AI agent platforms · Evaluation · MCP · AI security

Ke (Coco) Zhao

I build trustworthy agentic systems: evaluated, governed and secure.

I'm a software engineer at Amazon. I lead agent evaluation and Model Context Protocol (MCP) data access for a multi-agent platform that works with highly sensitive customer data: agents are evaluated before they ship, reach data only through governed access, and run behind guardrails. 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
Faster incident triage (3h → 3min)
60×
To launch a new MCP server, down from 2 months
2 weeks
Documents a day at 99.99% uptime
60M+
Users at the startup I co-founded
100k+

01 — Focus

What it takes to trust an agent

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, and I lead it for our multi-agent platform. A release gate in CI/CD scores every agent against ground truth before it ships. In production, a critic agent and daily signal models score live answers into one reliability score, and red-teaming finds failures before customers do.

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

  2. Govern access

    I founded and launched the MCP server that gives agents one secure, auditable way to reach highly sensitive customer data, with authentication, fine-grained authorization and data-classification controls built in. Its reusable framework cut other teams' build time for a new MCP server from two months to two weeks.

    • 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: strategy, roadmap and release gates
  • Founded the MCP platform, from first commit to production
  • Owns systems end to end: 60M+ docs/day at 99.99% uptime
  • Secures AI on highly sensitive customer data
  • Cut incident analysis from 3 hours to 3 minutes with a multi-agent system
  • Grows engineers: mentors 10+ 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

02 — Experience

Where I've worked

  1. Software Engineer · Amazon

    – Present · Seattle, WA

    Current
    • Lead agent evaluation for a multi-agent platform supporting customer service: wrote the strategy and roadmap with a group of engineers and scientists, now the shared standard for the organization's agent framework.
    • Founded the MCP platform and took it from first commit to production, hardening authorization for personal data and passing security review. AI agents across the organization now use it.
    • Architected an enterprise data platform (Lambda/EC2, OpenSearch, React) 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.
    • Led 5 engineers to turn a hackathon prototype, winner of the People's Choice Award at an internal hackathon in 2025, into a production multi-agent reporting system.
    • 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 the AI safety guardrails for LLM features and led products through multiple security reviews.
    • Set technical direction through design docs and security reviews; mentor to 10+ 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 built a toolkit that converts OpenNeuro datasets to BIDS-Incremental so they can stream into real-time pipelines (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 — 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.

The thread through all of it is measurement. My research asked whether a reading of the brain could be trusted, and my startup built behavioral assessments people could rely on. Evaluating an AI agent is the same problem with a different subject.

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

04 — 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

Web

  • React
  • Node.js
  • REST APIs
  • WebSockets

Security

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

05 — 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

06 — 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

07 — 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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