Your Agent Can't Fix What It Can't See | Milin Desai, Sentry | RAISE Summit 2026

Explosion in Coding Volume and Demographics

• GitHub reports an exponential, 100x rise in both new code creation and changes to existing code.
• The developer pool has expanded beyond traditional Computer Science graduates to include hobbyists, business leaders (e.g., a CFO building a beer-rating app), and non-technical problem solvers.
• The emergence of AI coding agents is further accelerating code generation, creating a surge in software complexity and production failures.

Sentry's Role and Market Position

• Sentry serves over 200,000 software teams globally, including leading AI model companies, B2C brands, and major SaaS providers like Atlassian and GitHub.
• The platform functions as a "signal" for production issues, providing real-time data on affected users, specific OS versions (e.g., Android), and the precise line of code responsible for failures.
• Market necessity is driven by the correlation between production downtime and revenue/user retention loss.

The "Self-Healing" Software Paradigm

• Definition: A process where AI autonomously identifies, roots causes, and deploys fixes for production issues within 5–10 minutes, bypassing manual human intervention.
• Current Automation Rates:
  • 30% to 40% of production issues are currently trivial enough to be fixed fully without human intervention.
  • Another 30% to 40% involve a hybrid approach: AI performs root cause analysis and suggests fixes, which a human evaluates before deployment.
  • The remaining percentage remains human-led, with AI in an assistance role.
• Risk Mitigation: Automated fixes are subject to a "verification agent" layer to ensure correctness before deployment, acknowledging that current automation is not 100% deterministic.

Economic Efficiency and AI Costs

• Organizations are shifting from a "wild experimentation" phase (token maxing) to "deterministic loops of automation" to manage rising AI costs, which now represent 3% to 5% of engineering team cost lines for some firms.
• Sentry achieves efficiency gains by leveraging specific context (line of code, user steps) and memory, allowing tighter, more accurate loops compared to generic experimentation.
• The business model assumes that increased software velocity and ubiquity will generate more complex systems to maintain, offsetting fears that fixing bugs faster could reduce future revenue.

Competitive Landscape and Model Agnosticism

• Sentry operates as a horizontal, model-agnostic layer, competing with major model providers (e.g., Anthropic, OpenAI) by offering unmatched breadth of coverage across devices, languages (JavaScript to C++), and architectures (front-end to back-end).
• The industry trend moves away from reliance on a single model provider toward a multi-model approach, with Sentry serving as the common context layer for diverse AI environments.

Future of the Developer Role

• Skill Evolution: The role of the developer is shifting from pure coding to system-level design, critical thinking, and storytelling.
• New Requirements: Engineers must possess "taste" for design, understand OS fundamentals and scalability, and communicate effectively with users and stakeholders.
• Workforce Outlook: While fewer developers may be needed long-term due to efficiency gains, the immediate future requires developers who can adapt to smaller, multi-functional teams.

Sentry's Strategic Outlook

• The company focuses on a single persona: the developer, aiming for ubiquity across all scales, from three-person startups to billion-user enterprises.
• Future growth is tied to democratizing "self-healing software" capabilities for all builders, regardless of team size or revenue.
• The organization expects a continued acceleration in fixing efficiency as underlying AI models improve and integrated context becomes more robust.