World Congress 2026 Europe Jul 10, 2026 Session details

What Production Knows: Closing the Loop Between AI Agents and the Systems They Build

May Walter

Are AI tools speeding up coding but breaking production? Discover how feeding live telemetry directly to agents enables them to self-correct and autonomously debug their own code.

Pause
Mute Enter Fullscreen
#1 about 4 min

The promise and risk of AI coding agents

High-velocity AI adoption increases individual developer effectiveness but introduces severe software delivery instability.

#2 about 4 min

Blind debugging loops caused by generative coding agents

Teaching models to generate code without providing operational feedback creates insurmountable debugging backlogs for human engineers.

#3 about 2 min

Equipping agents with runtime context and telemetry data

Supplying agents with real-time operational data doubles pull request acceptance rates and enables autonomous verification.

#4 about 3 min

Shifting from reactive observability to runtime intelligence models

Transitioning from dashboards used in hindsight to proactive data inputs guides automated code generation safely.

#5 about 4 min

Automating issue triage and pull request generation flows

Feeding production alerts into agents automatically isolates root causes and suggests verified infrastructure fixes.

#6 about 5 min

Validating code changes using integrated post-deployment monitors

Integrating agents with feature flags and canary deployments enables immediate performance verification and safe rollbacks.

#7 about 4 min

Assessing code blast radius and evaluating production viability

Capturing function-level execution context ensures agents understand unintended operational consequences before merging updates.

#8 about 5 min

Preparing platform infrastructure for agentic software delivery models

Building automated validation loops requires embedding live application reality directly into the software development life cycle.

Matching moments

3:33 min

Navigating developer bottlenecks and human accountability

Werner Vogels Werner Vogels +1 · World Congress 2026 Europe

2:34 min

Balancing developer autonomy with the adoption of coding agents

Clemens Wasner Clemens Wasner +4 · World Congress 2026 Europe

1:55 min

Shifting developer workloads and realistic AI productivity gains

Chris Heilmann +2 · LIVE

1:16 min

Closing the development loop with automated AI incident responses

Christian Heilmann Christian Heilmann +3 · World Congress 2026 Europe

2:06 min

Rethinking team structures around AI agent capabilities

Mike Mike · World Congress 2025

4:08 min

Transitioning software engineering teams to AI-native development workflows

Florian Deter Florian Deter +4 · World Congress 2026 Europe

Upcoming sessions on this topic

Open session

World Congress 2026 North America

September 24, 2026 · 11:10–11:15

Outdoor Stage

Architecting the 100X SDLC: Building Production Trust into AI-Assisted Delivery

Ranjan Parthasarathy

Founder, CPTO/CEO at AXIOMSTUDIO.AI

Ranjan Parthasarathy
Open session

World Congress 2026 North America

September 24, 2026 · 14:50–15:20

Mainstage

Running AI-Written Software in Production

Anurag Goel, Ivan Burazin, Milin Desai

Anurag Goel
Ivan Burazin
Milin Desai
Open session

World Congress 2026 North America

September 25, 2026 · 15:45–15:55

Outdoor Stage

Closing the Visibility Gap: Lessons from Safety Critical Agentic Systems

Vivek Pandit

Frontier AI Lead at Turing

Vivek Pandit
Open session

World Congress 2026 North America

September 25, 2026 · 14:10–14:40

Stage 1

The Broken Rung: How AI is Rebuilding Software Development from the Ground Up

Tomislav Tipurić

Chief Technology Officer, Nephos

Tomislav Tipurić
Open session

World Congress 2026 North America

September 24, 2026 · 16:50–17:20

Mainstage

Building AI Products vs. Building With AI

Aparna Dhinakaran, Rukmini Reddy, Tamar Bercovici

Aparna Dhinakaran
Rukmini Reddy
Tamar Bercovici
Open session

World Congress 2026 North America

September 25, 2026 · 14:50–15:20

Tech Leaders Stage

Keeping Code Quality at AI Speed

Daksh Gupta, Arthur Hicken, Brian Mann

Daksh Gupta
Arthur Hicken
Brian Mann