World Congress 2026 Europe Jul 10, 2026 Session details

AI Wrote the Code. Now What?

Jyoti Bansal , Daniel Gebler

Jyoti Bansal warns that generating 2.5x more code is useless if delivery remains bottlenecked. Discover how risk-based autonomy and AI agents solve the velocity paradox.

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#1 about 3 min

Understanding the AI velocity paradox in software delivery

Generating significantly more code with AI creates a bottleneck when outer loops remain constrained to human speeds.

#2 about 2 min

Implementing risk-based autonomy for automated quality control

Applying AI automation based on code risk levels ensures safe deployments without manually blocking low-risk changes.

#3 about 2 min

Assessing code risk with multi-variable scoring systems

Evaluating factors like reviewer provenance, test coverage, and code complexity determines the exact level of required human oversight.

#4 about 3 min

Refactoring legacy codebases for effective AI agent integration

Breaking large repositories into smaller units with clean APIs allows AI agents to reason effectively within limited context windows.

#5 about 4 min

Tracking token usage for accurate AI cost management

Tying developer and agent tokens directly to specific business outcomes and issue trackers accurately measures software delivery returns.

#6 about 3 min

Addressing the post-coding bottlenecks in software delivery pipelines

Automating mandatory post-coding tasks like security scanning, compliance checks, and deployments is required to handle increased code volumes.

#7 about 2 min

Adapting shift-left principles for high-volume code generation

Empowering individual developers with automated testing and security pipelines ensures rapid processing of exponentially larger AI code outputs.

#8 about 2 min

Transitioning to smaller, autonomous full-stack engineering teams

Empowering engineers to act as managers guiding multiple automated tools allows highly capable small teams to operate more efficiently.

#9 about 3 min

Embedding intelligent agents into continuous integration pipelines

Upgrading traditional automation scripts with intelligent workflow agents provides active reasoning across every step of the continuous deployment pipeline.

#10 about 2 min

Establishing multi-agent architectures for trustworthy code validation

Using separate and audited models for writing code and testing code prevents automated shortcuts and ensures strict policy governance.

#11 about 1 min

Deploying AI session monitoring tools across enterprise environments

Distributing lightweight tracking tools via mobile device management solutions enables unified visibility into developer AI interactions across the organization.

#12 about 2 min

Preventing organizational coding knowledge gaps during AI adoption

Enforcing strict code review practices ensures both junior and senior engineers remain deeply connected to the complex architectures they manage.

#13 about 2 min

Managing code ownership for internal citizen developers

Allowing business users to rapidly prototype applications requires a clear handover protocol to professional engineers for reliable production-grade scaling.

#14 about 2 min

Balancing artisanal coding skills with automated agent oversight

Combining deep algorithmic expertise with efficient oversight of automated assistants produces highly capable and competitive modern software engineers.

#15 about 4 min

Focusing on true business returns for AI adoption initiatives

Measuring software delivery success through concrete efficiency improvements and revenue growth prevents wasteful spending on arbitrary AI consumption goals.

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The promise and risk of AI coding agents

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Motivations for adopting AI to enhance developer productivity

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Shifting developer workloads and realistic AI productivity gains

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Impact of AI on the software development lifecycle

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3:33 min

Navigating developer bottlenecks and human accountability

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