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World Congress 2026 North America • Sep 24, 2026 • Session details

Running AI-Written Software in Production

Anurag Goel , Milin Desai , Richard Rizk , Sead Ahmetović , Jared Zoneraich

How do you debug production software that no human fully understands? Learn to build the rigorous evaluation frameworks and programmatic guardrails required to safely deploy AI-generated code.

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#1 about 4 min

Tracking the exponential growth of software errors

An increase in AI-generated code directly leads to higher volumes of software errors and performance issues in production.

#2 about 3 min

Collaborating with AI agents for issue triage

Integrating AI agents into chat platforms enables automated triaging and pull request generation for incoming bug alerts.

#3 about 4 min

Deploying untested code and maintaining system understanding

The rapid generation of code via AI increases preview deployments while amplifying the risks of shipping unreviewed logic.

#4 about 5 min

Implementing end-to-end encryption for enterprise AI models

Securing tokens during inference prevents unauthorized infrastructure access and protects sensitive enterprise workloads from external breaches.

#5 about 5 min

Preparing cloud infrastructure for autonomous agent selection

Cloud providers must expose machine-friendly interfaces and robust guardrails to support agents that dynamically provision infrastructure.

#6 about 2 min

Implementing bespoke evaluation layers for AI observability

Establishing custom offline and online evaluation systems is necessary to track model hallucinations and validate code generation outcomes.

#7 about 5 min

Defining mergeability standards for AI-generated pull requests

High-quality software agents must test their own work in isolated sandboxes to ensure generated code meets human review standards.

#8 about 3 min

Moving toward application-defined compute for long-horizon agents

The rise of autonomous agents necessitates infrastructure that can dynamically provision compute resources and sustain long-running stateful tasks.

#9 about 6 min

Managing enterprise data privacy across global inference providers

Directing AI workloads to locally hosted and encrypted models mitigates data exposure and limits reliance on foreign infrastructure.

Matching moments

2:09 min

Managing the operational impact of AI-generated code volume

Milin Desai Milin Desai +1 · World Congress 2026 Europe

1:42 min

High volume of AI-generated code entering production environments

Tomislav Tipurić Tomislav Tipurić · World Congress 2026 North America

2:15 min

Shifting bottlenecks in the era of AI code generation

Thanos Baskous Thanos Baskous · World Congress 2026 North America

2:41 min

Exploring modern AI SDKs and production deployment challenges

Michał Michalczuk Michał Michalczuk · Europe 2026 Virtual

1:55 min

Shifting developer workloads and realistic AI productivity gains

Chris Heilmann Chris Heilmann +2 · LIVE

2:52 min

Moving beyond demos to build production-ready software

Seth Webster Seth Webster · World Congress 2026 Europe

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