At GitHub Universe 2026, the company presented its strategy to position GitHub as a comprehensive end-to-end software delivery platform. The announcement reflects a fundamental shift in how development work happens: rather than developers writing every line of code, teams now work alongside AI assistants and coordinate fleets of agents. The measure of success remains unchanged—shipping completed software—but the path to that outcome has transformed.

The software development lifecycle no longer follows a linear progression from planning through coding, testing, and deployment. A code change, team request, or production alert can now initiate work at any point in the system. Developers and their agents move fluidly between stages based on what each task requires. In this model, developers set priorities, make decisions, and remain accountable for what reaches production, while agents handle implementation and verification. A continuous learning system processes outcomes from each cycle to improve subsequent iterations.

GitHub's agentic software delivery system targets three core objectives: velocity to move from concept to finished software quickly and cost-effectively; intelligence that combines platform-wide context with continuous learning to improve over time; and quality that makes completed work secure by default and catches defects before deployment.

New products and capabilities

GitHub Copilot app and multi-model orchestration

The GitHub Copilot app was designed specifically for agentic software delivery, connecting agents and tools across projects to enable directing work without managing every handoff. Beyond code generation, the app handles spec writing, design discussion facilitation, and telemetry analysis on a single agent-first interface.

Copilot Fusion introduces multi-model orchestration, automatically selecting the right AI model for each task—whether reasoning, code generation, debugging, or tool use—based on what the work requires and cost considerations. In the Artificial Analysis Coding Agent Index, Fusion Max achieved performance within two points of Opus 5.5 Max while cutting costs in half and reducing task completion time by 40%.

Project management and session continuity

Copilot projects, now available natively in the GitHub Copilot app, enable running agent work across multiple repositories in fully managed parallel sessions. A single interface tracks progress, consolidates decision-making, and directs next steps, reducing time spent managing execution and freeing developers to focus on prioritization.

Session continuity allows work to persist across devices and interfaces. A cloud-based agent session can be initiated on a phone during standup, steered from VS Code at a desk, and monitored from github.com during lunch, with the agent continuing work in the cloud. The underlying multi-model harness supporting this capability is designed for speed, efficiency, and cost control.

Automations and code review

Copilot automations enable AI-powered workflows within repositories that run in the cloud, triggered manually, by events, or on schedules. These automations can coordinate agents for tasks like issue triage and code review. A new learning capability allows automations to analyze outcomes and propose improvements for developer review and approval, embodying the continuous learning engine in practice.

Copilot code review Max addresses the review bottleneck created by higher development velocity. The new tier achieved the highest F1 score on ReviewBench for high-severity issues, while Lite and Balanced options maintain speed for routine reviews. Starting immediately, GitHub Enterprise customers gain access to all Copilot features—including the app, code review, and automations—without separate subscriptions, paying only for AI credits consumed. Self-serve purchasing will follow, and existing Copilot Business and Copilot Enterprise customers can maintain current plans or migrate when ready.

Infrastructure scaling and architecture changes

Supporting agentic development requires more than capacity expansion. GitHub has fundamentally reshaped its infrastructure architecture to handle the new volume and patterns of work that agents introduce.

  • 4 million CPU cores
  • 160 PB of high-speed storage
  • 50 Tb/s network capacity

The company is executing a multi-region deployment migration to Azure while maintaining current traffic levels for developers worldwide, with completion targeted for early next year. These investments expand capacity and enable future growth.

Git itself is undergoing architectural redesign to meet the throughput and reliability demands of the agentic era. Three principles guide the new Git architecture: simplify primitives, decouple storage from compute, and minimize coordination overhead. Similar architectural work extends across GitHub's AI gateway, the harness, and other platform components.

The scale of opportunity ahead

While development practices are evolving, developers remain central to the work. The opportunity extends beyond producing more code: it encompasses solving problems that were previously intractable. GitHub estimates that all software written to date will represent less than 1% of the software developed over the next five years. By connecting the full software delivery system, providing better developer-agent collaboration, and evolving infrastructure for what comes next, GitHub aims to unlock that potential on a unified platform.