Amazon AI agents
I built and implemented a team of Amazon agents supported by proprietary product selection and profitability assessment logic. I combine analysis of offers, inventory, costs, and advertising to identify priority areas.
Original product selection logic
This is one of my larger projects. I developed algorithms that select products for further analysis and determine an appropriate next step. The selection rules combine stock availability, listing status, profitability and advertising data to identify products worth developing, improving or discontinuing.
Which product should I consider?
Selection starts with stock availability, whether the listing is buyable and whether cost data is sufficient for assessment. A listing with a sales issue is passed to Listing & SEO analysis. A product being considered for advertising also needs a profitability check. Recommendations are assessed for a specific product, account and market.
Profitability calculated from costs
I prepared a calculation model that accounts for price, VAT, purchase cost, Amazon fees, FBA fulfilment costs, advertising and returns. Currency conversions use the market currency and specified exchange rate. Missing, outdated or unconfirmed cost data blocks financial assessment.
Priorities tailored to the situation
I implemented rules that direct agents towards relevant issues and opportunities. If a product is in stock but its listing cannot be purchased, the first step is to diagnose the restriction. A product being considered for promotion needs a stock and profitability assessment. These rules prioritise the analysis and recommendations.
Agents collaborate on one decision
Amazon Manager combines findings from Listing & SEO, Ads, Pricing & Margins, Account Health, and Returns & Service specialists, with research providing context. A conflict check flags price and advertising proposals affecting the same product. The combined assessment accounts for the relationship between price, selling costs and margin.
Calculations, justification and control
Calculation rules check costs, sources, data freshness and permitted changes. AI helps analyse and explain proposals. Recommendations remain linked to their source data, while unknown or restricted account states can block further assessment. Price limits, budget limits and human approval govern the move from proposal to action.
Working returns agent
I implemented an agent to support Amazon returns: reviewing the request and order details, preparing the GLS label after human approval, and forwarding the label and tracking number to the appropriate case. The process includes data verification and confirmation of completion. The refund decision remains a separate step.
An operational team of agents
The Amazon agent team is deployed and operational. I combined product selection, specialist analysis, Manager decisions and returns handling into one workflow. The system uses real account data; actions that require approval take place only after a person approves them.
Scope of implementation
- Proprietary product qualification logic: availability, offer readiness, profitability and data quality.
- Calculates the result after purchase costs, Amazon fees, advertising and returns, taking into account the market currency.
- Amazon Manager coordinating agents for Listing & SEO, Ads, Pricing & Margins, Account Health, and Returns & Service.
- Ready returns agent: handling the request, preparing the GLS label after approval and forwarding it to the appropriate case.
- Research as a source of additional context, proposals with justification and decision history.
- Proposal conflict control, price and budget limits, and human approval of changes.
The entire Amazon agent team is ready, deployed, and operational—including the returns agent. The modules work together in a single process, and operations requiring approval are performed after human approval.
The brand name is withheld on request. This description explains the work without disclosing customer data or internal materials.
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