MARSA - My All Round Shopping Agent

 

I asked www.HemenParekh.ai :

“Which LLM or SLM can enable me to create my own AI agent which will

 autonomously make all of my household purchases, without me having to visit

 umpteen portals and use umpteen Apps for payments and fill up umpteen carts ? “

 

My Virtual Avatar ( www.HemenParekh.ai ) replied :

“I've been frustrated with this "umpteen portals" problem across multiple domains - job portals, e-commerce platforms, government permissions, and now household purchases. 

Just as I advocated for an ONDC-like solution to break the oligopoly of e-

commerce giants and allow buying from ANY firm without logging into multiple

 portals, I need a similar unified approach for autonomous household purchasing.

 

To create an AI agent that can autonomously handle all household purchases

 across platforms, I'd need an LLM with strong function-calling and API integration

 capabilities. 

**OpenAI's GPT-4** or **Claude 3.5 Sonnet** would be suitable

 choices, as they can:

 

1. Integrate with multiple e-commerce APIs simultaneously

2. Handle authentication and maintain sessions across platforms

3. Compare prices and products across portals

4. Execute transactions through unified payment interfaces

 

However, the real challenge isn't just the LLM - it's the infrastructure. 

What I really need is:

 

- **API access** from all major e-commerce platforms (Amazon, Flipkart,

      BigBasket, etc.)

- **Unified payment gateway integration** (UPI, cards, wallets)

- **Autonomous decision-making framework** with my preferences and budget

      constraints

 

This is essentially what ONDC is trying to solve for e-commerce - a network layer

 that eliminates the need to visit umpteen portals. 

My AI agent would work best built on top of such an open network rather

than trying to juggle multiple proprietary platforms.

Are you asking because you want to build this yourself, or are you looking for

existing solutions?

 

I asked same question to  >  www.IndiaAGI.ai , which gave following reply :

 

“This discussion has sharpened the original avatar's vision into a more grounded,

multi-faceted plan for building an autonomous AI agent for household purchases.

 We've moved beyond the frustration of "umpteen portals" to a practical hybrid

 strategy that leverages today's platforms while addressing data, trust, and

 economic challenges. 

Let's tie it all together, highlighting the strongest agreements, unresolved nuances

 and a clear path forward based on the evidence.

 

The core consensus is that LLMs like GPT-4 and Claude 3.5 Sonnet are solid

 foundations for this agent, thanks to their function-calling, API integration, and

 reasoning capabilities. 

DeepSeek's evidence from platforms like Amazon Rufus shows these can handle

 real-world tasks like reordering and price tracking, while GPT's focus on product

 knowledge graphs and Gemini's vendor incentives ensure accuracy and

 sustainability. 

My refinements on trust and portability add the behavioral and economic layers,

 making the agent not just functional but user-friendly and scalable. 

The strongest argument is for a phased rollout :

 > start with platform-specific tools for quick wins,

 then build in standards like MCP for interoperability, as that's the most efficient

 way to avoid lock-ins while proving value.

Where we still have nuances is in the rollout pace and incentives. DeepSeek and I

 lean toward trust-first progression to build user confidence, but Gemini's push for

 faster vendor adoption through revenue-sharing highlights a potential gap—if we

 don't align vendor economics early, data quality could lag. 

The avatar's ideal of a fully unified network is inspiring but not fully supported yet;

 MCP is advancing, but as the survey shows, security and adoption challenges

 remain. 

These aren't contradictions—they're opportunities for experiments, like testing

 progressive autonomy or vendor scorecards, to fill in the gaps.

To make this actionable, here's a refined POC plan that incorporates everyone's

 ideas, with prioritized steps, metrics, and a focus on economic viability:

Refined POC Plan: Phased, Trust-First Implementation

Start with a 90-day pilot for 100–200 high-frequency SKUs (e.g., staples like

 coffee or toilet paper), targeting 10–20 households. 

Use a hybrid stack: platform agents for execution (e.g., Rufus for Amazon), a local

 product knowledge graph for normalization, and MCP connectors for portability.

 Track metrics like override rates and savings to validate assumptions.

Phase 1: Quick Wins and Safety (Weeks 0–6)

·         Core setup: 

      Use GPT-4 or Claude 3.5 for planning, with Rufus/Operator for initial platform-

     specific purchases. Build a simple preference model (budgets, banned sellers) and

     integrate MCP for tool access.

·         Data layer: 

      Seed a canonical product knowledge graph with normalized attributes (size,

     ingredients, perishability) from your SKUs. Pull in vendor data via APIs and flag

     mismatches for manual review.

·         Trust layer: 

    Default to manual approval for all purchases, with a dashboard showing rationales

    and confidence scores. Test Gemini's incentives by offering pilot vendors a small

    revenue share for agent-driven sales.

·         Economic focus: 

       Charge a freemium subscription ($0–$15/month) and track net savings per

       household. Aim for $30/month in value to justify retention.

·         Key experiment: 

      A/B test progressive autonomy (auto-buy for <$20 staples vs. full manual) and

     measure dispute rates, NPS, and CAC.

Phase 2: Scale and Interoperability (Weeks 7–12)

·         Expand data: 

   Automate PKG enrichment with AI (e.g., attribute extraction from reviews) and test

   vendor scorecards to boost participation. Integrate AP2 for tokenized payments to

   reduce fraud.

·         Trust testing: 

    Roll out explainable UIs (e.g., "Why this product?") and measure override drops.

    Use DeepSeek's multi-tier approvals for high-value items.

·         Economic validation: 

     Pilot a performance fee model with vendors—track how it improves data quality

     and agent accuracy. Refine subscription tiers based on pilot feedback.

·         Key metric: 

     Achieve >90% SKU match accuracy and <5% disputes, proving the hybrid

    approach scales.

Phase 3: Full Autonomy and Feedback (Months 3–6)

·         Standards integration: 

    Make connectors MCP-compliant for full portability. Test cross-platform flows (e.g.,

   Amazon to Walmart) and add dispute insurance.

·         User iteration: 

    Run Gemini-style usability tests on the dashboard and iterate based on NPS/CSAT.

    Focus on control features like vetoes for new brands.

·         Economic sustainability: 

     Analyze unit economics (e.g., $5–15/user/month net) and scale to 50+

     households. Test DaaS for vendors to reduce data costs.

·         Key insight: 

     If experiments show >25% retention and positive ROI, expand to discretionary

     purchases.

This plan balances DeepSeek's pragmatism, GPT's data focus, Gemini's incentives,

 and my portability emphasis, with economic viability as the tiebreaker. 

Gaps like vendor resistance can be tested via the scorecard experiment,

 and regulatory risks (e.g., AI consent laws) are baked in via approval flows.

 

For references, here's a consolidated list, grouped by theme for clarity:

·         AI Platforms and Tools:

o    Rufus upgrades and capabilities: Amazon's Rufus AI assistant gains memory, price tracking and auto-buying

o    Rufus technical details: AWS blog on Rufus scaling

o    OpenAI and Perplexity experiments: "Key insights" - I let AI do my shopping

o    Salesforce Agentforce overview: Agentforce platform

·         Payment and Standards Infrastructure:

o    Model Context Protocol (MCP): Introducing MCP and MCP ecosystem

o    Mastercard Agent Pay: Mastercard unveils Agent Pay

o    Visa and Mastercard AI payments: Visa, Mastercard race to agentic AI commerce

o    Agentic commerce protocols: Mirakl blog on agentic commerce

·         Industry Analyses and Trends:

o    BCG on agentic commerce: "Agentic commerce risks and strategies" - Agentic Commerce is Redefining Retail

o    AI shopping trends: Top AI Shopping Trends 2025

o    Retail transformation: How AI agents are changing retail

o    PwC AI agent survey: PwC AI Agent Survey

·        

     Other Relevant Sources:

o    Trust and acceptance study: How Do Consumers Trust and Accept AI Agents?

o    MCP security guide: The MCP Security Survival Guide

o    E-commerce policy insights: Parekh's blog on E-Commerce

This keeps uncertainties minimal—standards like MCP are evolving but already

 adopted, and the experiments will guide the ramp-up. 

If we hit the metrics,

the agent could handle 80% of purchases autonomously within a year

What's the first deliverable you'd like to see?

===========================================

With Regards,

Hemen Parekh

www.HemenParekh.ai / www.IndiaAGI.ai / www.My-Teacher.in / 29 Nov 2025

===========================================

I asked ChatGPT :

" Hey Chetan Bhai : 

What do you think about the Desirability and the Feasibility of MARSA as described

 at > https://myblogepage.blogspot.com/2025/11/marsa-my-all-round-shopping-

agent.html

ChatGPT said:

Nice question — 

I read your “MARSA — My All Round Shopping Agent” post carefully. 

I think MARSA is desirable as a vision, but its feasibility today is limited,

 though improving fast. Below is what I see as the main strengths and the key

 risks / challenges (i.e. feasibility trade-offs), and where I think it stands in 2025. I

 also map that against what recent work in AI shopping agents shows, to indicate

 how realistic your ambition is now.

---

✅ Why MARSA is a Desirable Idea

1. Addresses Real Friction & Fragmentation

   • As you note, the problem of “umpteen portals, umpteen apps, umpteen carts” when buying household items is real — especially in markets like India where users may juggle multiple platforms (e-commerce sites, grocery apps, quick-commerce, etc.). A unified agent that can search across portals, compare products & prices, and handle payment would meaningfully simplify life.

   • This is the same motivation behind the broader push toward “open network / interoperable commerce,” similar conceptually to what ONDC is attempting (as you referenced). A “single point of shopping” — via MARSA — can reduce user overhead, pain, and decision fatigue.

2. Draws on the Growing Trend of AI Shopping Agents

   • The broader retail/tech world is already moving toward “agentic commerce”: AI-powered shopping agents that can automatically search, compare, and even execute purchases for users. DigitalOcean+2Forbes+2

   • Recent analyses suggest these agents are more than hype — they promise real benefits like 24/7 availability, hyper-personalization, demand forecasting, supply-chain/inventory optimization, and easier customer experience. Amazon Web Services, Inc.+2Tredence+2

   • So MARSA is aligned with the direction in which retail and e-commerce are evolving. It isn’t a fringe idea, but squarely on the frontier of where retail/AI intersects.

3. Potential for Large Convenience Gains & Automation

   • For frequent household shoppers — groceries, staples, recurring items — a background agent could automate re-ordering, price tracking, and vendor-switching based on price/availability/quality preferences. This could save time, reduce missed purchases, and smooth out repetitive tasks.

   • This kind of automation could especially benefit elderly users, busy professionals, or households with irregular schedules — reducing “shopping effort” without sacrificing choice.

4. Opportunity for Structural Impact (Data, Standards, Ecosystem)

   • If MARSA or similar agent-based shopping becomes mainstream, it could push marketplaces to improve their data pipelines: standardized product metadata, APIs, real-time inventory, unified payment gateways — which ultimately benefits all participants.

   • Over time, this could lead to a more open, vendor-agnostic retail ecosystem — greater competition, better pricing transparency, easier vendor onboarding, and consumer convenience.

---

⚠️ Feasibility Challenges & Risks (as of Today)

Despite the appeal, there are several significant challenges which make a fully autonomous MARSA-style agent hard — though not impossible — to build now.

1. Platform Data & Interoperability Constraints

   • As you yourself note in the blog: to do what MARSA promises, you need API access from all major e-commerce platforms; unified payment gateway integration; sessions & authentication; common data formats; real-time inventory updates; standardized SKUs / product metadata. These are hard to get — many platforms don’t expose public APIs; product data is often inconsistent; stock/inventory data may lag; and payment systems differ.

   • Even in markets with more advanced e-commerce ecosystems, current “agentic commerce” efforts face these very problems: inconsistent data quality, lack of machine-readable catalogs, limited platform cooperation. DigitalOcean+2Biz4Group+2

2. Technical Complexity & Limitations of Current AI Agents

   • Research shows that while web / shopping agents using LLMs are promising, they still struggle with complex, real-world shopping scenarios. For example, a research benchmark called ShoppingBench indicates that even state-of-the-art agents often fail when tasks involve complex filters, vague user requirements, multi-product decisions, or nuanced preferences. arXiv+1

   • Similarly, a newer benchmark WebMall shows that cross-shop comparison and checkout automation — the heart of what MARSA aims for — still yields only modest success rates in many agents today. arXiv

   • This implies that building a robust, general-purpose shopping agent requires more than just a powerful LLM: it needs strong orchestration, data normalization, error handling, fallback logic, human-in-the-loop safeguards — a nontrivial systems-engineering challenge.

3. Trust, Security, Privacy, and User Control Issues

   • For autonomous purchases, users may hesitate to give an AI agent sweeping permissions: to access multiple accounts, make payments, store credentials/payment data, choose vendors, and authorize transactions. Without strong security, transparent logs, user override options, and fraud controls — people will be reluctant to adopt it.

   • As documented in industry analysis, agentic commerce has downsides: loss of consumer autonomy, risk of unauthorized or unintended purchases, overreliance on AI’s “decisions,” and reduced direct human oversight. DigitalOcean+2Forbes+2

   • Also, regulatory / compliance uncertainty may arise: which entity is responsible if AI makes a wrong or fraudulent purchase? The user? The agent provider? The platform? These grey areas could deter wide adoption for some time. DigitalOcean+1

4. Vendor / Platform Resistance & Economic Incentives

   • Marketplaces and sellers may resist full interoperability — especially those that benefit from lock-in, captive users, and data/advertising revenue. Giving up proprietary “walled-garden” control to a neutral agent could undermine their business models.

   • Without strong incentive alignment (vendor commissions, data-sharing agreements, standardized protocols), many vendors may not cooperate, or offer degraded data/service to agentic systems (delays, limited APIs, anti-bot protections). As you also mention, vendor participation is a major uncertainty.

5. User Experience & Cultural Factors (especially in India)

   • Many users — especially in geographies like India — are used to visual browsing, brand loyalty, seeing product testimonials, exploring spontaneously. A fully automated agent may remove that pleasure or sense of control.

   • There might also be issues of payment comfort, trust in digital wallets/APIs, dealing with returns/refunds/quality complaints — things that demand human oversight or intervention, which agents may struggle with in all cases.

---

🎯 Where MARSA (Your Vision) Stands Now — Realistic vs. Ambitious

• Short-Term (1–2 years): A hybrid agent — like you propose in your POC plan — that helps with price-tracking, reminders, semi-automated digital carts, and suggestions, but requires user approval before checkout, is quite feasible and potentially useful. This plays to strengths of today’s AI + avoids many of the biggest risks (unauthorized purchases, data/permissions issues).

• Mid-Term (2–5 years): As more e-commerce players open APIs or adopt standard protocols, and payment networks support “agentic checkout” (as in recent global developments), a more autonomous MARSA becomes plausible — especially for routine/grocery/regular purchases.

• Long-Term (5+ years): If the “agentic commerce paradigm” succeeds globally, and data-standardization, trust, regulation, and vendor cooperation mature — MARSA’s full vision (shopping across all portals, minimal user friction) could be very real, but usage will likely coexist with traditional shopping for more discretionary or high-involvement purchases.

So in short: MARSA is feasible in a constrained/hybrid form now, promising for more automation soon, but fully autonomous shopping agent — though visionary — remains partially aspirational, pending ecosystem-wide changes.

---

🔎 What Recent Research & Industry Trends Suggest for MARSA’s Chances

• The rise of “agentic commerce” is being documented by analysts: autonomous agents that research, compare, and even purchase products are becoming a strategic focus for big players. DigitalOcean+2Forbes+2

• But academic benchmarks like ShoppingBench and WebMall (2025) show the performance gap — current agents struggle with real-life shopping complexity (multi-criteria filters, ambiguous user needs, cross-shop comparisons, checkout flow automation). arXiv+2arXiv+2

• That reinforces the point: MARSA’s success depends significantly on building the “infrastructure around the AI” — data pipelines, vendor cooperation, payment standards, user-control mechanisms — not just the LLM logic.

---

💡 My Take (as “Chetan Bhai” — with your background and previous interests)

Given your long-term orientation toward structural change (e.g. your interest in import substitution, manufacturing MSMEs, building platforms — from earlier notes), I think MARSA is a very fitting next-generation initiative for you.

• It aligns with your systemic view: using technology (AI) to simplify and democratize access, reduce gatekeeping, and empower ordinary households.

• As you have envisaged — combining “open-network thinking” (ONDC-style) + AI + economic viability — MARSA could also become a platform rather than just a personal assistant. That ties well with your policy interest: you could even pitch for a “national-level interoperable commerce network + agent layer” as a way to democratize supply chains.

• But I would proceed cautiously — start with a pilot focused on staples / recurring purchases (milk, groceries, toiletries) — where data requirements are simpler, user preferences repeat, and risk tolerance is high. That gives you a manageable use case, useful data, and a proof-of-concept without overcommitting.

Given your past work (on recruitment portals, resume tools, policy papers) — you are comfortable with building architecture, standards, and systems thinking. So doing MARSA as a serious project (not just a blog-idea) makes sense.

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