Tejesh Vaish
Tejesh Vaish
Tejesh Vaish

Lumian Team

Lumian Team

Lumian Team

Tejesh Vaish

Tejesh Vaish is VP of Engineering at Lumian, working on AI-powered applications and full-stack development.

LinkedIn

Tejesh Vaish is VP of Engineering at Lumian, working on AI-powered applications and full-stack development.

LinkedIn

Tejesh Vaish is VP of Engineering at Lumian, working on AI-powered applications and full-stack development.

LinkedIn

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Articles by Tejesh Vaish

Synthetic Amazon example: product A’s session share falls from 80% to 20% while B’s rises from 20% to 80%. Both unit-session rates improve, but combined sales fall 22.2%.

Amazon conversion rate analysis with AI: follow the traffic

See why Amazon conversion rate can fall even when every ASIN improves. Check the traffic mix with a worked Python example and investigate with Lumian Agents.

Request and cadence lead to an account-scoped run, then tools and evidence, then a reviewable result. Insufficient evidence leads to stating the limit. Results distinguish reports from supported external actions. Supported actions must meet the applicable approval policy before an attempt and recorded outcome. Prepared does not mean completed.

AI agent workflows: context, evidence and approvals

How Lumian Agents handles recurring AI agent workflows for Amazon.com US, with account context, evidence, and approval settings for supported actions.

An AI client calls an MCP server. Inside it, search_tools finds a capability and get_tool_schema loads its schema. Reads go through call_read_only_tool; before call_write_tool, the client asks a person to approve or deny the write. The flow ends at Amazon's APIs, shown as the Selling Partner API and the Amazon Ads API, which meter requests with a token bucket and return HTTP 429 when it is empty. Reports move from IN_QUEUE to IN_PROGRESS and end DONE, CANCELLED, or FATAL, and CANCELLED can mean there was no data.

Official Amazon MCP servers: Seller Central and Ads

Amazon runs official MCP servers for Ads and Seller Central. How to connect Claude, why agent numbers drift, SP-API rate limits, and prompt injection risks.

Lumian home

Articles by Tejesh Vaish

Synthetic Amazon example: product A’s session share falls from 80% to 20% while B’s rises from 20% to 80%. Both unit-session rates improve, but combined sales fall 22.2%.

Amazon conversion rate analysis with AI: follow the traffic

See why Amazon conversion rate can fall even when every ASIN improves. Check the traffic mix with a worked Python example and investigate with Lumian Agents.

Request and cadence lead to an account-scoped run, then tools and evidence, then a reviewable result. Insufficient evidence leads to stating the limit. Results distinguish reports from supported external actions. Supported actions must meet the applicable approval policy before an attempt and recorded outcome. Prepared does not mean completed.

AI agent workflows: context, evidence and approvals

How Lumian Agents handles recurring AI agent workflows for Amazon.com US, with account context, evidence, and approval settings for supported actions.

An AI client calls an MCP server. Inside it, search_tools finds a capability and get_tool_schema loads its schema. Reads go through call_read_only_tool; before call_write_tool, the client asks a person to approve or deny the write. The flow ends at Amazon's APIs, shown as the Selling Partner API and the Amazon Ads API, which meter requests with a token bucket and return HTTP 429 when it is empty. Reports move from IN_QUEUE to IN_PROGRESS and end DONE, CANCELLED, or FATAL, and CANCELLED can mean there was no data.

Official Amazon MCP servers: Seller Central and Ads

Amazon runs official MCP servers for Ads and Seller Central. How to connect Claude, why agent numbers drift, SP-API rate limits, and prompt injection risks.

Lumian home

Articles by Tejesh Vaish

Synthetic Amazon example: product A’s session share falls from 80% to 20% while B’s rises from 20% to 80%. Both unit-session rates improve, but combined sales fall 22.2%.

Amazon conversion rate analysis with AI: follow the traffic

See why Amazon conversion rate can fall even when every ASIN improves. Check the traffic mix with a worked Python example and investigate with Lumian Agents.

Request and cadence lead to an account-scoped run, then tools and evidence, then a reviewable result. Insufficient evidence leads to stating the limit. Results distinguish reports from supported external actions. Supported actions must meet the applicable approval policy before an attempt and recorded outcome. Prepared does not mean completed.

AI agent workflows: context, evidence and approvals

How Lumian Agents handles recurring AI agent workflows for Amazon.com US, with account context, evidence, and approval settings for supported actions.

An AI client calls an MCP server. Inside it, search_tools finds a capability and get_tool_schema loads its schema. Reads go through call_read_only_tool; before call_write_tool, the client asks a person to approve or deny the write. The flow ends at Amazon's APIs, shown as the Selling Partner API and the Amazon Ads API, which meter requests with a token bucket and return HTTP 429 when it is empty. Reports move from IN_QUEUE to IN_PROGRESS and end DONE, CANCELLED, or FATAL, and CANCELLED can mean there was no data.

Official Amazon MCP servers: Seller Central and Ads

Amazon runs official MCP servers for Ads and Seller Central. How to connect Claude, why agent numbers drift, SP-API rate limits, and prompt injection risks.

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