platforms should prepare to meet users inside conversations while holding firm on transparency and safety.

Source: Marketing Dive

Final Reflection: Connecting the dots on agentic momentum

Across these stories, one theme is clear: agentic AI in commerce is maturing fast and touching every part of the ecosystem. Platforms face revenue-versus-risk decisions, courts may set liability precedents, enterprise partnerships are scaling production-ready solutions, commerce platforms are embedding agents into merchant workflows, and consumer apps are making agents mainstream. Therefore businesses must act on three fronts: manage risk through audit and governance, choose partners that offer proven controls, and experiment where clear customer value exists.

Looking forward, expect clearer contracts, richer vendor assurances, and more built-in transparency in agentic systems. Additionally, regulators and advertisers will push for accountability across advertising and recommendations. However, with careful design and partnerships, agentic AI in commerce can deliver faster operations, better discovery, and new revenue models. The next 12–24 months will decide which approaches become standard. Therefore start small, require auditability, and scale the agents that prove both valuable and trustworthy.