Konstantinos Leimonis

Konstantinos Leimonis

Konstantinos is as a Software Tech Lead at monday.com on the Client Foundations team, building developer tools and enabling teams with a microfrontend architecture. When he’s not architecting scalable frontend solutions or optimising developer workflows, he’s sharing microfrontend best practices, contributing to the developer tooling ecosystem, and helping teams navigate the complexities of distributed frontend architectures.
Creating Autonomous Verifiable Agentic Workflows With Human in the Loop
JSNation US 2026JSNation US 2026
Upcoming
Creating Autonomous Verifiable Agentic Workflows With Human in the Loop
Workshop
Ready to go from a plain repo to a workflow that changes code, verifies it, and merges it — on its own?AI coding demos look magical, but most stop at the pull request — and leave you wondering how to actually trust one at scale. The moment you point an agent at real repos, the questions pile up: how do you know it didn't break something? What stops a retry loop from running forever? When do you pull in a human? And who actually clicks merge? This workshop is the part the demos skip. You'll take a real repo and build the missing machinery: verification first, then an agent that does the work, proves it, and closes the loop by merging the safe changes itself.The core lesson: an agent can only automate what it can verify — and the loop isn't real until it merges.What you'll build:A repo made agent-ready — real tests, CI, and a baseline checkYour own skill, from scratch, that makes a code change and proves it didn't break anythingA verification gate that lets the agent fix only what its change broke — no false positivesA bounded execution loop that retries, then hands off to a human instead of hiding failuresAn auto-merge step that closes the loop — merging the change on green, with no human in the seatTopics covered:Splitting a task into a deterministic lane (scripts, zero model cost) and a non-deterministic lane (real judgment)Writing tests and wiring CI so the agent has eyesight into the repoCapturing a baseline and chasing only the NEW failures your change introducedAuthoring an agentic skill from scratch: what it reads, what it does, what it writes backPutting the loop on a leash — bounded retries, stable-green checks, routing failures to a humanClosing the loop with auto-merge — gate on green, merge the safe changes, escalate the restDesigning the skill so it composes into a larger fleet later — one reliable unit first, scale secondThis isn't a demo — it's a build. You'll write TypeScript, add real tests, and wire up a workflow that runs end to end: change, verify, and merge, on its own. By the end you'll have a working skill and the confidence to point it at your own codebase on Monday morning.
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From One Repo to Hundreds: Building an AI Agent Fleet for Large-Scale Code Migrations
AI Coding Summit LondonAI Coding Summit London
30 min
From One Repo to Hundreds: Building an AI Agent Fleet for Large-Scale Code Migrations
When you need to apply the same change across hundreds of repositories, manual PRs don't scale and traditional codemods can't handle the unexpected. In this talk, I'll walk through the engineering journey from building a single-repo migration skill to deploying a fleet of parallel AI agents that autonomously process repositories, fix breaking changes, and report progress — all without human intervention.

You'll learn the architecture behind cost-aware model routing, baseline comparison to avoid false positives, race-condition-free parallel execution, and risk-ordered rollout. I'll share what worked, what broke, and why a percentage of repos still needed a human.

This isn't a demo of AI writing code. It's a production playbook for running AI agents as automation at scale, applicable to any fleet-wide change that you can take back and adapt to your own fleet-wide change programmes the week after this talk.
Keeping Sane with React Micro-Frontends in Production by Applying Observability
React Summit 2022React Summit 2022
7 min
Keeping Sane with React Micro-Frontends in Production by Applying Observability
Teams are getting bigger by applying micro-frontends using React, but complexity tracking production issues can become extremely confusing when no clear responsibilities are met. That’s when observability for teams and applications on a scale becomes even more important, in order to be able to identify potential issues in production, separate the concerns and the responsibilities among them. In this presentation we’ll learn how we can achieve this.
Observability for Microfrontends
DevOps.js Conf 2022DevOps.js Conf 2022
31 min
Observability for Microfrontends
Teams are getting bigger and bigger following the micro-frontends architecture. Observability for teams on a scale becomes even more important in order not only to be able to identify potential issues in the runtime, but also separating the responsibilities. In this presentation we’ll learn how this can achieved.