Name: TechLead Conf London 2026: Adopting AI in Orgs Edition
Start: 2026-10-23T06:00:00.000Z
End: 2026-10-23T19:59:00.000Z

Lera

Violina Popova

harry whorlow

Daniel Ostrovsky

Mohamad Shiralizadeh

Albert Franquesa

Demezhan Marikov

Michał Hoffman ✨

Arash Sarfehjou

Yakovlev Eugen

eran

Arturo Martinez

Bien Marco Jorvina

Varun

Flamur Bajra

Francesco Mattia

Swetha Dakshinamurthy

Rahul Adhikari

Ahsan Ali

Alex Rembridge

Dominik Matt

Sebastien HUGUENOT

Anton Zhedik

Sanketh Kumar

Lowe

Said Chatila

Estefania León

Ciarán McKenna

Jonathan Khattak

sidrc85

Fredrik Höglund

Denis Urban 

Naveen Chand

Stop prompting. Start orchestrating. In four intense hours you'll go from using Claude Code like a faster autocomplete to commanding it like a senior engineer commands a team: engineering its context, deploying fleets of subagents, locking it down with hooks, and turning it loose on work that runs without you.Every Claude Code user hits a ceiling where the easy wins run out. The agent handles small stuff beautifully, then loses the thread on anything real. The difference between that ceiling and real mastery isn't better prompts. It's control. This workshop is about control.You'll spend the whole four hours inside CLASH, a real full-stack application, handed to you fully built so nothing stands between you and the hard parts. A serious codebase is the point: it's the only place agentic engineering shows you whether it actually holds up.The throughline is context. Treated carelessly, the context window fills with noise until the agent drifts. Treated as a resource you engineer, it becomes the biggest lever you have. From there the toolkit opens up. Repeatable work becomes a reusable Skill. Noisy, exploratory work goes to subagents that run in their own isolated context, several at once when the job allows. Hard rules become hooks the agent cannot cross. Your own systems come into reach through MCP.Then you let go of the wheel, carefully. The same agent that pairs with you can run headless in a pipeline, drive a long task to a defined finish on its own, or live inside your software through the Agent SDK. We close by setting two greenfield methodologies, Spec Kit and BMAD, side by side, so you leave knowing not just how to drive the agent but which approach fits which problem.Two ideas hold it together: context is king, and you push it, you own it. This was never about generating code faster. It's about staying in command while the agent does more.This is an advanced session for engineers, tech leads, and architects who already use Claude Code every day and want to reach the top of the curve. We move fast, and we start in the deep end.

Claude Code: Black Belt

Pawel Sawicki

2 years ago, the engineering team's structure was fairly standardized, 2-pizza cross-functional teams and an engineering manager leading the team. These days, everyone is figuring it out as they go, making adjustments and seeing what may work and what not. In this talk, Gregor will share how companies like OpenAI, Anthropic, Shutterstock and others build engineering teams in the AI era. He will also share his recommendation on the best structure for a specific type of organization.

How to Build AI-Native Engineering Teams

Gregor Ojstersek

Teams often start agent adoption by adding tools: MCP servers, API wrappers, chat interfaces in developer portals. But once coding agents move from local help into real delivery workflows, they usually fail earlier than the tool call. They read stale ownership, conflicting runbooks, missing deploy history, and policies that were written for humans. This talk introduces company memory as the missing layer for production coding agents: trustworthy, permission-aware engineering context that agents can read, cite, and act on safely. We’ll look at what belongs in that memory, how to expose it through capability registries and machine-readable tool contracts, how to use on-behalf-of identity instead of shared AI service accounts, and how to grow trust from read → recommend → act. The goal is simple: before giving agents more tools, make sure they can trust what they read.

Your Coding Agent Is Only as Good as Your Company’s Memory

Eugene Sergueev

Our telemetry dashboard was green — a bug-fixer skill firing hundreds of times a week, a test-coverage agent running on every diff, manual-tester agents driving browser test cases across products, security reviews kicking off across teams, daily active users climbing. And I still couldn't answer one question: did any of it make us better? Every usage metric was lying. This talk is how we drove real adoption of an internal AI plugin marketplace across 30+ engineering teams — hands-on workshops and embedded champions — and how we now measure what actually matters: fewer bugs, faster cycles, less busywork, not call counts.

Usage Lies - What Actually Proves an Internal AI Tool Works

Yusuf Tayman

Why Software Fundamentals Still Matter

Matt Pocock

As agents become a growing part of the software delivery lifecycle, engineers will need to develop new capabilities beyond writing code alone. In this session, IBM HashiCorp Field CTO Anna McDougall introduces the COG Model for engineering skills development in the AI era: Curation, Orchestration, and Governance. Yet while many tools and skills are changing, many of the attributes that define exceptional engineers remain consistent. So what will distinguish the best engineers in an age of AI? Which qualities become more important, which become less so, and what does engineering excellence really look like when you're no longer coding by hand? This keynote aims to answer this question while helping leaders best identify how to build great systems and develop the careers of the engineers in their teams.

New Era, New Skills, Same Old Story

Anna J McDougall

AI makes individual engineers faster - and quietly more isolated. As coding agents become everyone's default pair, the collaboration that keeps an org healthy erodes: pairing fades, context gets trapped in private chat histories, knowledge stops diffusing, teams drift into silos. Velocity dashboards look great, so nothing seems wrong - until onboarding slows and bus-factor spikes. This talk names the failure mode, explains why standard metrics miss it, and shows what leaders can do to keep teams connected as AI adoption accelerates.

Faster and Lonelier: AI, Isolation, and the Erosion of Engineering Collaboration

Daniil Mazepin

As LLMs become everyday tools for developers, the way we interview engineers must evolve. We will learn strategies to adapt technical interviews, embracing AI as a tool while still assessing judgment, critical thinking, and collaboration.

Interviewing in the Post-LLM World

Dünya Kirkali

We used to explain our bugs to rubber ducks. Now, the duck talks back, writes code, and occasionally lies to our faces. LLMs are incredible tools. They can cure "blank screen syndrome," instantly write boilerplate, and explain weird legacy code. But treating them like senior engineers is a massive trap. They hallucinate confidently, lose track of the bigger picture, and make it dangerously easy to deploy code you don't actually understand.

Beyond Rubber Ducking - Engineering in Times of AI (LLMs)

Carlos Fuentes

Every team measures the engineering part of delivery - and it's already the fastest part. We measured the whole thing, from idea to learning, using data we already had: issue changelogs, structured experiment records, engineering analytics. We reconstructed the real clocks of our delivery and learned that an idea takes almost six months to become a validated result. Engineering we could see in detail. Much of the rest we couldn't see at all: some steps leave no trace anywhere, and between the steps there are hand-offs - unstructured, owned by nobody, impossible to measure. That's where the time lives. I'll walk through the investigation, the arithmetic behind why speeding up engineering alone barely moves the needle, and what actually does: improving the whole loop. Then the model we built on that premise and are piloting now - every step has a typed input and a typed output, and inside every step there's a self-checking improvement loop: AI drafts, a human who didn't draft it judges, and the judges themselves are reviewed - including the steps owned by analysts and medical reviewers, not just engineers.

Does AI Actually Make You Faster? How We Built a Self-Improving Delivery Loop That Did.

Andrei Malyhin

AI is giving developers unprecedented autonomy. Every engineer can now create their own workflow, combining coding agents, prompts, knowledge bases, and tools in different ways. While this can dramatically increase individual productivity, it introduces a new challenge for engineering leaders: maintaining consistency across teams. In this talk, we'll explore how AI is creating new forms of organizational drift, why team alignment matters more than ever, and practical approaches for building shared AI practices without limiting innovation.

One Team, Many AIs

Pablo Ibanez

How do you know an AI system will refuse the thing it must always refuse? Today the answer is testing: ask it a thousand ways and hope the thousand-and-first isn't different. The policy in this talk has 55 variables a caller controls: that's 36 quadrillion situations! It also has a test suite of 33 unit tests, all green. This talk is about deciding the question instead of sampling it. A safety rule like "never do X without checking Y" becomes a logic circuit, and a circuit becomes arithmetic: equations whose only solutions are the situations the rule allows. Asking whether the rule can be broken turns into asking whether that system has any solution. Live: one rule proved unbreakable, with a short record of why that you can check yourself without trusting the program that found it; and two that break, each repaired and re-proved on the spot. This proves the rail, not the model.

Proving What an AI Won't Do

Fabio Dias

How do you deliver with AI and still sleep at night? By engineering trust into your delivery system. In this talk, I'll share an end-to-end AI engineering workflow we built and ran on a production codebase, redefining roles, responsibilities, quality gates, and human decision points across the entire delivery lifecycle. You'll leave with a practical framework for engineering AI workflows where reliability is designed in, trust is earned, and success becomes the default outcome, not a fortunate accident.

Trust Engineering: Replacing Hope with Reliability

Kseniia Korostelova

AI usage grows, cost follows, and leadership asks you to control it. The instinct is to reach straight for a spending cap. Our concern was that doing so would undermine the adoption we had encouraged, while the arithmetic showed that a daily limit would not control the overall budget anyway. This case study follows the design of cost governance for AI development tooling across an engineering organisation while adoption continues to evolve. Governance that has any chance of sticking depends as much on framing the policy and sequencing cost control efforts as it does on thresholds, and choosing a number is often easier than deciding what the control is supposed to achieve.

Putting a Ceiling on the Thing You Just Told Everyone to Use

Alexandru-Daniel Tufa

AI assistants now write a growing share of the code your team ships, but most review processes and quality gates were designed for human authors. Reviewing more code faster isn't a strategy. In this lightning talk, Albert Franquesa introduces Control, Evidence, Trust, a simple model for leading teams through this shift: enforce your standards before code is written, measure what AI actually contributes and what it gets wrong, and use that evidence to decide where agents can safely do more and where humans must stay in the loop. Trust in AI code isn't a feeling. It's earned with control and proven with evidence.

Control, Evidence, Trust: Leading Teams When AI Writes the Code

AI tools are everywhere in software engineering. But adopting new tools and fundamentally changing how engineering teams work are two very different things.Many teams are experimenting with AI, yet still operate around workflows designed long before these capabilities existed. What if the bigger opportunity isn't simply writing code faster, but rethinking the work itself?In this talk, we'll explore how engineering teams can move beyond AI experimentation towards more intentional, AI-assisted workflows. We'll look at the repetitive work we've learned to live with, how AI challenges traditional approaches to development and reviews, and why engineering judgment, ownership, and quality matter more than ever.You'll leave with a practical framework for identifying opportunities to rethink workflows within your own engineering team.

Beyond AI Tools: Rethinking How Engineering Teams Work

Kanika Arora

Panel Discussion: Manager or Maker? The Leadership Identity Crisis

Kevin Ball

Freddie Tibbles

Daria Stroganova

TechLead Conference

I'm attending TechLead Conference 2025 – get your free remote ticket and join me there

The Conference for Tech Leads, Staff Engineers, and Technical Eng Managers. TechLead Conf 2026 tackles two critical challenges facing technical leaders today: navigating AI adoption in organizations and reducing system complexity. Through real-world case studies from startups to Big Tech, senior engineers and tech leads will share practical insights from the trenches.Engage in discussion rooms, hallway track with experts, hands-on practical workshops, and tens of insightful talks.

TechLead Conf London 2026: Adopting AI in Orgs Edition

FocusReactive

Descope

Hygraph

Mia-Platform

Runpod

QualityClouds Norma 

FocusReactive is an&nbsp;<meta charset="utf-8" id="isPasted">engineering-led <a href="https://focusreactive.com/" target="_blank" rel="noopener noreferrer">Headless CMS Development Company</a>&nbsp;and&nbsp;<a href="https://focusreactive.com/next-js-expert-agency/">NextJS&nbsp;development experts</a>. Independent partners of Payload CMS, Sanity, Storyblok, Vercel and Shopify. Delivering&nbsp;<meta charset="utf-8" id="isPasted">Composable content platforms built for AI SEO, growing teams, and marketing velocity.

Descope is a developer-friendly customer &amp; agentic identity platform. Thousands of teams use Descope’s visual workflows &amp; SDKs to easily build signup, login, MFA, SSO &amp; more for their customer apps &amp; AI agents. <a href="https://www.descope.com/" target="_blank" rel="noopener noreferrer">Learn more</a>.

Identity journeys for customers and AI agents

Global enterprises trust Mia-Platform to securely transition from vibe coding to agentic engineering! It delivers the governance needed to scale AI securely and profitably. <a href="https://mia-platform.eu/?utm_source=conf&utm_medium=email&utm_campaign=tech_lead" target="_blank" rel="noopener noreferrer">Learn more</a>.

The Developer Platform for Agentic Engineering.

Runpod is the AI developer cloud for building, training, fine-tuning and deploying AI on one platform – without hyperscaler pricing, lock-in or infrastructure overhead! <a href="https://runpod.io" target="_blank" rel="noopener noreferrer">Learn more</a>.

Quality Clouds brings Norma to TechLead London! Norma checks every change your AI coding agent makes against your team's rules, inside Claude Code, Cursor or any MCP client. Free forever. <a href="https://qualityclouds.ai/norma?utm_source=techleadconf&utm_medium=newsletter&utm_campaign=techlead-london-2026" target="_blank" rel="noopener noreferrer">Learn more</a>.&nbsp;