Mamaev Agentic Lab
RU
RU
⚙ Agent Engineering · for business

When the promptstopsworking

I design and build production agent systems: orchestration, observability, fallback routing, recovery. Not "one big prompt" — engineering architecture with verification and traces.

→ Discovery call⬡ Open course
STATUS: MONITORING AGENTIC_FLOW
NODE_01: OK
NODE_02: OK
ORCHESTRATOR: OK
RECOVERY: OK
// services

Three engagement formats

RATIONALE: Мы не строим промпты. Мы инсталлируем автономную инженерную инфраструктуру.
SVC-SKILL-MOD-01

Audit & Spec

Map your manual processes → architectural spec for an agent system. Includes AI/Tool/Code node decomposition and ROI assessment.

Output
Implementation-ready spec
Term
~2 weeks
SVC-SKILL-MOD-02

Build & Ship

Pipeline implementation on n8n / Claude Code / custom backend. CI/CD, observability via Langfuse, LLM fallback routes, alerting.

Output
Production-ready system
Term
~6 weeks
SVC-SKILL-MOD-03

Embed Engineer

Part-time embedded agent engineer with your team. Help with design, reviews, fighting production fires.

Output
Continuous capacity
Term
monthly retainer
SVC-SKILL-MOD-04

Practice OS — for private practice

A format for solo practitioners (therapists, coaches, healers): a diagnosis of how you work with AI today — borrowed tools, personal practice, or sovereign practice — and the assembly of your Practice OS: the memory, knowledge base, helpers and rituals of your work. 1:1, in person in Nashville (The Healing Society) or remote.

Output
A working Practice OS on your own files and tasks + a plan to grow it yourself
Term
format and scope discussed on a discovery call
The manifesto: “The Assembly Point” →
// cases

What I’m already building

VIBERULER

published CLI tool

A benchmark for people who code with AI: it reads local session transcripts and measures how the work actually went. The default run makes zero network calls; before anything is submitted it prints the exact JSON and asks. Published on npm, one command to run.

stack: TypeScript · npm · CF Worker

ASTROLABE

multi-engine monorepo

Sixteen independent interpretation engines under one umbrella: deterministic calculation first, LLM explanation on top. The governance rule is strict — with no retrieved sources the model is never called, so the system cannot invent doctrine. Shared RAG layer, single gateway to the models.

stack: Python · RAG (BM25 + bge-m3) · uv workspace

ECHO

local speech-to-text

Bilingual dictation that runs on your own hardware: Russian and English, including both mixed inside one sentence. Recognition happens on-device and types straight into whatever app is in front of you — no cloud, no account, no telemetry. Windows, macOS, Linux.

stack: Whisper/Parakeet · GPU · desktop

OPENYOGA

desktop tutor over a corpus

A monorepo built around a source corpus: conversion into clean Markdown, a typed SDK for validating and querying it, and a desktop tutor that runs practices and reads lessons over that corpus. Works offline; the architecture is decoupled from any single tradition.

stack: Tauri v2 · TypeScript · npm workspaces
// process

How we work

STEP_01
Discovery call (30 min, free)
STEP_02
Workshop: task decomposition, scope, acceptance criteria
STEP_03
Spec → your team or I implement
STEP_04
Build sprints with weekly check-ins
STEP_05
Handover + observability + runbook

Let’s start with a conversation

30 minutes to understand the task and whether it makes sense to work together. No fluff, no slides.

sasha@mamaev.coach