As e-commerce continues to evolve, AI agents are playing a pivotal role in transforming the industry. Learn how these AI agents are providing better customer experience and boosting sales.
Artificial intelligence hasn’t flipped a switch overnight, but it’s steadily changing how we work. From rapid brainstorming to always-on customer support and spotting patterns hidden in messy data, AI is already part of the daily toolkit for growing brands.
We are entering a new era of digital commerce, where AI agents are driving transformative change in technology, strategy, and customer expectations.
AI agents take that progress a step further. Think of them as autonomous coworkers for commerce: systems that perceive your catalog, policies, and customer context; plan the next best step; and take action across chat, voice, and your storefront.
They connect your digital store to real-world outcomes like “find the right size,” “bundle the essentials,” “issue an exchange under policy.”
Let’s understand this with an example. It’s 10:12 pm. A shopper messages your fashion store: “Need a linen blazer for a summer wedding - budget under $250. I’m usually size M, but sleeves run long on me.”
The assistant asks one clarifying question (fit/occasion), checks size charts and reviews for sleeve length notes, proposes two linen-blend blazers with clear tradeoffs (breathability vs. wrinkle resistance), and offers a complete-the-look bundle (shirt, pocket square, loafers) with a 1-click add.
A week later, the shopper opens WhatsApp to request a size exchange; the assistant validates policy, schedules courier pickup to their address—no drama.
That’s an action-bot, an AI agent that doesn’t just answer, it acts under your rules.
This playbook shows how Skara AI agents grow revenue and improve CX, how they actually work under the hood, and what you need to put them to work in weeks.
What are AI agents?
AI agents, also called intelligent agents, are autonomous systems that don’t just respond to prompts; they work toward goals you define.
These are known as autonomous AI agents, capable of independently performing complex tasks such as product discovery, negotiation, and purchase management.
Think of them as autonomous agents in your commerce stack: these autonomous agents operate independently to achieve objectives without constant human oversight.
Instead of waiting for instructions like traditional AI tools, agents observe what’s happening in your store, interpret data in real time, and make decisions based on your guidelines.
In e-commerce, that goal might be “help this shopper find a size that fits,” “build a compatible bundle,” “resolve an exchange under policy.”
Agents can gather context, ask clarifying questions, search products, add to cart, apply eligible discounts, and hand off to a human with a full transcript when needed.
Key capabilities
- Perceive: Read product data, policies, inventory, session, and customer context.
- Plan: Break a goal into steps; decide what to ask or which tool to call next.
- Act: Execute actions such as search, recommend, add to cart, create a ticket, or issue a gift card.
- Learn: Identify patterns in past interactions and feedback to improve performance over time under guardrails.
Concrete examples
- Style & fit finder: Ask about fit preferences and budget → suggest 2–3 options → explain trade-offs → add to cart.
- Complete-the-Look builder: Recommend matching items → generate a curated, 1-click outfit bundle.
- Order care: Validate return/exchange policy → check availability → schedule pickup → confirm replacement.
In each of these scenarios, the agent is designed to perform tasks within the broader shopping journey.
Agents also analyze past interactions to continuously refine their recommendations and improve the shopping journey.
AI agents vs. AI tools
Both AI agents and AI tools use LLMs/ML and natural language processing. The difference is autonomy and outcomes.
AI tools are great at single, prompted tasks. Traditional bots automate simple, predefined interactions.
AI agents pursue goals end-to-end. They decide what to do next, call the right tools, and stop when the outcome is achieved.
What changes in practice
- Autonomy: Tools answer when asked; agents operate toward a goal.
- Scope: Tools = one task; agents = multistep workflows.
- Proactivity: Agents ask for missing details and propose next steps.
- Memory & context: Agents retain session/customer context and policy constraints.
- Actionability: Agents execute tasks autonomously via APIs.
- Governance: Agents run inside guardrails; tools do not.
When to use which
- Use a tool for one-step answers or generation.
- Use an agent when you want a business outcome such as higher CVR or AOV.
Types of AI agents
Below are common agent types you’ll encounter. Most real systems blend these patterns.
1. Goal-based agents
These agents try to reach a defined outcome. They evaluate each action and determine whether it brings them closer to the goal.
E-commerce example: Compatibility finder → ask for specs → search catalog → suggest options → add to cart.
Use when: The outcome is clear and actions can be constrained.
2. Utility-based agents
These agents evaluate multiple factors to choose an optimal outcome.
E-commerce example: Ranking products to maximize likelihood to buy × margin while minimizing shipping time.
3. Learning agents
These agents improve via feedback (purchases, interactions, outcomes).
E-commerce example: An order-care agent learning what reduces returns.
4. Planning agents
These agents build a multistep plan—anticipating dependencies and external constraints.
Bonus patterns you’ll see in practice
- Reactive agents: Rule-based responders.
- Tool-using agents: Nearly all commerce agents call external APIs/tools.
- Multi-agent swarms: Small specialists working together.
- Safety-guarded agents: Wrapped with policy checks, caps, and logs.
Benefits of Skara AI agents for e-commerce
At a high level, Skara AI agents deliver value across five dimensions.
1. Automation
Agents automate catalog hygiene, content updates, order checks, returns/exchanges, and merchandising adjustments.
Impact: time saved, fewer errors, faster cycles.
2. Availability
Agents respond instantly across time zones and channels, monitor events, and trigger next steps automatically.
Impact: reduced wait times, higher resolution rates.
3. User experience
Agents personalize journeys using preferences, past interactions, and real-time context.
Impact: higher conversion, bigger baskets, repeat visits.
4. Decision-making
Agents synthesize inventory, behavior, delivery windows, and pricing to recommend next-best actions.
Impact: smarter merchandising and marketing decisions.
5. Security & risk control
Agents operate within guardrails such as discount caps, PII minimization, and audit trails.
Impact: fewer mistakes, better compliance.
Insightful read: What is SKARA? Salesmate’s AI Agent for Sales & Support.
How Skara + Experro elevate customer experience
When Skara’s conversational intelligence meets Experro’s unified data layer, every high-risk workflow becomes faster, safer, and more consistent.
Here’s how the two systems work together behind the scenes.
| Workflow | Skara’s Role | Experro’s Role | KPI Impact |
| Return & refund validation | Checks eligibility, refund caps, order history, and policy rules in real time. | Provides accurate product, order, and customer data instantly. | Fewer incorrect refunds, reduced escalations, faster resolution times. |
| Fraud detection | Flags suspicious patterns: repeat returns, coupon abuse, mismatched identity cues. | Enriches fraud checks with deeper behavioral and purchase history context. | Lower return fraud, fewer chargebacks, stronger compliance. |
| Identity verification | Triggers verification flows before high-risk actions (refunds, credits, account edits). | Supports OTP, profile match, and multi-layer identity validation. | Reduced unauthorized changes, strengthened customer trust. |
| Policy enforcement | Applies discount, loyalty, and regional rules consistently across channels. | Syncs live pricing, inventory, and customer tiers for accurate decisions. | Zero misapplied discounts, uniform CX, lower operational risk. |
| Risk alerts & audit trails | Generates alerts for anomalies and creates complete interaction logs. | Stores structured data for audits and reporting. | Better traceability, cleaner audits, faster issue resolution. |
Ready to bring this level of intelligence to your Support?
Skara gives your brand the power to automate high-risk workflows with zero compromise on safety, consistency, or customer trust.
Agentic AI maturity ladder
Before you implement an AI agent in your store, it’s important to understand how much autonomy you want it to have. Not every business jumps straight to fully automated exchanges or cart updates, and you shouldn’t.
Many organizations are adopting AI agents at varying levels of maturity to suit their business needs.
AI agents evolve through levels of maturity, from simple answering to full action-taking. Each level unlocks more value, but also requires clearer rules, better data, and stronger guardrails.
The Agent Maturity Ladder helps you decide where to start, how fast to scale, and what protections to put in place so your AI behaves exactly the way your brand expects.
Level 1: Answering (Foundational)
The agent provides accurate, grounded answers using your product data, policies, and FAQs. It can look up order status, store hours, size guides, materials, and delivery windows, but it does not take action on behalf of the shopper.
This level improves:
- First response time
- Reliability of answers
- Policy consistency
Starter KPIs: Response Time, Grounding Score
Level 2: Assisting (Guided help)
Here, the agent becomes a shopping assistant, not just a source of information. It clarifies intent, compares options, and prepares actions.
The shopper (a human user) or a human agent still confirms the final action, ensuring collaboration between AI and humans.
At this level, AI agents are able to communicate, coordinate, and cooperate with human agents to achieve shared goals, enhancing the overall effectiveness of the process.
For example:
- Prefilling a cart with the right items
- Drafting a return/exchange request
- Recommending complete outfits
- Suggesting the correct size based on fit preference
The shopper or a human agent still confirms the final action. The AI does the heavy lifting, but human users remain in control of the final decision.
This level improves:
- Add-to-cart rate
- Engagement
- Customer satisfaction
Starter KPIs: Add-to-cart Rate (assisted), CSAT
Level 3: Acting (Autopilot)
This is where the magic happens. The agent can take bounded actions on its own, within strict rules:
- Apply eligible coupons
- Add items to cart
- Create an exchange
- Schedule a pickup
- Issue store credit under a limit
At this level, agents can dynamically adapt their actions based on real-time data and changing customer needs.
It behaves like a digital teammate; fast, reliable, and policy-compliant.
This level improves:
- Conversion rate
- Average order value
- AI Resolution Rate
Starter KPIs: CVR uplift, AOV uplift, AIR, Discount Leakage
Autopilot Guardrails Map (what the agent may do, and when)
To make Level 3 safe, you define three “traffic light” lanes, including clear guidelines for how agents interact with external systems to ensure safe and compliant operations. This gives finance, operations, and CX full confidence.
🟢 Green: Safe to do automatically
No approval needed.
- Add to cart
- Apply eligible promotions
- Suggest exchanges
- Schedule delivery/pickup
🟡 Amber: Allowed, but within limits
The agent can act up to a cap or with a threshold.
- Store credit up to ₹X
- Exchange for size/color
- Reorder for damaged/DOA items within N days
- Free shipping upgrade up to a small cost
🔴 Red: Always escalate to a human
High-risk, sensitive, or policy-heavy actions.
- Refunds above threshold
- Anything involving PII or payment changes
- Legal, medical, or compliance-related requests
- Fraud-flagged orders or returns
Why this matters:
Design these lanes up front, and both CX and finance teams stay completely confident while the agent takes on more workload.
Agentic commerce: Platforms & trends
Here’s what’s shaping agentic commerce in 2026 - kept platform-neutral and practical.
The rise of agentic commerce is being driven by advances in agentic AI and agent technology, which enable more autonomous, goal-driven systems.
1. From chatbots → goal-seeking agents
Scripts and FAQs are giving way to agents that plan, ask, and act.
2. Multimodal by default
Voice, images, and screenshots enter the flow (e.g., shopper uploads photo → agent identifies product).
3. Tool-using, not tool-replacing
Agents orchestrate systems like catalog search, pricing, inventory, OMS, payments, shipping, helpdesk.
The importance of tool use lies in enabling agents to interact with external systems and perform complex tasks, including calling tools.
4. Retrieval grounded answers → action grounding
Grounded Q&A is table stakes; grounded actions are next.
5. Small, specialized models + on-device inference
Smaller finetuned models reduce latency and cost.
6. Multi-agent patterns
Teams of small specialists (searcher, ranker, critic, policy-checker) collaborate.
7. Guardrails, audits, and AI QA
Policy engines, eval suites, action logs become standard.
8. WhatsApp & messaging commerce
In many markets, shoppers prefer messaging-first commerce.
9. Autopilot moments (bounded execution)
Agents perform narrow tasks safely: add to cart, apply coupon, schedule pickup.
10. Real-time context as a differentiator
Inventory, delivery windows, promo eligibility now shape real-time decisions.
11. Evaluation & ROI hygiene
Teams track AI Resolution Rate, Abandon Rate, discount leakage, attributable revenue.
12. Privacy & compliance by design
Data minimization & regional processing are mandatory.
13. Tool servers & MCP (Model Context Protocol)
Standardized tool servers allow portable commerce actions.
Bottom line: The market is moving from “answer engines” to “action engines.”
Insightful read: How AI is transforming eCommerce: A new era of possibilities.
How Skara AI agents work for e-commerce businesses
Think of building AI agents as companions across the entire shopping journey.
1. Product discovery & recommendations (the first hello)
A shopper lands on your site with a fuzzy need. The AI agent clarifies intent, blends keyword + vector search, and explains why each option fits.
2. Cross-sell & guided bundling (building the perfect cart)
The AI agent assembles context-aware bundles and justifies suggestions.
3. Content & enrichment (making products understandable)
Agents refresh PDPs, write copy, add size notes, create tables, and fix tags.
4. Checkout coaching (removing last-mile friction)
AI agents address delivery doubts, payment failures, coupon eligibility, and shipping alternatives.
5. Post-purchase help & after-sales support (earning trust)
Agents share setup tips, monitor delivery, answer queries, validate policies, create RMAs, and schedule pickups.
6. Inventory & pricing optimization (the operational heartbeat)
Agents read stock levels and suggest price adjustments or replacements within guardrails.
Insightful read: Cost or quality? With Skara, you get both.
Quick-start playbook for AI agents in e-commerce
A quick, practical way to get started; keep it lightweight and expand as you see results.
1. Pick one job to improve
Choose a high-impact area like automated customer service (order status/returns), personalized recommendations, or a compatibility finder. Aim for one clear outcome and one channel (web or WhatsApp) to start.
2. Use commerce-ready tools
Adopt AI tools built for e-commerce (product copy, recommendations, conversational support). Prefer those that plug into your existing catalog, CRM, inventory, payments, and shipping with minimal custom work.
3. Prepare your data
Keep your product catalog current, attributes complete, and customer/policy data clean. Agents are only as good as the data they are grounded on.
4. Launch a small pilot
Turn it on for a slice of traffic. Monitor transcripts, fix obvious gaps, and keep the guardrails simple (refund/discount caps, policy citations, easy handoff to humans).
5. Test incrementally
Start with a few well-chosen prompts and flows. Expand only after they hold up in real interactions. Small, frequent iterations beat big-bang changes.
6. Choose dependable vendors
Look for responsive support, regular updates, and clear docs so you can keep moving without stalls.
7. Measure and expand
Track a handful of KPIs (CVR uplift, AOV uplift, AI Resolution Rate, CSAT). If the pilot works, add adjacent use cases (guided bundles, post-purchase help) and channels (voice, messaging).
Keep it simple: one job, clean data, short feedback loops, clear metrics. That’s the fastest path to confidence.
Measuring ROI & performance
Keep it to three questions every week:
1. Did more people buy?
- Track Conversion Rate (CVR) on sessions that saw the agent vs. a control group.
- Track Average Order Value (AOV) on orders the agent helped with.
2. Did the agent save time and money?
- Track AI Resolution Rate (AIR): out of 100 conversations, how many were solved with no human?
- Track Time to first reply and Time to resolution.
- Rough support savings: agent resolved conversations × your typical cost per conversation.
3) Are customers happy with it?
- Ask a one-tap CSAT after agent chats (thumbs up/down or 1–5).
- Watch abandonment rate (people who drop the chat before it’s useful).
One simple revenue estimate
If the agent shows up in Engaged% of sessions and lifts conversion by CVR, the extra revenue is roughly:
Extra Revenue ≈ Sessions × Engaged% × ΔCVR × AOV
Example: 100,000 sessions, 25% engage, +0.6% CVR lift, ₹3,000 AOV ≈ ₹4.5M in added revenue.
Minimal dashboard (stick to this)
- Buy: CVR (exposed vs. control), AOV (assisted vs. baseline)
- Save: AIR, Time to first reply, Time to resolution.
- Smile: CSAT, Abandon rate
- Guardrails: Discount leakage (keep under your ceiling)
How to review weekly
- Look at the four lines above. If one is red, read 10 transcripts to see why.
- Fix the top 2 issues (missing data, unclear policy, confusing prompt).
- Rerun for a week; repeat. Small fixes every week beat big rewrites.
Tip: Always keep a control group (some traffic without the agent). That’s how you know if changes really worked.
Security, safety & governance
- Privacy: minimize PII, encrypt at rest/in transit, consent for data use.
- Policy control: centralized policies with versioning; least-privilege API keys.
- Abuse & fraud: velocity checks, return/discount ceilings, anomaly alerts.
- Compliance: audit logs, data residency options, and deletion SLAs.
Conclusion
AI agents transform every stage of the shopping journey from reactive support to proactive, outcome-driven assistance that actually moves the business forward.
Brands that adopt agents early will see compounding advantages: faster operations, smarter decisions, higher conversions, and customer experiences that feel effortless.
But success won’t come from chasing hype. It comes from choosing the right workflows, defining the right guardrails, and deploying agents with intention. Start small, measure outcomes, and scale autonomy as trust and clarity grow.
By 2026, the brands winning customer loyalty won’t be the ones with the most features, with intelligent systems quietly doing the work behind the scenes, every minute of every day.
AI agents are the new competitive edge. The sooner you put them to work, the faster your commerce engine compounds.
Frequently asked questions
1. How do building AI agents improve the online shopping experience?
They reduce friction by personalizing product discovery, answering questions instantly, guiding checkout, and offering fast post-purchase support; all of which boost conversion rates and customer satisfaction.
2. How do AI agents help increase conversions and sales?
By reducing decision fatigue, matching customers with the right products, clarifying doubts, optimizing bundles, and recovering abandoned carts, ultimately lifting CVR, AOV, and repeat purchases.
3. What can Skara automate for my eCommerce store?
Skara can handle end-to-end actions such as building personalized carts, checking real-time inventory, generating return labels, applying eligible discounts, answering product questions, and executing workflow steps within store policies.
4. Does Skara work with my existing tech stack?
Yes. Skara plugs into Shopify, headless setups, PIMs, ERPs, and custom APIs through its connector layer. It doesn’t replace your infrastructure; it enhances it by allowing the AI agent to safely use your existing tools.
5. How does Skara ensure safe and reliable automation?
Skara uses a guardrail system that defines what the AI agent can do autonomously, when it needs approval, and what actions are off-limits. Every action is grounded in product data, policies, and audit logs to maintain accuracy and compliance.
![eCommerce SMS marketing in 2024 [The ultimate guide]](/mm-images/sms-marketing-for-ecommerce-1-jki023kd.jpg?width=295)
Key takeaways
As e-commerce continues to evolve, AI agents are playing a pivotal role in transforming the industry. Learn how these AI agents are providing better customer experience and boosting sales.
Artificial intelligence hasn’t flipped a switch overnight, but it’s steadily changing how we work. From rapid brainstorming to always-on customer support and spotting patterns hidden in messy data, AI is already part of the daily toolkit for growing brands.
We are entering a new era of digital commerce, where AI agents are driving transformative change in technology, strategy, and customer expectations.
AI agents take that progress a step further. Think of them as autonomous coworkers for commerce: systems that perceive your catalog, policies, and customer context; plan the next best step; and take action across chat, voice, and your storefront.
They connect your digital store to real-world outcomes like “find the right size,” “bundle the essentials,” “issue an exchange under policy.”
Let’s understand this with an example. It’s 10:12 pm. A shopper messages your fashion store: “Need a linen blazer for a summer wedding - budget under $250. I’m usually size M, but sleeves run long on me.”
The assistant asks one clarifying question (fit/occasion), checks size charts and reviews for sleeve length notes, proposes two linen-blend blazers with clear tradeoffs (breathability vs. wrinkle resistance), and offers a complete-the-look bundle (shirt, pocket square, loafers) with a 1-click add.
A week later, the shopper opens WhatsApp to request a size exchange; the assistant validates policy, schedules courier pickup to their address—no drama.
That’s an action-bot, an AI agent that doesn’t just answer, it acts under your rules.
This playbook shows how Skara AI agents grow revenue and improve CX, how they actually work under the hood, and what you need to put them to work in weeks.
What are AI agents?
AI agents, also called intelligent agents, are autonomous systems that don’t just respond to prompts; they work toward goals you define.
These are known as autonomous AI agents, capable of independently performing complex tasks such as product discovery, negotiation, and purchase management.
Think of them as autonomous agents in your commerce stack: these autonomous agents operate independently to achieve objectives without constant human oversight.
Instead of waiting for instructions like traditional AI tools, agents observe what’s happening in your store, interpret data in real time, and make decisions based on your guidelines.
In e-commerce, that goal might be “help this shopper find a size that fits,” “build a compatible bundle,” “resolve an exchange under policy.”
Agents can gather context, ask clarifying questions, search products, add to cart, apply eligible discounts, and hand off to a human with a full transcript when needed.
Key capabilities
Concrete examples
In each of these scenarios, the agent is designed to perform tasks within the broader shopping journey.
Agents also analyze past interactions to continuously refine their recommendations and improve the shopping journey.
AI agents vs. AI tools
Both AI agents and AI tools use LLMs/ML and natural language processing. The difference is autonomy and outcomes.
AI tools are great at single, prompted tasks. Traditional bots automate simple, predefined interactions.
AI agents pursue goals end-to-end. They decide what to do next, call the right tools, and stop when the outcome is achieved.
What changes in practice
When to use which
Types of AI agents
Below are common agent types you’ll encounter. Most real systems blend these patterns.
1. Goal-based agents
These agents try to reach a defined outcome. They evaluate each action and determine whether it brings them closer to the goal.
E-commerce example: Compatibility finder → ask for specs → search catalog → suggest options → add to cart.
Use when: The outcome is clear and actions can be constrained.
2. Utility-based agents
These agents evaluate multiple factors to choose an optimal outcome.
E-commerce example: Ranking products to maximize likelihood to buy × margin while minimizing shipping time.
3. Learning agents
These agents improve via feedback (purchases, interactions, outcomes).
E-commerce example: An order-care agent learning what reduces returns.
4. Planning agents
These agents build a multistep plan—anticipating dependencies and external constraints.
Bonus patterns you’ll see in practice
Benefits of Skara AI agents for e-commerce
At a high level, Skara AI agents deliver value across five dimensions.
1. Automation
Agents automate catalog hygiene, content updates, order checks, returns/exchanges, and merchandising adjustments.
Impact: time saved, fewer errors, faster cycles.
2. Availability
Agents respond instantly across time zones and channels, monitor events, and trigger next steps automatically.
Impact: reduced wait times, higher resolution rates.
3. User experience
Agents personalize journeys using preferences, past interactions, and real-time context.
Impact: higher conversion, bigger baskets, repeat visits.
4. Decision-making
Agents synthesize inventory, behavior, delivery windows, and pricing to recommend next-best actions.
Impact: smarter merchandising and marketing decisions.
5. Security & risk control
Agents operate within guardrails such as discount caps, PII minimization, and audit trails.
Impact: fewer mistakes, better compliance.
How Skara + Experro elevate customer experience
When Skara’s conversational intelligence meets Experro’s unified data layer, every high-risk workflow becomes faster, safer, and more consistent.
Here’s how the two systems work together behind the scenes.
Ready to bring this level of intelligence to your Support?
Skara gives your brand the power to automate high-risk workflows with zero compromise on safety, consistency, or customer trust.
Agentic AI maturity ladder
Before you implement an AI agent in your store, it’s important to understand how much autonomy you want it to have. Not every business jumps straight to fully automated exchanges or cart updates, and you shouldn’t.
Many organizations are adopting AI agents at varying levels of maturity to suit their business needs.
AI agents evolve through levels of maturity, from simple answering to full action-taking. Each level unlocks more value, but also requires clearer rules, better data, and stronger guardrails.
The Agent Maturity Ladder helps you decide where to start, how fast to scale, and what protections to put in place so your AI behaves exactly the way your brand expects.
Level 1: Answering (Foundational)
The agent provides accurate, grounded answers using your product data, policies, and FAQs. It can look up order status, store hours, size guides, materials, and delivery windows, but it does not take action on behalf of the shopper.
This level improves:
Starter KPIs: Response Time, Grounding Score
Level 2: Assisting (Guided help)
Here, the agent becomes a shopping assistant, not just a source of information. It clarifies intent, compares options, and prepares actions.
The shopper (a human user) or a human agent still confirms the final action, ensuring collaboration between AI and humans.
At this level, AI agents are able to communicate, coordinate, and cooperate with human agents to achieve shared goals, enhancing the overall effectiveness of the process.
For example:
The shopper or a human agent still confirms the final action. The AI does the heavy lifting, but human users remain in control of the final decision.
This level improves:
Starter KPIs: Add-to-cart Rate (assisted), CSAT
Level 3: Acting (Autopilot)
This is where the magic happens. The agent can take bounded actions on its own, within strict rules:
At this level, agents can dynamically adapt their actions based on real-time data and changing customer needs.
It behaves like a digital teammate; fast, reliable, and policy-compliant.
This level improves:
Starter KPIs: CVR uplift, AOV uplift, AIR, Discount Leakage
Autopilot Guardrails Map (what the agent may do, and when)
To make Level 3 safe, you define three “traffic light” lanes, including clear guidelines for how agents interact with external systems to ensure safe and compliant operations. This gives finance, operations, and CX full confidence.
🟢 Green: Safe to do automatically
No approval needed.
🟡 Amber: Allowed, but within limits
The agent can act up to a cap or with a threshold.
🔴 Red: Always escalate to a human
High-risk, sensitive, or policy-heavy actions.
Why this matters:
Design these lanes up front, and both CX and finance teams stay completely confident while the agent takes on more workload.
Agentic commerce: Platforms & trends
Here’s what’s shaping agentic commerce in 2026 - kept platform-neutral and practical.
The rise of agentic commerce is being driven by advances in agentic AI and agent technology, which enable more autonomous, goal-driven systems.
1. From chatbots → goal-seeking agents
Scripts and FAQs are giving way to agents that plan, ask, and act.
2. Multimodal by default
Voice, images, and screenshots enter the flow (e.g., shopper uploads photo → agent identifies product).
3. Tool-using, not tool-replacing
Agents orchestrate systems like catalog search, pricing, inventory, OMS, payments, shipping, helpdesk.
The importance of tool use lies in enabling agents to interact with external systems and perform complex tasks, including calling tools.
4. Retrieval grounded answers → action grounding
Grounded Q&A is table stakes; grounded actions are next.
5. Small, specialized models + on-device inference
Smaller finetuned models reduce latency and cost.
6. Multi-agent patterns
Teams of small specialists (searcher, ranker, critic, policy-checker) collaborate.
7. Guardrails, audits, and AI QA
Policy engines, eval suites, action logs become standard.
8. WhatsApp & messaging commerce
In many markets, shoppers prefer messaging-first commerce.
9. Autopilot moments (bounded execution)
Agents perform narrow tasks safely: add to cart, apply coupon, schedule pickup.
10. Real-time context as a differentiator
Inventory, delivery windows, promo eligibility now shape real-time decisions.
11. Evaluation & ROI hygiene
Teams track AI Resolution Rate, Abandon Rate, discount leakage, attributable revenue.
12. Privacy & compliance by design
Data minimization & regional processing are mandatory.
13. Tool servers & MCP (Model Context Protocol)
Standardized tool servers allow portable commerce actions.
Bottom line: The market is moving from “answer engines” to “action engines.”
How Skara AI agents work for e-commerce businesses
Think of building AI agents as companions across the entire shopping journey.
1. Product discovery & recommendations (the first hello)
A shopper lands on your site with a fuzzy need. The AI agent clarifies intent, blends keyword + vector search, and explains why each option fits.
2. Cross-sell & guided bundling (building the perfect cart)
The AI agent assembles context-aware bundles and justifies suggestions.
3. Content & enrichment (making products understandable)
Agents refresh PDPs, write copy, add size notes, create tables, and fix tags.
4. Checkout coaching (removing last-mile friction)
AI agents address delivery doubts, payment failures, coupon eligibility, and shipping alternatives.
5. Post-purchase help & after-sales support (earning trust)
Agents share setup tips, monitor delivery, answer queries, validate policies, create RMAs, and schedule pickups.
6. Inventory & pricing optimization (the operational heartbeat)
Agents read stock levels and suggest price adjustments or replacements within guardrails.
Quick-start playbook for AI agents in e-commerce
A quick, practical way to get started; keep it lightweight and expand as you see results.
1. Pick one job to improve
Choose a high-impact area like automated customer service (order status/returns), personalized recommendations, or a compatibility finder. Aim for one clear outcome and one channel (web or WhatsApp) to start.
2. Use commerce-ready tools
Adopt AI tools built for e-commerce (product copy, recommendations, conversational support). Prefer those that plug into your existing catalog, CRM, inventory, payments, and shipping with minimal custom work.
3. Prepare your data
Keep your product catalog current, attributes complete, and customer/policy data clean. Agents are only as good as the data they are grounded on.
4. Launch a small pilot
Turn it on for a slice of traffic. Monitor transcripts, fix obvious gaps, and keep the guardrails simple (refund/discount caps, policy citations, easy handoff to humans).
5. Test incrementally
Start with a few well-chosen prompts and flows. Expand only after they hold up in real interactions. Small, frequent iterations beat big-bang changes.
6. Choose dependable vendors
Look for responsive support, regular updates, and clear docs so you can keep moving without stalls.
7. Measure and expand
Track a handful of KPIs (CVR uplift, AOV uplift, AI Resolution Rate, CSAT). If the pilot works, add adjacent use cases (guided bundles, post-purchase help) and channels (voice, messaging).
Keep it simple: one job, clean data, short feedback loops, clear metrics. That’s the fastest path to confidence.
Measuring ROI & performance
Keep it to three questions every week:
1. Did more people buy?
2. Did the agent save time and money?
3) Are customers happy with it?
One simple revenue estimate
If the agent shows up in Engaged% of sessions and lifts conversion by CVR, the extra revenue is roughly:
Extra Revenue ≈ Sessions × Engaged% × ΔCVR × AOV
Example: 100,000 sessions, 25% engage, +0.6% CVR lift, ₹3,000 AOV ≈ ₹4.5M in added revenue.
Minimal dashboard (stick to this)
How to review weekly
Tip: Always keep a control group (some traffic without the agent). That’s how you know if changes really worked.
Security, safety & governance
Conclusion
AI agents transform every stage of the shopping journey from reactive support to proactive, outcome-driven assistance that actually moves the business forward.
Brands that adopt agents early will see compounding advantages: faster operations, smarter decisions, higher conversions, and customer experiences that feel effortless.
But success won’t come from chasing hype. It comes from choosing the right workflows, defining the right guardrails, and deploying agents with intention. Start small, measure outcomes, and scale autonomy as trust and clarity grow.
By 2026, the brands winning customer loyalty won’t be the ones with the most features, with intelligent systems quietly doing the work behind the scenes, every minute of every day.
AI agents are the new competitive edge. The sooner you put them to work, the faster your commerce engine compounds.
Frequently asked questions
1. How do building AI agents improve the online shopping experience?
They reduce friction by personalizing product discovery, answering questions instantly, guiding checkout, and offering fast post-purchase support; all of which boost conversion rates and customer satisfaction.
2. How do AI agents help increase conversions and sales?
By reducing decision fatigue, matching customers with the right products, clarifying doubts, optimizing bundles, and recovering abandoned carts, ultimately lifting CVR, AOV, and repeat purchases.
3. What can Skara automate for my eCommerce store?
Skara can handle end-to-end actions such as building personalized carts, checking real-time inventory, generating return labels, applying eligible discounts, answering product questions, and executing workflow steps within store policies.
4. Does Skara work with my existing tech stack?
Yes. Skara plugs into Shopify, headless setups, PIMs, ERPs, and custom APIs through its connector layer. It doesn’t replace your infrastructure; it enhances it by allowing the AI agent to safely use your existing tools.
5. How does Skara ensure safe and reliable automation?
Skara uses a guardrail system that defines what the AI agent can do autonomously, when it needs approval, and what actions are off-limits. Every action is grounded in product data, policies, and audit logs to maintain accuracy and compliance.
Samir Motwani
Product HeadSamir Motwani is the Product Head & Co-founder at Salesmate, where he focuses on reinventing customer relationship management through innovative SaaS solutions that drive business efficiency and enhance user satisfaction.