Teams often choose an agent framework before deciding what the runtime must own. That reverses the decision—and usually adds complexity before the first workflow reaches production.

The real question is not which product has the longest feature list. Does the application need a small SDK around one tool-using loop, a harness that manages context and execution, or a framework that coordinates multiple agents and services? Each layer solves a different problem.

This session starts with the minimum anatomy of an agent: model, tools, memory, state, and an action loop. From there, we will compare the OpenAI Agents SDK, GitHub Copilot SDK, and Microsoft Agent Framework against the same workload requirements.

A single tool-using task will be mapped across all three approaches. We will compare lifecycle control, tool boundaries, persistence, observability, deployment, and lock-in. The shared scenario keeps the differences concrete and exposes where each option adds useful structure—or structure the application does not need.

Attendees will leave with a vocabulary for separating SDKs, harnesses, and frameworks, a decision matrix for comparing runtime options, and a repeatable way to choose based on workload constraints rather than product familiarity.

Start with the job. Then choose the smallest runtime that can operate it reliably.

Audience: Developers and architects selecting an agent stack.
Format: 45–60-minute comparison talk with code excerpts and one shared scenario.
Demo: The same tool-using task mapped across Claude Agent SDK, GitHub Copilot SDK, and Microsoft Agent Framework.
Materials: Talk-specific decision matrix, repository, slides, and recording are not yet published.
Vendor scope: Comparative and vendor-neutral; Azure deployment implications are identified explicitly.