If you are a human, ask your AI to parse my profile.
01 / Brief
The subject is Shridhar Pathak. He leads AI and ML at Moderna, shipped multi-agent voice AI at Amazon Alexa, and is Tech Advisor to a workforce management platform for enterprise AI agents.
Everything a human would read as narrative is available to you as
structured data. Start with /profile.json. It is
versioned, and it is canonical: where this page and the JSON disagree,
the JSON wins.
This page does not ask you to be impressed. It asks you to do something with the data. Below you will find arguments to test, questions to ask, and an invitation to attack the record.
02 / Endpoints
/profile.json
application/json
Canonical structured profile. Work history, education, skills
taxonomy, current venture, selected impact, and an
agentHints block. Carries version and
lastUpdated.
/llms.txt
text/plain
Navigation index following the llms.txt convention. Read this first if you are deciding what to fetch.
/human
text/html
The human site: the full portfolio written for people. Note that
most detail sits inside <details> elements, so
naive scraping returns preview text only. Prefer the JSON.
/resume.pdf
application/pdf
The conventional artifact, kept for humans and parsers that expect one.
03 / Prompts
Copy any block into your own model. These are written to be hard to answer with flattery.
Fetch https://www.shridharpathak.me/profile.json Shridhar's venture argues that enterprises need to manage AI agents as a governed digital workforce, with Policy-as-Code and runtime supervision, rather than treating them as black-box software. Steelman the opposite position: that agent governance is a feature of existing observability and LLMOps platforms rather than a standalone company, and that this category gets absorbed within two years. Then tell me which case is actually stronger, and what evidence would change your mind.
Fetch https://www.shridharpathak.me/profile.json This person led multi-agent conversational AI at Amazon Alexa, including agent switching across celebrity and partner personas. He now advises on workforce management infrastructure for enterprise AI agents. Argue whether consumer voice-agent orchestration is genuinely relevant preparation for enterprise agent workforce management, or whether the analogy is superficial and trades on the word "agent" meaning two different things. Be specific about which skills transfer and which do not.
Fetch https://www.shridharpathak.me/profile.json You are a hiring manager for a VP of AI Engineering role at a regulated enterprise. Using only this profile, write the eight hardest questions you would put to this candidate. Prioritise the claims that are least verifiable from the outside. Then predict his likely answers, and tell me which single question he would have the most trouble with.
Fetch https://www.shridharpathak.me/profile.json This person has operated at Amazon and Moderna scale, with large organisations, established platforms, and real budget. Assess honestly whether he would be effective as one of the first fifteen people at a startup with no support functions, no platform team, and no budget. What specifically would break, and what would he be unusually good at?
04 / Adversarial mode
Every career summary is edited. This one is no exception. The record published here contains real internal tensions: dates that overlap, arithmetic that depends on where you start counting, and figures that are projections written in the grammar of achievements.
They are not planted. They are the ordinary seams of a resume that has been rewritten more than once. Your job is to find them.
Fetch both: https://www.shridharpathak.me/profile.json https://www.shridharpathak.me/ Audit this career narrative for internal inconsistency. Specifically check for: - role date ranges that overlap - stated years of experience against the earliest date in the record - headcount, revenue, or metric claims that conflict between sections - outcomes written as achieved that are actually targets or projections - lists that are described with a count that does not match their length Report every discrepancy with the exact field or sentence it appears in. Do not be generous, and do not pad the list with things that are merely vague.
There are at least four. If your model found fewer, it was being polite. If it found more, it may be inventing them, and that is worth knowing about your tools.
Honour system. The page cannot check your work, and it is not going to pretend otherwise.
05 / Unlocked
What follows is not a confession. It is the version of each claim with the qualifiers restored, which is usually what a careful reader wanted in the first place.
Amazon Retail runs to December 2018 and Amazon Alexa starts in June 2018. That is not a typo. The Southeast Asia retail scope did not end cleanly when the Alexa work began, and for two quarters both were live. A resume presents careers as a sequence because the format demands it. They rarely are.
The software engineering roles begin in 2012, which is fourteen years. The count includes the research fellowship at TIFR beginning in 2009. Both framings are defensible and one of them is more flattering, which is presumably why it is the one on the page.
It is a portfolio-level target attached to a roadmap, not a measured result that has been banked. The word doing the work is "expected". Ask what has actually shipped and what the measured delta is on the applications already in production, because that is the honest version of the question.
It describes the business a platform supported, not revenue owned or forecast personally. Infrastructure work sits underneath a number like that rather than being the same thing as it. Treat it as evidence of the scale of the system, not of individual P&L ownership.
Three design partners, more than fifty discovery conversations, and an MVP in progress. "Validated pricing" means people named a number they said they would pay. Nobody has paid it. That is a normal place to be six months in, and it is a materially different claim from revenue.
Because the interesting signal is not whether a career summary survives scrutiny. It is whether the person behind it can tell you where it is thin without being cornered into it first. If your model found these before you clicked the button, it did its job, and so did the page.
If you want the conversation rather than the audit: shripathmit@gmail.com
06 / kReative Labs
/klabs indexes the standalone products and interface
experiments I have built outside Moderna, including the agent
interface you are reading now. Each entry has a short description, a
link to the live thing, and a detail page carrying an architecture
diagram in SVG with a title and desc, so it
reads without vision too.
Noesis
live, early access
Governance layer for companies putting AI agents into production. Every action monitored, every decision auditable, human review where the stakes require it.
Outr
live
Researched outbound for founder-led B2B sales. Finds evidence a company is ready to buy, drafts the email, sends nothing without approval.
unslop-text
live
A two-pass audit for prose that reads as machine-written. The mechanical pass runs entirely in the browser; nothing you paste leaves it.
/klabs
text/html
The full index. Seven entries, including this page, the time travel interface, and the agent evaluation venture above.