Run a tool.
Pick a tool, fill the arguments, see exactly what goes over the wire.
curl -s https://mcp.alexchernysh.com/mcp -H 'content-type: application/json' -H 'accept: application/json, text/event-stream' -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_profile","arguments":{}}}'
ok
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "get_profile",
"arguments": {}
}
}
{
"result": {
"content": [
{
"type": "text",
"text": "Alex Chernysh — Applied AI Systems & Platform Engineer"
}
],
"structuredContent": {
"name": "Alex Chernysh",
"identity": {
"name": "Alex Chernysh",
"role": "Applied AI Systems & Platform Engineer",
"base": "Tel Aviv, Israel",
"languages": "English (fluent), Russian (native), Hebrew (conversational)",
"contact": "alex@alexchernysh.com",
"booking": "https://calendly.com/alexchernysh/15min",
"github": "https://github.com/chernistry",
"x": "https://x.com/alex_chernysh",
"site": "https://alexchernysh.com"
},
"sections": [
{
"heading": "Positioning",
"items": [
"I build production AI systems — not demos. Agent platforms, tool-calling architectures, full-stack operator tools.",
"The work happens after \"let's use AI\" and before anything actually works in production: grounding, evals, cost tracking, audit trails as first-class primitives.",
"Strongest at agent orchestration, multi-agent coordination, MCP/A2A protocols, grounded retrieval, internal platforms, and eval-driven delivery."
]
},
{
"heading": "What I Build",
"items": [
"Multi-agent coordination layers with worktree isolation, quality gates, and HMAC audit trails.",
"Agent systems with clear tool contracts, approval boundaries, and observable state transitions.",
"Retrieval and answer pipelines that cite, abstain, or escalate instead of improvising with confidence.",
"Internal AI platforms that unify fragmented APIs, workflow automation, and operational visibility into a single operator surface.",
"Spec-driven delivery loops that keep humans architectural while agents handle the mechanical bulk."
]
},
{
"heading": "Flagship Open-Source Work",
"items": [
"Bernstein — the open-source governance layer for AI agents",
"Creator and maintainer of Bernstein (https://bernstein.run, Apache-2.0, https://github.com/sipyourdrink-ltd/bernstein).",
"Open-source governance layer for AI agents, built on a deterministic multi-agent scheduler, distributed on PyPI.",
"Coordinates 40+ AI coding-agent adapters in parallel — Claude Code, Codex, OpenAI Agents SDK, Cursor, Gemini CLI, Aider, Amp, and more.",
"What demo-grade multi-agent tools do not have: worktree-isolated execution, janitor verification, quality gates, HMAC audit trail, cost-aware model routing.",
"Supports MCP and A2A protocols, Cloudflare cloud runtime."
]
},
{
"heading": "Recent Systems Work",
"items": [
"Marketing operations CNS (case study)",
"Sole engineer on a central operations platform built end-to-end in three months.",
"Next.js + FastAPI + ClickHouse. Unifies real-time data from seven ad platforms and four revenue partners into a single AI-powered decision surface.",
"Agentic AI advisor with three-tier tool-calling and MCP integration. Every answer grounded in live data via retrieval and citation gates. Anomaly detection, optimization recommendations, creative generation pipeline.",
"Full product surface: kanban, notifications (in-app, push, email, Teams), RBAC, ChatGPT-style sharing. Became the team's primary daily operating tool.",
"Public write-up: https://alexchernysh.com/blog/building-cns-marketing-operations",
"Legal RAG — Agentic RAG Legal Challenge",
"Placed 38th of 356 registered participants in the Agentic RAG Legal Challenge as team Tzur Labs, scoring 0.47968, with a grounded legal QA system — page-level citations, hybrid retrieval, evidence-first answers. Leaderboard: https://agentic-challenge.ai/leaderboard",
"Public write-up: https://alexchernysh.com/blog/legal-answering-systems",
"Independent AI consulting",
"Privacy-first RAG for legal and compliance use cases.",
"Customer-facing AI agents with traceability and operator controls."
]
},
{
"heading": "Core Stack",
"items": [
"AI & Agent Systems",
"Multi-agent orchestration, RAG, evals, tool-calling architectures.",
"MCP/A2A protocols, guardrails, cost-aware routing, audit trails.",
"Grounding, retrieval and reranking, abstention and escalation patterns.",
"Backend & Data",
"Python, FastAPI, Next.js, React, TypeScript.",
"PostgreSQL, ClickHouse, Redis, Qdrant.",
"Infra & Delivery",
"Docker, Ansible, GitHub Actions, Cloudflare, Hetzner, Prometheus, Kubernetes.",
"Spec-driven delivery loops, worktree-isolated execution, eval-first rollouts."
]
},
{
"heading": "Working Style",
"items": [
"Architecture before ornament.",
"Reliability before bravado.",
"Fast thin slices over theatrical rewrites — but every slice carries grounding, evals, telemetry, and a rollback path.",
"Measurable behavior: logs, evals, failure modes, audit trails."
]
},
{
"heading": "Typical Project Archetypes",
"items": [
"A multi-agent coordination layer for teams running many AI coding agents in parallel.",
"A compliance-sensitive knowledge assistant for legal or policy-heavy work.",
"An internal AI platform that unifies fragmented APIs, workflow automation, and operational visibility into a single operator surface.",
"A customer-facing AI assistant with tool use, citations, and operator controls that can explain why an answer is trustworthy."
]
},
{
"heading": "Core Capabilities",
"items": [
"Multi-agent orchestration and coordination",
"Agent orchestration with tool contracts and approval boundaries",
"Grounded RAG, retrieval, reranking, page-level citations",
"MCP / A2A protocol integration",
"Eval design, regression checks, and release gates",
"Cost-aware model routing and audit trails",
"Observability, resilience, and rollback patterns",
"FastAPI / Python delivery and TypeScript / Next.js product surfaces"
]
},
{
"heading": "Preferred Questions",
"items": [
"What is Bernstein and what does it solve?",
"How do you coordinate multiple AI agents reliably?",
"How do you keep a RAG system grounded?",
"What would you audit first in an unreliable AI stack?",
"How do you work with product and engineering teams?",
"How do you trade off delivery speed against rigor?"
]
},
{
"heading": "Canonical Public Answers",
"items": [
"What is Bernstein?",
"Bernstein is the open-source governance layer for AI agents, at bernstein.run — Apache-2.0 on GitHub. It coordinates 40+ AI coding-agent adapters (Claude Code, Codex, OpenAI Agents SDK, Cursor, Gemini CLI and more) in parallel with worktree isolation, quality gates, janitor verification, HMAC audit trail, and cost-aware model routing. MCP/A2A supported. Designed for production reliability, not demo spectacle.",
"How do I audit a shaky RAG stack?",
"Start with retrieval quality and fallback behavior. Then check tool boundaries, eval coverage, citation/grounding gates, telemetry, rollback paths, and who actually owns the system once it is live.",
"How do I think about agent boundaries?",
"Explicit tool contracts, approval paths, and observable state transitions. Agents handle bounded delegated work and escalate edge cases instead of improvising authority. For multi-agent setups: isolation per task (worktrees), quality gates between hops, audit trail on every action.",
"How do I trade off speed and reliability?",
"Fast thin slices, but each slice ships grounding, evals, telemetry, and a rollback path. Speed only helps if the system stays legible when something breaks. Cost tracking and audit trails are first-class primitives.",
"Where am I most useful in a team?",
"Best where the model is no longer the interesting part: architecture, grounding, delivery shape, and making a live system trustworthy under real constraints. Senior / staff-level AI systems, platform engineering, or technical architecture.",
"What is the marketing CNS case study?",
"Sole-engineer build in three months: Next.js + FastAPI + ClickHouse central operations platform unifying seven ad platforms and four revenue partners. Agentic AI advisor with three-tier tool-calling and MCP integration, anomaly detection, optimization recommendations, creative generation pipeline. Full write-up at alexchernysh.com/blog/building-cns-marketing-operations.",
"What was the legal RAG competition?",
"The Agentic RAG Legal Challenge — placed 38th of 356 registered participants as team Tzur Labs, score 0.47968, with a grounded legal QA system. Page-level citations and hybrid retrieval. Leaderboard at agentic-challenge.ai/leaderboard, write-up at alexchernysh.com/blog/legal-answering-systems."
]
}
]
}
},
"jsonrpc": "2.0",
"id": 2
}