AI agent platforms · Evaluation · MCP · AI security

Ke (Coco) Zhao

I build AI agents people can trust: evaluated, governed and secure.

I'm a software engineer at Amazon who builds the platforms that make AI agents trustworthy: rigorously evaluated, limited to governed data access, and secure enough for highly sensitive customer data. I lead agent evaluation and Model Context Protocol (MCP) data access for the multi-agent platform that helps customer service understand the voice of the customer. 6+ years shipping production software: 4 at Amazon, 2 as a startup co-founder.

What I do Experience
Portrait of Ke (Coco) Zhao
Seattle, WA
Years shipping software
6+
Documents processed daily
60M+
Platform uptime
99.99%
Faster incident triage (3h → 3min)
60×

01 — Focus

I build AI agents people can trust

A demo agent is easy. An agent a business can rely on needs evidence it works, a strict limit on what it can access, and guardrails for when things go wrong. That is the part I own.

  1. Evaluate

    Evaluation is the center of a trustworthy agent ecosystem. I lead that workstream for our multi-agent platform: release gates, LLM-as-judge monitoring, and red-teaming, so we ship on evidence instead of demos.

    • LLM-as-judge
    • Release gates
    • Red-teaming

  2. Govern access

    I lead the MCP platform that gives agents one secure, auditable way to reach highly sensitive customer data, with authentication, fine-grained authorization and data-classification controls built in.

    • MCP
    • AuthN / AuthZ
    • Data classification

  3. Guard

    I build the guardrails that run in production: prompt-injection defense, PII detection and scrubbing at >99.9% accuracy, and hallucination monitoring. I've taken AI products through multiple security reviews, and I'm adding formal security training as an NYU Cyber Fellow.

    • Prompt-injection defense
    • PII protection
    • Threat modeling

At a glance

Trustworthy AI agents and AI platforms, end to end

  • Leads agent evaluation and the MCP platform
  • Owns systems end to end: 60M+ docs/day at 99.99% uptime
  • Secures AI on highly sensitive customer data
  • Grows engineers: mentors 5+ and sets direction through design docs and security reviews
  • Co-founder of a startup that grew from 0 to 100k+ users and was acquired
  • Researcher: 8 papers, 249+ citations
Get in touch LinkedIn

02 — Experience

Where I've worked

  1. Software Engineer · Amazon

    – Present · Seattle, WA

    Current
    • Lead the agent evaluation workstream for a multi-agent platform supporting customer service: release gates, LLM-as-judge monitoring, and red-teaming, so agents ship on evidence instead of demos.
    • Lead the MCP platform that helps that multi-agent platform understand the voice of the customer: MCP servers, tools and agent skills that give AI agents across the organization one secure, governed way to reach highly sensitive customer data.
    • Architected a full-stack enterprise data platform (React, Lambda/EC2, OpenSearch) processing 60M+ documents daily from 20+ sources at 99.99% uptime; serves 10k+ users with <1s p95 latency across 600k+ daily requests.
    • Designed a multi-agent AI system on Claude (Bedrock) with MCP for agent coordination and RAG/GraphRAG for retrieval; its incident-analysis agent cut investigation time from 3 hours to 3 minutes.
    • Built real-time ingestion (SNS/SQS/Kinesis) with <5 min latency, and REST APIs with OAuth, rate limiting and access controls serving 100+ downstream teams.
    • Shipped AI safety guardrails: prompt-injection defense, PII detection and scrubbing (>99.9% accuracy), hallucination monitoring; led products through multiple security reviews.
    • Set technical direction through design docs and security reviews; mentor to 5+ engineers; work across science, product and engineering.
    • Agent evals
    • MCP
    • Claude / Bedrock
    • RAG / GraphRAG
    • Python
    • Java
    • TypeScript
    • React
    • Lambda
    • OpenSearch
    • DynamoDB
    • Kinesis

  2. Co-founder & Software Development Lead · ForeverBrainTech

    – · Shanghai (remote)

    Acquired
    • Architected a full-stack SaaS behavioral-assessment platform (React, Node.js, PostgreSQL) that scaled to 100k+ users across 5 provinces, designed for 10x horizontal growth.
    • Built the ML data pipeline end to end, from real-time behavioral data collection to analysis to automated reports, working alongside data scientists and psychologists.
    • Grew engineering from 0 to 10+ engineers. Acquired by HengNao Tech in summer 2022.
    • React
    • Node.js
    • PostgreSQL
    • ML pipelines
    • SaaS

  3. Research Assistant · UPenn CNDS · Princeton Computational Memory Lab · Penn Medicine

    – · Philadelphia, PA

    • Contributed to the open-source RT-Cloud framework: redesigned its cloud architecture for real-time fMRI neurofeedback and integrated OpenNeuro datasets (NeuroImage, 2022).
    • Built a full-stack remote research platform (React, Node.js, MongoDB) that served 10k+ participants during COVID-19, keeping multi-site studies running while labs were closed.
    • Built computational models with PyTorch and PsyNeuLink; co-authored 8 peer-reviewed papers.
    • Python
    • PyTorch
    • fMRI
    • React
    • MongoDB
    • AWS

03 — Selected work

Things I've built

Amazon · CurrentLeading

MCP data-access platform for AI agents

A Model Context Protocol layer that gives any AI agent one secure, governed interface to highly sensitive customer data. It powers a multi-agent platform that helps customer service understand the voice of the customer, with tools for exploring and summarizing customer insights and agent skills that chain those tools into full deep dives. Authentication, authorization and data-classification controls are built in.

  • MCP
  • Agent skills
  • LLMs
  • Security
Amazon · CurrentLeading

Agent evaluation

Evaluation is the center of a trustworthy agent ecosystem, so I treat it as core infrastructure for our multi-agent customer-service platform: release gates before launch, LLM-as-judge monitoring in production, and red-teaming to find failures before customers do.

  • LLM-as-judge
  • Release gates
  • Red-teaming
Amazon

AI safety guardrails

Production guardrails for LLM features: prompt-injection defense, PII scrubbing at >99.9% accuracy, hallucination monitoring and automated security controls.

  • Python
  • LLMs
  • Security
Amazon

Multi-agent incident analysis

Specialist agents on Claude (Bedrock), coordinated over MCP, with RAG/GraphRAG retrieval. Incident triage went from 3 hours to 3 minutes.

  • Claude / Bedrock
  • MCP
  • GraphRAG
Amazon

Enterprise data platform

React + Lambda/EC2 + OpenSearch, fed by 20+ sources. Handles 60M+ documents a day at 99.99% uptime with <1s p95 latency for 10k+ users.

  • React
  • AWS
  • OpenSearch
  • Kinesis
ForeverBrainTech · Acquired

Behavioral-assessment SaaS

Co-founded and architected the product: 100k+ users across 5 provinces, an end-to-end ML reporting pipeline, and a team grown from 0 to 10+ engineers.

  • React
  • Node.js
  • PostgreSQL
  • ML
UPenn

CogBrain remote research platform

A web platform that served 10k+ research participants during COVID-19, with multi-site studies and real-time data aggregation.

  • React
  • Node.js
  • MongoDB
Princeton · Open source

RT-Cloud: real-time fMRI

Cloud architecture for BrainIAK's real-time fMRI neurofeedback framework, published in NeuroImage (2022).

  • Python
  • Cloud
  • fMRI
View project
Open source

OpenNeuro → BIDS-I toolkit

Downloads OpenNeuro datasets and converts them to BIDS-Incremental so they can be streamed into real-time fMRI pipelines.

  • Python
  • BIDS
  • Neuroimaging

04 — About

From neuroscience labs to production AI agents

Hi, I'm Coco. I grew up in Wenzhou, China, moved to Philadelphia at 16, and graduated Magna Cum Laude from the University of Pennsylvania with a double major in Computer Science and Cognitive Science.

At Amazon I work on the parts of AI agents that decide whether people can trust them: how agents are evaluated, what data they can reach, and what stops them when something goes wrong. The data involved is highly sensitive customer data, so security and governance are part of every design.

Before Amazon I co-founded ForeverBrainTech, a behavioral-assessment startup that grew to 100k+ users and was acquired in 2022, and spent four years doing neuroimaging research at UPenn, Princeton and Penn Medicine, which led to 8 peer-reviewed papers.

Outside of work

  • 🎹 Piano
  • 🧩 Rubik's Cube
  • ✈️ Travel
  • 🔢 Sudoku
Role
Software Engineer, Amazon
Specialty
Trustworthy AI agents: evaluation, governed data access (MCP), AI security
Experience
6+ years shipping production software: 4 at Amazon, 2 as a startup co-founder
Location
Seattle, WA
Education
BA CS & Cognitive Science, UPenn (Magna Cum Laude) · MS Cybersecurity, NYU (in progress)
Research
8 peer-reviewed papers · 249+ citations · h-index 8

05 — Toolkit

What I work with

Trustworthy AI

  • Agent & LLM evaluation
  • LLM-as-judge
  • Red-teaming
  • Guardrails
  • Prompt-injection defense
  • PII protection
  • Data governance

AI / ML

  • MCP
  • Multi-agent systems
  • LLMs (Claude, Bedrock)
  • RAG / GraphRAG
  • Agent skills & harnesses
  • PyTorch

Languages

  • Python
  • Java
  • TypeScript
  • JavaScript
  • SQL

Backend & cloud

  • AWS Lambda
  • EC2
  • API Gateway
  • S3
  • SageMaker
  • Spring Boot
  • Kubernetes
  • CI/CD

Data

  • OpenSearch
  • DynamoDB
  • SNS / SQS / Kinesis
  • Real-time streaming
  • ETL

Frontend

  • React
  • Node.js
  • REST APIs
  • WebSockets

Security

  • AuthN / AuthZ
  • Fine-grained access control
  • Threat modeling
  • Security reviews

06 — Research

Publications

Cognitive neuroscience research on decision-making, sleep and brain imaging, plus one NLP paper. 8 peer-reviewed papers, 249+ citations, h-index 8 (Google Scholar). Click Cite to copy APA or BibTeX.

  1. 2023

    Author as Character and Narrator: Deconstructing Personal Narratives from the r/AmITheAsshole Reddit Community

    Giorgi, S., Zhao, K., Feng, A. H., & Martin, L. J.

    AAAI ICWSM, 17, 233–244

    DOI
  2. 2022

    RT-Cloud: A cloud-based software framework to simplify and standardize real-time fMRI

    Wallace, G., Polcyn, S., Brooks, P. P., Mennen, A. C., Zhao, K., Scotti, P. S., … Norman, K. A.

    NeuroImage, 257, 119295

    DOI
  3. 2022

    Cerebellum anatomy predicts individual risk-taking behavior and risk tolerance

    Quan, P., He, L., Mao, T., Fang, Z., Deng, Y., Pan, Y., Zhang, X., Zhao, K., … Rao, H.

    NeuroImage, 254, 119148

    DOI
  4. 2021

    Impaired vigilant attention partly accounts for inhibition control deficits after total sleep deprivation and partial sleep restriction

    Mao, T., Dinges, D., Deng, Y., Zhao, K., Yang, Z., … Rao, H.

    Nature and Science of Sleep, 13, 1545–1560

    DOI
  5. 2021

    Neural mechanisms underlying breast cancer related fatigue: A systematic review of neuroimaging studies

    Arya, N., Vaish, A., Zhao, K., & Rao, H.

    Frontiers in Neuroscience, 15, 735945

    DOI
  6. 2020

    Altered spontaneous brain activity in obsessive-compulsive personality disorder

    Lei, H., Huang, L., Li, J., Liu, W., Fan, J., Zhang, X., Xia, J., Zhao, K., Zhu, X., & Rao, H.

    Comprehensive Psychiatry, 96, 152144

    DOI
  7. 2019

    High sensation seeking is associated with behavioral and neural insensitivity to increased negative outcomes during decision-making under uncertainty

    Xu, S., Luo, L., Xiao, Z., Zhao, K., Wang, H., Wang, C., & Rao, H.

    Cognitive, Affective, & Behavioral Neuroscience, 19(6), 1352–1363

    DOI
  8. 2019

    Test-retest reliability of cerebral blood flow for assessing brain function at rest and during a vigilance task

    Yang, F. N., Xu, S., Spaeth, A., Galli, O., Zhao, K., Fang, Z., … Rao, H.

    NeuroImage, 193, 157–166

    DOI

07 — Education

Education

In progress

NYU Tandon School of Engineering

MS, Cybersecurity

– Present

Security architecture, threat detection and risk management for systems built around AI.

Cyber Fellow Scholarship

Completed

University of Pennsylvania

BA, Computer Science & Cognitive Science

Graduated

Double major covering AI, algorithms, data systems, perception, decision-making and neuroeconomics, with four years of research in neuroimaging labs alongside.

Magna Cum Laude

08 — Contact

Let's talk

I'm always happy to talk with fellow builders about trustworthy AI agents, agent evaluation, MCP and AI security.

cocozhao321@gmail.com

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