TechLead Conf Amsterdam 2026: Adopting AI in Orgs Edition

Every engineering organisation is experimenting with AI coding assistants, but few have built production-grade LLM integrations into their core developer infrastructure. I have such an experience, and I will share real patterns from deploying an AI-powered code review system across a 400+ person engineering organisation (~200 developers) — covering a competitive evaluation of 4 tools across 18 dimensions, building a webhook-based review architecture with slash commands and auto-review, evolving context enrichment from static rules to AI-powered document selection, managing a 4-model fallback chain on Vertex AI, and measuring impact through a feedback dashboard. Attendees will leave with a battle-tested
playbook for integrating LLMs into their own engineering workflows — not as toys but as production infrastructure.

Workshop Structure
1. The Code Review Bottleneck at Scale
2. Tool Evaluation — 4 Candidates, 18 Dimensions
3. Architecture — Webhook Server & Auto-Review
4. Context Enrichment — From Path Rules to AI- Document Selection
5. Model Strategy — Migration & Fallback Chain
6. Measuring Impact — Feedback Dashboard

Every engineering leader has been told "We don't have budget" - whether for tackling technical debt, modernising legacy systems, or adopting AI tooling. The problem is rarely the idea itself. It is how we present it. We frame platform health as an engineering preference when it should be positioned as business risk.

In this interactive workshop, Viktor Didenchuk shares a battle-tested framework from leading cloud platform delivery at JPMorganChase that translates any technical investment - from incident tooling to AI adoption - into the three languages executives actually speak: Revenue Risk, Regulatory Exposure, and Operational Resilience. Through three real-world scenarios with live audience polling, attendees will practise reframing technical asks into compelling, quantified business cases that survive quarterly financial reviews.

Walk away with a repeatable playbook you can apply on Monday morning to secure budget for the initiatives your organisation needs - including AI.

AI agents are becoming part of the software development process, but most teams treat them like isolated tools rather than participants in a structured workflow. Without coordination, agent-driven development quickly turns into chaos: duplicated work, endless retries, and unpredictable delivery.

I will introduce a practical approach to organizing AI agents using principles from lean manufacturing and Kanban flow systems. By applying concepts such as pull-based work, WIP limits, and bottleneck management, engineering teams can orchestrate multiple AI agents—system analyst, developer, and tester—into a predictable software delivery pipeline.

Through a live demonstration using GitHub Projects and modern AI coding tools, I will show how agents autonomously pull tasks, move work across pipeline stages, and escalate to humans only when necessary. The result is a development workflow that reduces coordination overhead while dramatically improving throughput and visibility.