Agentic commerce

The next step from thinking to acting

A decade ago, data and automatization revolutionized the retail industry by enhancing experience on the isolated parts of the journey and reacting without a clear understanding of the context and anticipation of the needs. These capabilities were effective, but they didn’t change the core principles of how commerce operated.

In the next wave of intelligent retail, AI offers retailers new values: perception, reasoning and action. It means that brands can finally move beyond reactive systems toward autonomous commerce agents that results in fewer hand-offs, richer insights, faster resolution and, ultimately, adaptive, self-optimizing customer journeys. And we help brands experience each and every benefit of this transformation.

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Why now

What makes agentic systems transformative

A missing layer between intelligence and execution

By evaluating context, interpreting goals, hypothesizing multi-step actions, executing them through connected APIs and services, agentic AI systems mirror the human process of reasoning.

Continuous adaptation and improvement

Agentic models learn from every interaction and amplify results without manual control. It enables them to continuously refine recommendations and automate next steps.

Empowering both retailers and shoppers

Agentic commerce provides retailers with the ability to dynamically adjust offers and services, while customers receive an ever more accurate and contextual experience.

The next era of commerce

How to win with agentic commerce

Gartner forecasts that by 2028, 33% of enterprise software will incorporate agentic AI capabilities, up from less than 1% today, and that 15% of daily work decisions will be made autonomously by AI agents. The business case is equally compelling. According to McKinsey, implementing agentic AI could boost operational efficiency by up to 30%. The retailers that embrace this new mindset will be the ones to harness agentic AI not as a novelty, but as a durable advantage.

The true transformation lies in the shift from AI as a passive assistant to AI as an active, reliable agent. Agentic systems introduce intent modeling, task orchestration and continuous learning loops and to move the industry beyond multimodal perception. They mirror the human process of reasoning—evaluating context, interpreting goals, hypothesizing multi-step actions and executing them through connected APIs and services.

Reasoning & contextual understanding

Goal-oriented customer assistance

Adaptive merchandising & dynamic curation

Autonomous product & style advisor

Connected
service agents

Continual learning & optimization

Reasoning & contextual understanding

General-purpose models treat customer requests as keywords only.

Agentic systems use large reasoning models (LRMs) and structured memory graphs to interpret complex customer goals across text, voice and visual signals. These systems combine multimodal embeddings with symbolic reasoning, allowing them to infer intent beyond literal queries. This translates “help me find a look for an interview” into a contextual plan. By dynamically grounding responses in customer history, inventory data and brand tone, the agent delivers active decisions, instead of passive answers.

Goal-oriented customer assistance

Scripted chatbots can answer a question, but they cannot achieve the goal behind it.

Unlike scripted chatbots, agentic assistants plan and execute multi-step tasks such as finding, comparing and even purchasing items autonomously on the user’s behalf. They leverage reasoning chains and API orchestration layers to connect conversational output with actions like cart updates or returns. This transforms customer support and commerce flows from static interactions into ongoing, adaptive collaborations between human and AI.

Adaptive merchandising & dynamic curation

Traditional systems operate on narrow datasets and miss contextual cues.

Agentic merchandising systems reason over multiple data layers: inventory, seasonality, visual trends and consumer intent to curate experiences in real time. They employ generative recommendation models and multimodal RAG frameworks to synthesize insights across structured and unstructured data. This leads to a living storefront that adapts continuously to customer needs, emerging trends and contextual cues.

Autonomous product & style advisors

Scaling human-level styling and product guidance to every customer personally is challenging.

AI agents trained on multimodal embeddings (text, image, video) can interpret style, fit, preferences and aesthetic context the way a human stylist would. They use cross-modal similarity and reasoning engines to assemble complete outfits, accessories or product sets for specific occasions or moods. Over time, reinforcement learning fine-tunes these suggestions based on engagement metrics and user feedback, yielding ever more relevant advice.

Connected service agents

Rigid flowcharts can’t handle real customer complexity.

Agentic service systems unify customer support across various modalities (text, voice, image) and resolve issues through reasoning. They access real-time order, logistics and CRM data using API connectors, then reason about next-best actions in context. This reduces escalations, shortens resolution time and ensures support feels intelligent, empathetic and brand consistent.

Continual learning & optimization

Traditional systems operate in isolation without feedback-driven optimization.

Agentic systems maintain a persistent memory of interactions, using it to refine their understanding of both customers and operations. They employ reinforcement learning from human feedback (RLHF) and self-correcting reasoning loops to improve outcomes autonomously. For commerce, this creates a self-learning ecosystem where every transaction strengthens the intelligence of the next.

Technologies

Technologies that make it happen

Serve as the cognitive core of commerce agents, enabling multi-step reasoning, contextual understanding, and decision-making that mimic human judgment. They transform static interactions into dynamic, intent-driven dialogues that evolve in real time.

Power autonomous behavior by linking reasoning with action—allowing agents to plan, delegate and execute commerce workflows like personalized styling, order management and cross-channel coordination.

Ground reasoning in real-time business data such as inventory, product attributes and CRM insights, ensuring agents’ recommendations and actions are always relevant, verifiable and accurate.

Provide persistent context that allows agents to “remember” previous interactions, preferences and brand cues, building continuity across sessions and enabling more personal, anticipatory experiences.

Enable reasoning agents to act directly within enterprise systems—updating carts, checking stock, issuing returns—bridging intelligent intent with operational execution.

Feed voice, visual and behavioral signals into reasoning loops, giving agents a situational understanding of user intent and environment that supports truly adaptive interactions.

Allow agents to learn from every engagement, refining tone, accuracy and reasoning over time to deliver increasingly precise and emotionally aligned commerce experiences.

Create controlled environments for training reasoning behaviors, improving safety and reliability before deployment, ensuring agents can handle ambiguity, conflict and human nuance.

Define ethical, compliance and brand boundaries that anchor autonomous actions in trust and responsibility, ensuring every decision aligns with business principles and customer safety.

Support scalable, low-latency deployments of reasoning agents close to the customer from mobile to in-store, enabling instant responsiveness and privacy-preserving intelligence at the edge.

Reasoning-capable LLMs (e.g., GPT-4, Claude, Gemini Pro)

Agent frameworks (LangChain, AutoGen, ReAct, CrewAI)

Retrieval-augmented generation (RAG) Pipelines

Memory & context layers (Vector Databases + Knowledge Graphs)

Action orchestration & tool use APIs

Observation interfaces (Multimodal Sensors)

Reinforcement learning & feedback loops (RLHF, RLAIF)

Simulation & synthetic data generation

Governance & policy layers

Composable infrastructure (Microservices + Edge Reasoning)