Making every token count: Scaling GitHub Copilot efficiently

Token optimization: How to scale GitHub Copilot efficiently

In the new era of usage-based AI billing, token efficiency has shifted from a minor optimization to a critical skill for developers, platform teams, and engineering leaders. As GitHub Copilot scales across enterprise environments, understanding the mechanics of context windows, model selection, and agent quality is essential for maintaining high developer velocity without inflating operational costs.

Join us for a deep dive into the "token economy" of GitHub Copilot. We will move beyond the misconception that more context always equals better output, exploring how to design leaner, smarter AI workflows that maximize both ROI and productivity.

What you will learn:

• The mechanics of token consumption: understand context windows, cached input tokens, and how compounding errors affect multi-step agent flows.

• Cost vs. performance optimization: how to select the right models and leverage custom agents and skills for specific development tasks.

• Practical pre-filtering & guardrails: actionable techniques to implement deterministic guardrails and reduce unnecessary token expenditure.

• Maximizing Copilot efficiency: best practices for refining agent instructions to deliver higher-quality outputs with fewer tokens.

Who should attend?

• CTOs, CIOs & VPs of Engineering looking to scale AI adoption responsibly and control usage costs.

• Platform Engineers & DevOps Leaders designing enterprise AI infrastructure and workflows.

• Software Architects & Lead Developers wanting to master GitHub Copilot prompt techniques and agent orchestration