Agentic Retail – Building a Retail Planning Team of AI Agents
Fri Aug 08 2025
Author
Noah Xiao
TL;DR – Agentic Retail, in Brief
Agentic Retail is a new way of planning where AI agents actively manage tasks like pricing, forecasting, and ordering — not just analysing data, but taking action. These agents work together across functions, respond to live data, and support human planners with faster, smarter decisions.
Key Points:
- AI agents as retail planners, not just tools.
- AI agents as a self-sufficient system, combining their own intelligence, with data, tools, and human instructions and guardrails.
- Cross-function coordination across pricing, supply, and store ops.
- Humans stay in charge, with agents doing the heavy lifting.
Introduction
Retail - buy from suppliers, sell to customers - might seem simple, right? But as businesses grow and markets shift, complexity kicks in. Retailers face big questions: What products should we source? How many should we stock? How do we price them? Where should our stores be? How do we stand out from the competition?
Answering these takes serious time, resources, and planning. More importantly, these aren’t one-time decisions — they need constant attention, data, and coordination across teams.
Today, technology and data are playing a much bigger role in helping planners and managers make smarter, faster decisions. From forecasting demand to optimising promotions, digital tools are reducing the guesswork and improving accuracy.
One of the more interesting developments in this space is the rise of AI Agents— smart systems that don’t just analyse data, but also take goal-driven actions. Think of them as a network of digital assistants that constantly monitor the business, spot opportunities or risks, and use advanced tools to make various planning tasks. Agents also can talk to each other to ensure coherency across business functions. These agents can act on behalf of humans or support decision-makers with faster, more data-driven insights.
Retail business in a nut shell
What is Agentic Retail
Agentic Retail is an emerging model where AI agents act as intelligent planners across key retail functions — from demand forecasting to promotions and assortment planning. These agents continuously monitor the business using data such as inventory, transactions, product range, pricing, and market trends.
Trained with specific goals and rules, they can take action using advanced tools like demand forecasting models, simulations, promotion optimisers, and assortment engines — applying the right tool at the right moment. Together, these agents form an AI ecosystem, working in sync with each other and with human teams. They learn, adapt, and improve over time.
In essence, each agent focuses on a different area, using data and intelligent tools to support or automate parts of the planning process. It’s a collaborative system — not about replacing people, but enhancing how decisions are made.
This is one of the most exciting trends in retail planning today — and well worth exploring.
Example of AI agents in order planning
A Simple Illustration
Let’s say a retailer has a chain of convenience stores. It’s Monday morning — time to plan next month’s orders.
Instead of a human logging into spreadsheets and chasing reports, the AI Order Planning Agent wakes up automatically. It’s triggered by a scheduled planning cycle — and also keeps an eye on dynamic events like low stock alerts or sudden sales spikes.
Step 1: The Agent Detects a Change
Sales of bottled water spiked over the weekend due to a heatwave. The agent notices this unusual demand in the sales and inventory data — which it reads in real-time from integrated systems.
Step 2: The Agent Calls Intelligent Tools
To respond, the Order Planning Agent calls on its toolkit:
- Demand Forecast helps project future sales for bottled water (where future hot weather is factored in in the forecast).
- What-if Simulation runs different demand and replenishment scenarios to understand the variance of demand the corresponding supply
- Order Plan Generator creates a proposed order for each store based on the forecasted demand distribution and the existing inventory level.
- Cost Optimisation checks if bulk orders align with supplier agreements and budget goals.
Each tool contributes part of the plan, and the agent pulls it all together.
Step 3: Collaboration Across AI Agents
Next, the Order Planning Agent talks to other agents:
- The Pricing Agent tells whether a price change could ease demand or boost it.
- The Assortment Agent checks if alternative SKUs would be impacted.
- The Store Ops Agent verifies that upcoming deliveries can be managed based on shelf space and staff rosters.
They share inputs, sync decisions, and align their outputs — all within minutes.
Step 4: Final Output – A Smart, Aligned Order Plan
By the time a human planner checks in, the AI has already drafted a data-backed, cost-optimised, store-aware order plan — ready to review, adjust if needed, and push to execution.
The human planner didn’t disappear — they’re supervisors of the agents, and also now freed up more time to focus on strategy: reviewing vendor performance, negotiating new deals, or planning for the cycle’s agent behaviour.
AI Agents as Retail Planners
In an agentic retail setup, AI agents act like intelligent co-planners across functions — from order creation and demand planning to pricing and store operations. What makes them valuable isn’t just automation, but their ability to monitor live data, make decisions based on business goals, and adapt as conditions change.
Besides the capability of automation and calling intelligent tools like pricing optimisation and order plan generation, what's especially powerful is how these agents can work together. A pricing agent can adjust strategy to move stock, while an inventory agent updates restock plans in response. This connected planning reduces manual coordination and speeds up response time.
Importantly, this isn’t about replacing people. It’s about amplifying human decision-making. AI handles routine tasks and complex calculations, while planners focus on strategy, creativity, and customer experience.
The result? Faster decisions, more agile operations, and better cross-functional alignment. With the right setup, agentic retail isn’t just a concept — it’s a practical way to build a smarter, more responsive retail organisation.
AI Agents vs. Current Technology
It’s also important to distinguish AI agents from traditional ML and analytics — not because one replaces the other, but because they play different roles in the system.
ML models and analytics engines, like the ones powering our demand forecasts or pricing optimisers, are already highly effective at generating insights and even taking automated actions. What AI agents add is orchestration and adaptability. They decide when and how to use these tools, coordinate actions across multiple systems, and adjust based on evolving goals or feedback — all without needing to be manually triggered. In that sense, agents act more like intelligent conductors, bringing together different capabilities to respond in a more dynamic and goal-driven way.
A Real-Life Experiment
One real-world test of agentic retail involved an AI agent managing a vending machine in 2025 — a simple setup with a fridge, checkout tablet, and a limited product range. The AI was fully responsible for product selection, pricing, restocking coordination, and customer interaction.
The results were mixed. The agent responded to feedback, sourced niche products, and adjusted operations in real time — promising signs. But it also made avoidable errors: underpricing items, giving excessive discounts, mismanaging inventory, and failing to learn from feedback. The venture ultimately ran at a loss.
From our perspective at jahan.ai, experiments like this are encouraging — not because they’re perfect, but because they highlight exactly where the next layer of sophistication is needed. These aren’t fundamental blockers. They’re solvable problems, and many already have proven solutions.
Benefits and Challenges of Agentic Retail
The promise of agentic retail is compelling — not because it replaces humans, but because it enables faster, smarter, and more connected decision-making across the business.
Key Benefits
- More Accurate Plans: AI agents can continuously monitor the business, apply scientific tools like demand forecasting or pricing optimisation, and react instantly to changes. This 24/7 visibility, combined with access to proven models, leads to more precise, data-driven plans. With less time spent on spreadsheets and firefighting, planners can focus on strategy, innovation, and customer experience. Human creativity plus machine precision leads to better decisions overall.
- Efficiency: By automating routine tasks — like replenishment, markdowns, and promo planning — agents reduce human workload, lower manual errors, and increase operational efficiency. These small improvements, done frequently and at scale, can drive meaningful cost savings.
- Holistic, Cross-Function Planning: Unlike siloed teams, agents can optimise across domains — pricing, inventory, staffing — all in one loop. For example, a pricing change can instantly trigger a coordinated adjustment in restocking and promotion.
While the potential of agentic retail is strong, bringing AI agents into live operations isn’t plug-and-play. There are real challenges — but they’re not roadblocks. With the right design, tools, and expertise, each can be addressed over time.
- Trust and Control: Retailers must be cautious about handing decisions to autonomous systems. Poorly configured agents could misprice products, apply tone-deaf messaging, or unintentionally breach brand guidelines — damaging trust and reputation. That’s why robust governance is essential. This includes business rules, decision boundaries, clear escalation protocols, and performance tracking. It’s not about full autonomy from day one — it’s about building confidence through visibility, controls, and gradual rollout.
- Data and Tool Readiness: AI agents are only as effective as the systems they operate in. That means real-time, high-quality data across sales, stock, suppliers, and customers — and seamless access to the right tools, like forecasting engines, pricing models, or inventory simulators. For many retailers, this is a foundational challenge. Data may be fragmented, tools siloed, out-of-date, or not good enough. Investing in clean data infrastructure and well-integrated decision tools is a must before meaningful autonomy can be unlocked.
- Skills and Design Gap: Perhaps the most overlooked challenge is the skills gap. Designing and deploying agentic systems isn’t just about having a language model — it requires deep knowledge of retail workflows, decision logic, guardrail engineering, and how to connect AI agents with real-world tools. This is where many retailers get stuck: not because the tech isn’t ready, but because they lack the specialised skills to build agents that are useful, safe, and aligned with business goals.
Outlook
Agentic retail is no longer a distant concept — it’s already being explored and implemented by forward-thinking retailers and solution providers. From dynamic pricing to automated replenishment and promo planning, AI agents are beginning to take on real responsibilities in live environments.
We’re seeing growing momentum across the industry as retailers move beyond static analytics toward AI-driven, real-time decision-making. What started with narrow use cases is rapidly expanding into broader orchestration across pricing, demand, supply chain, and store ops — supported by smarter models and more robust agent frameworks.
The opportunity now lies in scaling what works: giving agents access to the right tools, embedding business logic and guardrails, and creating systems where humans and AI collaborate effectively. Businesses that succeed here won’t just run faster — they’ll plan smarter, adapt quicker, and build a competitive edge rooted in agility and intelligence.
How We Can Help
At jahan.ai, we’ve been deeply involved in this space since the early wave of large language models and generative AI. Beyond building high-performance tools like demand forecasts, pricing optimisers, assortment optimisers, and workforce optimisers, we also focus on the orchestration layer — helping businesses design and deploy AI agents that can intelligently use these tools, make decisions, and work within real-world constraints.
Our team brings deep expertise in agentic frameworks, fine-tuning, context engineering, and governance — all essential to making agent-based systems both effective and reliable. Whether you’re exploring pilot use cases or ready to scale, we offer deployable solutions and tailored support to help bring agentic retail to life in your business.
To learn more or have a conversation, reach out to info@jahan.ai — we’re always keen to share ideas and help where we can.
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