Choosing the right AI model shouldn’t feel like roulette. We sit down with Kate Catlin, the product manager from GitHub. Kate shares how model strengths and explains how auto mode aims to pick the right model for the job so developers can focus on outcomes.

We dig into practical tactics that cut through hype: start with a golden dataset, run evaluations early, and keep refining with real user prompts once your AI is live. If you’re overwhelmed by weekly model releases, you’re not alone—Kate outlines how to compare new options with scoring and selective manual review.

She also tackles the enterprise challenge: slow model approvals that leave teams on outdated systems. With a disciplined eval pipeline, organisations can safely adopt newer, faster, and often cheaper variants that deliver better results.

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Build an agent that understands intent, finds the right data, and takes action across your CRM—without wiring a maze of connectors. We sit down with Microsoft MVP and functional architect Nathan Rose to explore how Dataverse and Model Context Protocol in Copilot Studio turn instructions into outcomes, shrinking complexity and unlocking reliable, low‑code automation.

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We walk through a plan-first workflow that asks Copilot to survey the codebase, propose a step-by-step solution, and only then switch to agent mode to implement. That simple change lifts code quality, reduces rework, and keeps diffs aligned with architecture. We also dig into custom agents with scoped permissions and roles: a testing agent for unit coverage, a docs agent for READMEs, a refactor agent limited to certain directories. Prompt craft matters, but stable configuration, coding standards, and CI guardrails matter more. Think of it as turning best practices into reusable instructions that AI can follow every time.

Johan shares field notes from consulting and charity projects: where AI saves hours, where it still stumbles, and how code generation quality has improved over the past year. We explore SpecKit’s promise for scaffolding and simpler apps, acknowledge its preview status and quota costs, and outline how to adopt incrementally in legacy systems. Along the way, we cover open source tracking via changelogs and issues, integrating agents into CI/CD, and designing a workflow that is auditable, secure, and team-friendly.

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