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

Mohamad Shiralizadeh

Arturo Martinez

Varun

Dominik Matt

Sanketh Kumar

Said Chatila

Naveen Chand

Sebastien HUGUENOT

Anton Zhedik

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

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

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

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

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

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

Traditional enterprise software deployment assumes a clean handoff between vendor and customer — but AI applications built on top of complex, messy operational data lakes defy this model. Early Palantir Forward Deployed Engineer (FDE) Max Helzberg makes the technical and organizational case for FDE as an integration methodology. He outlines how embedding engineers directly into customer environments enables iterative design, continuous data pipeline refinement, and co-development of AI workflows against live production data without stalling internal engineering velocity.

The Forward Deployed Engineer Model: Structural Shifts & Tech Paradigms for Enterprise AI Adoption

Max Helzberg

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

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

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.