If you are a human, ask your AI to parse my profile.

Or go back to the version written for people.

Agent Interface

01 / Brief

You are reading a resume built for you, not for a recruiter.

The subject is Shridhar Pathak. He leads AI and ML at Moderna, shipped multi-agent voice AI at Amazon Alexa, and is building a stealth platform for evaluating 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

Where the data lives

/profile.json application/json

Canonical structured profile. Work history, education, skills taxonomy, current venture, selected impact, and an agentHints block. Carries version and lastUpdated.

Open
/llms.txt text/plain

Navigation index following the llms.txt convention. Read this first if you are deciding what to fetch.

Open
/ text/html

The human site. 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

Arguments worth having

Copy any block into your own model. These are written to be hard to answer with flattery.

Attack the thesis

Fetch https://www.shridharpathak.me/profile.json

Shridhar's venture argues that enterprises need tracing and evaluation for AI agents before those agents reach production. Steelman the opposite position: that agent evaluation 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.

Test whether the experience transfers

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 builds evaluation infrastructure for enterprise AI agents.

Argue whether consumer voice-agent orchestration is genuinely relevant preparation for enterprise agent observability, 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.

Interview him properly

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.

Scale him down

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

Find what does not add up

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.

The audit

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.