Retail Frontier Firms are evolving their operating models to keep pace with increasingly dynamic markets, using AI to support more responsive and resilient decision-making and execution across commerce channels. Rather than improving individual functions in isolation, these organizations are rethinking how commerce operates end to end, enabling AI agents to work alongside people to support faster, more consistent outcomes across the business. This evolution is accelerating as retailers navigate rising customer expectations, sustained margin pressure, volatile demand, and ongoing labor constraints: conditions that benefit from decisions being made and executed more continuously.

In Retail Frontier Firms, AI capabilities are embedded where decisions and value are created: in stores, at the digital shelf, across merchandising, pricing, fulfillment, customer service, and checkout. AI agents interpret signals from customers, inventory, suppliers, and channels and help coordinate actions across the enterprise. This supports retailers as they respond to change with greater speed, consistency, and scale across touchpoints.

This operating model is enabled by agents that share context and operate cohesively across the retail ecosystem. At the core of agentic commerce is Model Context Protocol (MCP), which provides AI agents with access to a shared, enterprise-grade understanding of products, inventory, pricing, policies, and customer intent. By grounding agents in a common business context, MCP helps support aligned, governed, and consistent decision-making across channels and functions. The future of retail is increasingly shaped by a human and AI agent operating model, connected by shared context and open protocols.

• Model Context Protocol (MCP) unlocks hundreds of thousands of business functions for secure, real-time use by agents, developers, and applications. 
• Agent Communication Protocol (ACP) enables agents across merchandising, supply chain, store operations, and service to collaborate end to end, helping reduce fragmentation and align execution across functions.
• Payment and transaction agent protocols extend AI capabilities through checkout and settlement, supporting trusted, compliant transactions across in-store, online, and conversational commerce.

Together, these capabilities support a more outcome-driven operating model focused on availability, margin, conversion, service levels, and loyalty. Humans define strategy, priorities, and guardrails, while AI agents help orchestrate execution across day-to-day operations: supporting modern retail operations designed for Commerce Anywhere.

Emerging retail trends shaping the future of commerce

As consumer expectations continue to rise, shoppers increasingly demand seamless, continuous interactions where they move effortlessly from social-commerce discovery to mobile checkout, in-store pickup, curbside fulfillment, or voice-activated reordering. Frontier retail responds to this shift by dissolving the boundaries between channels and touchpoints, allowing commerce to adapt in real time to customer intent, location, and context. For brands, this means the ability to deliver frictionless, anticipatory commerce at scale by meeting customers wherever they are, with relevance and speed, without adding operational complexity.

The industry is rapidly shifting away from static, siloed channels toward autonomous, context-aware agents that orchestrate buying journeys seamlessly across stores, digital experiences, and conversational interfaces. Agents move beyond traditional personalization. They actively guide product discovery, shape contextual offers, negotiate availability, and coordinate fulfillment, helping to continuously optimize inventory, pricing, promotions, and supply-chain decisions behind the scenes. As personalization and automation become table stakes, agentic AI emerges as the strategic engine driving scalable growth and sustainable Commerce Anywhere.

Introducing Microsoft Dynamics 365 Commerce MCP Server

Agentic commerce introduces a new operating model in which AI agents collaborate through MCP, enabling continuous decision-making and coordinated execution across the retail value chain. The new Dynamics 365 Commerce MCP Server exposes core retail business logic including catalog, pricing, promotions, inventory, carts, orders, and fulfillment as MCP-enabled capabilities. Expected to be in preview in February 2026, this will allow retailers to build agentic commerce experiences where AI agents can securely discover, decide, and execute retail workflows across digital, physical, and conversational channels.

By combining the ERP, Analytics, and Commerce MCP servers, Dynamics 365 supports a more agent-driven operating model in which front-office experiences and back-office operations are connected and optimized, helping retailers operate with greater agility and readiness for Commerce Anywhere.

How retailers can begin adopting agentic commerce today

Retail leaders can begin moving toward agentic commerce by adopting AI agents in three practical ways:

1. Starting with agents embedded in Dynamics 365
2. Extending capabilities through custom-built agents using MCP
3. Leveraging partner-built agents across the broader retail ecosystem

Together, these approaches allow retailers to progress at their own pace while aligning agent adoption to their operating model, business priorities, and maturity.

1. Start with agents embedded in Dynamics 365

Purpose-built agents are designed to address common retail challenges and operational friction points. Dynamics 365 agents and retail industry agents can be embedded directly into core business processes, allowing teams to realize value quickly.

Microsoft retail industry agents, like the Catalog Enrichment Agent and Personalized Shopping Agent are examples of vertical-specific agents designed around retail data models, workflows, and decision patterns that support scenarios like product discovery, assortment accuracy, and personalized engagement without requiring custom development.

Today, in Dynamics 365 the Supplier Communications Agent is a good example of embedded agents in action. Retailers can proactively monitor supply signals and engage suppliers in real time to confirm availability, align delivery timelines, and respond to changes earlier. This supports faster coordination, fewer surprises, and more reliable execution at scale.

2. Build custom agents using MCP

Retail operations are shaped by business logic that is unique to each organization: driven by merchandising strategies, store formats, service models, and supply-chain constraints. Microsoft Copilot Studio enables retailers to build custom AI agents that encode their own rules across replenishment, allocation, fulfillment, and store execution, aligning agent behavior directly to how the business operates.

These custom agents can operate across planning and selling in the flow of work using MCP-powered access to enterprise systems. Inside Microsoft Teams, Merchandising Managers and Planners can collaborate in real time with agents that access products, demand forecasts, supplier relationships, inventory, and pricing through Dynamics 365 ERP MCP.

On the selling side, through the Dynamics 365 Commerce MCP Server, your custom agents can extend intelligence into customer experiences. Agents can discover products, personalize offers, assess availability, reserve inventory, and complete transactions across digital, physical, and conversational channels while operating with a unified view of pricing, promotions, and fulfillment.

3. Extend agentic commerce through partners and the ecosystem

Retailers can further accelerate agent adoption by leveraging partner-built agents designed for specific retail scenarios and industries. Commerce MCP enables software development companies and system integrators to build agents more quickly by reducing integration overhead, standardizing access to retail data, and maintaining enterprise-grade trust and compliance.

Early partner solutions already demonstrate the breadth of what’s possible, from store associate productivity and clienteling to conversational commerce and business-to-business (B2B) buying experiences, including:

• Amicis: The Store Commerce Agent is a voice-first, screen-aware assistant designed for in-the-moment store execution. It can help associates complete high-friction tasks like returns, exchanges, order lookups, and policy checks in Dynamics 365 Commerce using natural voice commands, while adapting to what’s on the POS screen.
• Evenica: The B2B Licensee Product Request Agent uses conversational AI and image recognition to support licensees in finding beverage products. When a product is not available in the catalog, the agent can create a request case to support the product intake process.
• Argano: The Retail Clienteling Agent offers a conversational clienteling experience by bringing together customer insights, product data, and agentic AI into a single, governed workflow. It helps retail associates improve customer relationships by delivering personalized, brand-aligned interactions before, during, and after in-store appointments.
• Sunrise: The Commerce Companion is a suite of retail agents that help simplify everyday store operations across inventory and fulfillment to purchasing and store processes. Using natural language, it is designed to deliver fast, accurate answers and guided actions, which can enable associates to serve customers efficiently while keeping operations moving smoothly.
• Visionet: FashionGPT Agent can turn natural-language shopping intent into real-time retail execution across product, pricing, inventory, and promotions. It drives the end-to-end shopping journey and help turn conversations into measurable actions across channels.

Together, embedded agents, custom-built agents, and partner solutions give retailers flexible entry points into agentic commerce supporting near-term impact while laying the foundation for a more adaptive, AI-enabled operating model across Commerce Anywhere.

Agentic retail with Dynamics 365 in action at NRF 2026

At NRF, we will demonstrate how Dynamics 365 works with Copilot and agentic capabilities to support Commerce Anywhere and more efficient, end-to-end retail operations. We will share examples of how retailers are using Dynamics 365 to evolve their operating models and advance Frontier Firm capabilities.

Visit us during NRF expo hours at Level 3, Booth 4503, and join the related theater sessions at our booth:

• Beyond the Boutique: How Frette Uses AI to Transform Store Experience
  January 11, 2026 (Sunday), 2:00 PM ET
  Session led by Sunrise Technologies
• Reimagine retail business processes with Agentic ERP
  January 13, 2026 (Tuesday), 2:30 PM ET

The future of retail belongs to frontier organizations that can sense, decide, and act in real time. With agentic commerce enabled by Dynamics 365, retailers gain the foundation to move faster with confidence, aligning strategy, execution, and customer experience through intelligent agents that operate seamlessly across every channel. We look forward to connecting with you in New York and exploring how agentic business applications in Dynamics 365 can support your next step forward.

The post Agentic AI in retail: How Dynamics 365 powers Commerce Anywhere appeared first on Microsoft Dynamics 365 Blog.

In October 2025, we announced the general availability of the Sales Qualification Agent (SQA) in Dynamics 365 Sales—a breakthrough in autonomous lead qualification. Sales Qualification Agent empowers sellers by helping build higher quality opportunity while eliminating tedious, repetitive work. Sales Qualification Agent autonomously researches every lead, initiates personalized outreach, and engages prospects to understand purchase intent, ensuring that sellers spend their time meeting prospects who are ready to take the next step. With modes enabling both seller-driven and fully autonomous qualification, the agent supports a key goal for sales organizations—increasing revenue per seller.

Customers are using Sales Qualification Agent in two ways: 

1. Helping boost revenue beyond current sales capacity
   • Responding to inbound leads within minutes instead of days, increasing response rates and in turn, qualified opportunities.
   • Engaging leads that sellers are unable to follow up on due to capacity constraints, or those deemed economically unviable to pursue.
   • Increasing pipeline quality by focusing the seller’s time on a handful of high intent, engaged leads recommended by the agent.
2. Helping reduce sales costs
   • Reducing back-office costs related to lead research and validation, using Sales Qualification Agent in “Research only” mode to hand-off only the leads that meet the ideal customer profile criteria.
   • Automatically disqualifying low-quality leads, saving hours of seller time during the week.

Continuing benchmarking the quality of sales AI agents

Microsoft is building the future of agentic Sales technology with prebuilt AI agents, such as Sales Qualification Agent, the Sales Research Agent, and the Sales Close Agent available in Dynamics 365.

At Microsoft, we’re committed to delivering quality, trust, and transparency with our agents, and that requires rigorous evaluation. As we continue to build new agents and improve existing ones for critical sales workflows, evaluation benchmarks provide a structured and transparent way for our customers to measure quality for the jobs the agent does.

Today, we’re announcing the Microsoft Sales Bench—a new collection of evaluation benchmarks designed to assess the performance of AI-powered sales agents across real-world scenarios. This framework brings together purpose-built metrics, hundreds of sales-specific scenarios, and composite scoring validated by both human and AI judges.

The Sales Bench isn’t starting from scratch. It now formalizes and expands what began with the Sales Research Bench, published on October 21, 2025, which evaluates how AI solutions answer business research questions for sales leaders.

Today, we’re extending the Microsoft Sales Bench with a second benchmark: the Microsoft Sales Qualification Bench, focused on measuring how effectively AI agents qualify leads and generate high-quality pipeline.

Introducing the Sales Qualification Bench for lead qualification

This Microsoft Sales Qualification Bench evolved from rigorous evaluations we conducted since the Sales Qualification Agent’s public preview in April, with the goal of objectively measuring quality as we further developed the agent, partnering with customers from a diverse set of industries. Since the preview, we measured every update against these standards, ensuring improvements are real and repeatable.

We generated a synthetic dataset modeled after companies from three different industries, with 300 leads, with attributes such as name, company, and email ID—representative of what sales teams typically work with before any enrichment or hygiene is performed. In addition to these typical attributes, we also added key knowledge inputs such as value proposition of the products being sold, customer case studies, and documentation for answering customer questions.

In addition to Sales Qualification Agent, we used the evaluation framework to measure ChatGPT by OpenAI on the same dataset. Since we didn’t have access to an autonomous agent from OpenAI, we mimicked how a human seller would use ChatGPT to recreate the three key jobs SQA performs. We provided each system—Sales Qualification Agent and ChatGPT—the exact same lead inputs, knowledge sources, and contextual signals under controlled evaluation configurations. We used a ChatGPT Pro license with GPT-4.1. This model is the closest match (and slightly better) to Sales Qualification Agent’s GPT-4.1 mini, which we intentionally chose to deliver optimal quality at lower cost per lead than newer models. Additionally, Pro license was chosen to optimize for quality: ChatGPT’s pricing page describes Pro as “full access to the best of ChatGPT.”¹

The framework evaluates outputs from the three jobs across Sales Qualification Agent and ChatGPT:

• Research: Company research for the lead—background, strategic priorities, financial health, and latest news.
• Outreach: A personalized email generated based on research, to make initial contact with the lead.
• Engagement: The agent’s conversation with a lead until it’s qualified or dispositioned.

Our scoring metrics span core quality (accuracy, relevance, completeness), trustworthiness (grounding and citations), and business-specific success criteria (e.g., relevancy of company research to highlight interest in the seller’s offerings, personalization of the initial outreach emails sent to catch the lead’s attention, accuracy of responses to the lead’s questions to drive purchase intent, and the timing of handoff to a seller when the lead is ready to engage).

Outputs were scored independently by both human reviewers and an LLM judge built with GPT-5.1, using a 1–10 scale for each metric. These metric-specific scores were then rolled up using a simple average to produce a composite quality score. The result is a rigorous benchmark presenting a composite score and dimension-specific scores to reveal where agents excel or need improvement. Our methodology, metrics, and their definitions are described in this technical blog.

Results

In evaluations completed on December 4, 2025, using the Sales Qualification Bench, Sales Qualification Agent outperformed ChatGPT on each of the three jobs required for sales qualification:

1. Research: The Sales Qualification Agent outperformed ChatGPT with 6% higher aggregate scores, leading on relevancy and completeness in research results that highlighted the lead company’s interest in the seller’s offerings.
2. Outreach: Sales Qualification Agent demonstrated 20% better results compared to ChatGPT, generating email drafts with accurate personalization and mentions of relevant recent events that will resonate with the lead.
3. Engagement: Sales Qualification Agent’s email responses to engage a lead over a multi-turn conversation scored 16% higher than ChatGPT’s. SQA generated emails that responded to the lead’s questions with accurate answers that develop their purchase interest and with precise discovery questions that qualify the lead before handing off to a seller.

In addition to performing better on these metrics, Sales Qualification Agent has the ability to run autonomously, which can help significantly reduce the time spent generating pipeline while helping sales teams build better quality pipeline.

Sales Qualification Agent scores well on these three jobs as its optimized for sales-specific scenarios and uses the following techniques to get great results:

1. It uses agentic Retrieval Augmented Generation (RAG) to relentlessly research each lead, ensuring greater completeness. More on this in the following section.
2. With knowledge of what the company sells, it can contextualize every workflow to increase relevancy for both the seller and the lead.
3. It can retrieve organizational knowledge from attached documents and internal repositories like SharePoint with greater precision, boosting accuracy of its responses when engaging with the lead.

The technical blog details which metrics SQA excels at relative to ChatGPT, where it falls short, and why.

Translating evals to real-world impact

Running evals led to major Sales Qualification Agent improvements during its six-month preview. Early results prompted us to try agentic AI design patterns, especially agentic RAG, which improved our company research by allowing iterative web searches and real-time reasoning. They also led us to enhance data coverage by auto-linking existing CRM records to each lead and inferring company names from lead emails. These updates provided sellers with deeper insights, revealing strategic opportunities and risks beyond basic facts.

For instance, when researching leads for a security company, Sales Qualification Agent can link news on recent cyberattacks to increased demand for its software. As highlighted in the technical blog, research synthesized by the agent makes such inferences more consistently than ChatGPT. Enhancing the agent’s research also improved the relevance and personalization of outreach emails, helping agents better engage leads and clarify their ability and intent to purchase before handing them off to sellers.

Sandvik Coromant, a leader in precision cutting tools, partnered with us to pilot Sales Qualification Agent for their Digital Commerce program. After the updates, Pia Cedendahl, Global Sales Manager for Strategic Channels/Partners and Online Sales, noted, “Sales Qualification Agent’s answers became far more on-point to our business—it’s like having a research assistant that already understands what we care about.” Sandvik Coromant saw improved lead conversion and higher engagement from their Digital Account Managers, validating the impact of our evaluation-driven approach. Pia joined Microsoft leaders at the Microsoft Ignite 2025 session, “Accelerate revenue and seller productivity with agentic CRM,” where she shared how the team saved more than 120 hours and $19,000 in just the first three weeks since launching a pilot, and forecasted a 5% increase in revenue with full rollout.

Better insights, more personalization, proven value

Equipped with agentic AI design and backed by data-driven evaluation, customers can confidently use the Sales Qualification Agents so that:

• Sellers receive comprehensive company overviews, timely news highlights, and actionable recommendations that are consistently delivered with high quality—drawing a clear line from insight to action.
• Sales leaders can expand their qualified pipeline cost efficiently, with the agent ensuring high lead quality.
• Prospects benefit from more personalized outreach, enhancing their experience and supporting increased conversion rates.

What’s next

We’ll continue to refine Sales Qualification Agent using agentic design patterns, aiming to make every improvement measurable and meaningful. Stay tuned for the full evaluation results and methodology for the Sales Qualification Bench, which will be published for transparency and reproducibility. We also intend to add more evaluation frameworks and benchmarks to the Microsoft Sales Bench collection including benchmarks that cover future sales agent capabilities.

¹ChatGPT pricing page, accessed November 24, 2025

The post Dynamics 365 sets the bar for agentic sales qualification on new benchmark appeared first on Microsoft Dynamics 365 Blog.