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Agentic commerce

The next step from thinking to acting

Why now

What makes agentic systems transformative

A missing layer between intelligence and execution

Continuous adaptation
and improvement

Empowering both retailers
and shoppers

The next era of commerce

How to win with agentic commerce

Reasoning & contextual understanding

Goal-oriented customer assistance

Adaptive merchandising & dynamic curation

Autonomous product & style advisor

Connected
service agents

Continual learning & optimization

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)

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

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.

Agent frameworks (LangChain, AutoGen, ReAct, CrewAI)

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.

Retrieval-augmented generation (RAG) Pipelines

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.

Memory & context layers (Vector Databases + Knowledge Graphs)

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

Action orchestration & tool use APIs

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

Observation interfaces (Multimodal Sensors)

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

Reinforcement learning & feedback loops (RLHF, RLAIF)

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

Simulation & synthetic data generation

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

Governance & policy layers

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

Composable infrastructure (Microservices + Edge Reasoning)

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.

News & insights

AI & data engineering AI advisory
Article The 10x engineer reframed: How agentic systems unlock authentic acceleration
The ceiling on individual contribution In 1968, a study reported something that would echo through software culture…
NeirIPS
AI & data engineering
Research NeurIPS 2025 Best Paper Award: Why 1000-layer networks unlock new capabilities
Many researchers have scaled vision and language models using self-supervised learning techniques to generate successful gains….
bias in AI
AI & data engineering AI advisory
Article Mapping bias in AI: From Mercator to Machine Learning
I love The West Wing. Before post-COVID remote working, I thrived in walking around the office…
AI & data engineering
Research Solvd at NeurIPS 2025: Explainable AI (XAI) and Reinforcement Learning (RL) at scale
NeurIPS has long been the stage where foundational questions are challenged, new empirical frontiers are revealed…
Multimodal commerce
AI & data engineering Digital experience Retail & consumer goods
White paper Multimodal commerce: new era of online shopping
Humans are multimodal. Sight, hearing, smell, taste and touch help us perceive different types of information to explore the world…
Introducing Multimodal Commerce
AI & data engineering Retail & consumer goods
Article Introducing multimodal commerce: The next era of customer experience
From mobile-first to AI-first I’ve worked with hundreds of organizations over the last 20 years, and the…
AI & data engineering
Research GUIDE for incremental learning – Solvd at ECAI 2025
The European Conference on Artificial Intelligence (ECAI) lets Artificial Intelligence (AI) researchers and practitioners connect and…
AI & data engineering
Research Classifier-free Guidance with Adaptive Scaling  – Solvd at ECAI 2025 
Image generation using AI methods comes with an inherent bargain – the image delivered either strictly follows the prompt or comes…
AI & data engineering
Research Studying the particle collisions – Solvd at ECAI 2025 
The ECAI (European Conference on Artificial Intelligence) is a leading Artificial Intelligence (AI) event in Europe…

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