AI consulting

AI consulting & the agentic layer

Not another AI chat. A layer that reads errors, writes fixes and opens pull requests on its own — in your stack, under your control. I build it with your team and teach them to run it.

loop engineering

I build the system that runs the agents.

Loop engineering: instead of prompting AI by hand, I build the durable loop it runs in — observed signal in, reviewed pull request out, a governance gate before anything ships.

AI doesn't write instead of your team. It works inside a context that has boundaries, memory and measurement.

autofix.runrunning

agent · autofix

autonomy: L1 → L3, rolled out, not flipped
human-in-the-loop: gates where it matters
self-hosted: your data, not someone's cloud
audited: every action logged & reversible

what i do

I build the agentic layer. And I teach your team to run it.

I don't show up with a tool and leave. I set the context AI works in reliably — instructions, boundaries, skills, MCP, measurement — wire it into your stack, and hand it to the people who'll keep working in it long after I'm gone.

01context-engineering

Context engineering

Moving from prompting to running context is a craft, not a setting. I teach the team to write instructions and AGENTS.md, hold boundaries, give the agent memory and feedback from real code. The result doesn't depend on a specific model or tool — it survives the next model swap.

AGENTS.md · skills · memory · boundaries

→Instructions, AGENTS.md and shared memory across the team
→Boundaries: AI never touches auth, payments or keys
→Processes independent of any single model or tool

02agentic-layer

Agentic layer in production

I turn repeated work into a skill or sub-agent that does it the same way every time — PR descriptions, test generation, code review, cross-codebase research. It doesn't stop at a demo: the layer ships into your workflow and runs in production with budget, concurrency and cost-tracking.

skills · subagents · cost-tracking · HITL

→Skills for repeatable tasks in your stack
→Sub-agents & parallel research, verified against code
→Runs in production — not a one-day demo

03autofix

Observability → autofix

I wire an agent to your observability. A new error lands — the agent reads the stack trace, code and tests, reproduces it, finds the cause, writes the fix and tests, and opens a pull request. You approve it. Same principle for logging and debugging: a shorter path from incident to fix.

sentry.issue → reads context → fix + tests → PR

→Reproduce, fix and test from a real stack trace
→A PR to review, not auto-merge — humans stay in control
→Same approach for logging and debugging

04mcp

Custom MCP servers

I build the MCP server through which AI understands and operates your own software — your database, internal APIs, Jira, Confluence. Deterministically, with permissions and boundaries, not screen-scraping and hoping.

mcp: db · jira · confluence · internal API

→AI ↔ your apps, data and internal sources
→Deterministic, fast, with clear boundaries
→Permissions and auditability from the first commit

05mentoring

Workshops & mentoring

Live, in your stack, I show how to run AI — tips, tricks and gotchas from real deployments, not slides. I grow a champion network that keeps the practice alive after I leave, and cover the AI-literacy duty under EU AI Act article 4.

live PoC · champion network · EU AI Act art. 4

→Live PoC in your own code, not a generic demo
→A champion network that holds the practice
→AI-literacy per EU AI Act, article 4

the foundation

Safe, governed adoption.

Under the whole layer sits governance — DORA, EU AI Act, GDPR. AI never touches auth, payments or keys. I measure what matters: cycle time, change-failure rate, time from incident to fix. No hero numbers.

DORA · EU AI Act · GDPR · boundaries over auth, payments, keys

proof

No slideware. This runs.

Three systems carrying this practice — my own products in production, built with the same loop I build for clients.

eve.dev.lovinka.com

evelive

7 agents · survives SIGKILL · 38-tool HITL control plane

eve

A self-hosted agent platform that survives a hard kill — and resumes the human approval it was waiting on.

React 19 · Node 24 · Postgres

deployik.lovinka.com

Deployik

~90 tools

→blue-green deploys
→auto-SSL · AES-256-GCM secrets
→agent-driven via MCP

Deployik

A self-hosted Go PaaS that agents can drive.

Go · chi · go:embed

fixit.app

FixItlive

Governed in-product AI · tri-stack RN / NestJS / Go

FixIt

Real-time services marketplace with an in-product AI layer.

Expo / RN · NestJS · Go

All projects

lukas_pribikPrague, Czech Republic

about

AI is a tool. The team stays yours.

For 10+ years I've built scalable, fast web applications — as a Frontend Architect and full-stack engineer, on enterprise projects and my own products. The last few years I've done something that looks like development but is a different discipline underneath: engineering the context AI works in. I strengthen teams, I don't replace them — and the same workflow I deploy for clients, I run myself every day.

10+ yrs in production · Frontend Architect / full-stack
Founder of Reservine · building FixIt
Based in Prague · in cooperation with ADF

github.com/LEFTEQ in/lukas-pribik-dev

contact

Let's map where your AI adoption has the most leverage.

A short conversation to find where an agentic layer makes the most sense — and where to steer clear. No commitment, no hype.

Write me an email→lukas.pribik@byadf.cz

or find me onGitHub·LinkedIn·Instagram·+420 735 124 376