Put an Agent Inside Your App in 10 Minutes or Less with the GitHub Copilot SDK - Daniel Ward
This talk demonstrates how to rapidly embed custom AI agents into existing applications using the GitHub Copilot SDK. The session covers agent architecture—defining an agent as an LLM augmented with tools and a looping mechanism—and provides two coding demos (in C#) showing basic functionality like weather queries, streaming responses, and creating custom tools. For build engineers, the focus is on advanced use cases such as automated incident response (generating suggested fixes and opening Pull Requests from production logs) and building comprehensive daily briefing systems that integrate multiple enterprise services (Jira, Teams, GitHub).
Key takeaways
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Agent Architecture
17:53
An AI agent is fundamentally an LLM combined with external tools and a looping mechanism. The LLM handles the creative analysis, while the tools allow it to perform deterministic actions (e.g., fetching web data or querying APIs).
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Copilot SDK Functionality
3:35
The Copilot SDK is an open-source library that allows calling GitHub Copilot (or other LLMs like Anthropic/OpenAI) from code. It supports multiple languages (TypeScript, Python, Go, C#, etc.) and provides granular control over sessions, including event handling (`onEvent`) for streaming responses.
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Automated Incident Response
51:01
A powerful use case is connecting production errors to automated fixes. The process involves the SDK collecting logs, using Copilot to suggest a fix based on stack traces, and then programmatically opening a draft Pull Request (PR).
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Reliability vs. Prompting
53:21
For repeatable, mission-critical workflows (like daily reports), using deterministic code with the SDK is significantly more reliable than relying solely on prompt chaining, as failures in a chain compound exponentially.