GitHub Actions

What if the network was the sandbox? — Remy Guercio, Tailscale

Jun 1, 2026 · 24:29

Remy Guercio from Tailscale argues that standard sandboxing conflates execution isolation with access control, proposing Aperture—an LLM gateway built on Tailscale's WireGuard identity network—which gives every connection verified identity (user, tag, or group) so agents get placeholders instead of real API keys, making exfiltration impossible. Aperture provides visibility into every tool call, bash command, and MCP request without instrumentation inside the container; internally at Tailscale, bash dominates over structured tool calls. Access permissions are configured via Tailscale's grants and ACLs, supporting quotas, cost controls across providers, and webhooks for tool calls. The gateway works at the LLM layer, capturing even non-tool-call agent behaviors like direct code execution, and is available on Tailscale's free plan.

Cooking with Agents in VS Code — Liam Hampton, Microsoft

May 21, 2026 · 17:04

Liam Hampton from Microsoft demonstrates how to run three AI agents simultaneously in VS Code using GitHub Copilot: a local agent with Claude Opus for iterative unit test writing, a background agent using git work trees to build a front end from a GitHub issue with minimal oversight, and a cloud agent in GitHub Actions to add documentation and open-source files. He argues that VS Code serves as a single entry point for all three agent types—local for hands-on iteration, background for large tasks where partial involvement is acceptable, and cloud for tasks the developer doesn't need to touch. The talk walks through a live demo where the three agents work in parallel on one codebase, solving different problems (testing, UI, documentation) without interference, and explains the underlying infrastructure: cloud agents run securely in GitHub Actions with MCP servers and built-in safeguards, while local and background agents leverage VS Code's chat customizations and third-party extensions. Hampton also highlights the new modal for managing agents, custom instructions, skills, and MCP servers, positioning Copilot as a unified control plane for diverse AI workflows.

CI/CD Is Dead, Agents Need Continuous Compute and Computers — Hugo Santos and Madison Faulkner

May 13, 2026 · 18:37

Madison Faulkner and Hugo Santos (Namespace) argue that traditional CI/CD is dying because it was built for humans pushing one or two diffs a week; at agent scale, thousands of autonomous agents opening PRs cause runner saturation, cold Docker builds, cache thrash, and merge queues that behave like serialized database locks. They propose replacing PRs with intent and plan fed into an agent loop that performs fast inline validation—builds and tests in the inner loop—while humans review intent-plus-outcome instead of diffs in a premerge queue. The future they envision involves agents exploring multiple commits in parallel for the same plan (a multiverse) where the inner loop must be stateful and extremely fast to keep up with the moving tip of the repo. CI doesn't disappear but shifts into continuous enforcement of invariants and governance within the agent harness.

Ship Agents that Ship: A Hands-On Workshop - Kyle Penfound, Jeremy Adams, Dagger

Jul 27, 2025 · 1:21:00

Kyle Penfound and Jeremy Adams from Dagger demonstrated building production-minded AI agents using Dagger, a programmable delivery engine that runs containerized workflows identically on laptops and in CI. They created a workspace submodule with tools — read file, write file, list files, and run tests — and wired it to an LLM (Claude 3.5) so the agent can edit code and validate changes through the project's actual test suite. The agent runs in isolated containers for determinism and traceability, with behavior visualized via Dagger Cloud. They extended the agent to respond to GitHub issues: a label triggers GitHub Actions to run Dagger, read the issue, hand the assignment to the agent, and open a pull request. The talk emphasized that Dagger's modularity lets developers give agents precisely scoped tools while reusing existing CI workflows, providing guardrails without sacrificing flexibility.

Piloting agents in GitHub Copilot - Christopher Harrison, Microsoft

Jul 26, 2025 · 59:07

Christopher Harrison (Microsoft) demonstrates GitHub Copilot's agent capabilities for AI Engineers, arguing context is key to effective pair programming. He explains Copilot's modes—completion, chat, edit, local agent, and coding agent—and how Model Context Protocol (MCP) enables access to external tools like databases. Harrison shows how Copilot-instructions.md files provide project-specific guidance, and how coding agent runs within ephemeral GitHub Actions environments with strict security (no external access, only write to its own branch). He emphasizes that AI doesn't change DevOps fundamentals: tests still run locally and via PR workflows, and code review remains essential. The lab covers assigning issues to Copilot, configuring MCP servers, and using .instructions files for reusable patterns, with a repeat session at 3:30 PM.

Unlocking AI Powered DevOps Within Your Organization — Jon Peck, GitHub

Jun 27, 2025 · 22:13

Jon Peck, a developer advocate at GitHub, explains how organizations can unlock AI-powered DevOps by integrating tools like GitHub Copilot into their workflows, achieving up to 1.5x feature throughput and 30% average improvement in successful builds. He emphasizes starting with brownfield (existing code) rather than greenfield, using agent mode for iterative planning and narrowed context, and codifying team best practices via copilot-instructions.md files. Peck also covers governance: safety, privacy, org-wide policies, and excluding sensitive files. Autonomous AI use cases include auto-generating PR descriptions, code review, and assigning issues to Copilot, always keeping a human in the loop with isolated branches. Finally, MCP servers allow agents to interact with GitHub and other systems, automating commits and PR creation.

The Agent Awakens: Collaborative Development with Copilot - Christopher Harrison, GitHub

Jun 27, 2025 · 1:04:06

GitHub Enterprise Advocates Christopher Harrison and John Peck demonstrate GitHub Copilot's Coding Agent at the AI Engineer World's Fair, arguing that context is the key to effective AI pair programming. They explain that coding agent works by assigning GitHub issues with detailed requirements, then running inside a secure, ephemeral GitHub Actions environment with no internet access and limited write permissions. Harrison emphasizes using `copilot-instructions.md` and `.instructions` files to guide code generation, and MCP servers to access external data or APIs. He stresses that AI does not change the DevOps flow—code still requires manual review, linters, security scans, and unit tests. The session covers agent mode, edit mode, and multi-file edits, along with practical Q&A on enterprise adoption, model transparency, and managing multiple coding agents.

Collaborating with Agents in your Software Dev Workflow - Jon Peck & Christopher Harrison, Microsoft

Jun 27, 2025 · 1:04:06

GitHub's Christopher Harrison and Jon Peck explain how to collaborate with Copilot Coding Agent as a peer programmer, emphasizing that context—through clear code, comments, instructions files, and MCP servers—is key to success. They walk through a lab where Copilot works on assigned issues in ephemeral GitHub Actions environments without internet or write access to the main repo, creating draft PRs that require human review. Harrison advises against being passive-aggressive, advocating specific prompts and instructions files to guide the agent. The episode covers MCP's role in providing external tools, the need for code review and security checks even with AI, and pricing: $39.99/month for Enterprise tier with coding agent. They also highlight .instructions files for reusable patterns and note that coding agent currently supports only tools, not resources.

Case Study + Deep Dive: Telemedicine Support Agents with LangGraph/MCP - Dan Mason

Jun 22, 2025 · 1:56:13

Dan Mason of Stride presents a case study on building autonomous agents for telemedicine support, replacing a human-driven button-pushing workflow with LangGraph, Claude, MCP, and a Node.js/React/MongoDB stack, achieving roughly 10X capacity increase. The LLM-driven virtual operations associate ("Ava") assesses patient messages, updates state with anchors and scheduled messages, and passes proposals to an evaluator agent that scores confidence and complexity, escalating to humans below 75% confidence. Mason explains how treatment "blueprints" in Google Docs are read directly by the LLM instead of using RAG, enabling new treatments to be added without writing code. He details the eval system using LLM-as-a-judge via PromptFu and retry logic for tool-calling errors, and discusses trade-offs around confidence scoring, prompt caching, and model selection (Claude for steerability). The team includes two software engineers, one designer, and Mason, who wrote most LangGraph code with AI assistance (Kline).

GitHub Copilot: The World's Most Widely Adopted AI Developer Tool

Feb 6, 2025 · 29:49

GitHub Senior DevOps Advocate Dave Bernason demonstrates how GitHub Copilot has evolved from an AI code generator to a comprehensive developer assistant with chat, enterprise knowledge bases, pull request summaries, and third-party extensions, emphasizing that Copilot keeps developers in flow and requires human oversight. He shows Copilot Chat explaining code, refactoring, debugging, and generating unit tests. Copilot Enterprise adds Bing search, knowledge bases from Markdown files, and repo indexing for accurate answers, exemplified by updating a hard-coded sales tax function to use the Avalara API. Copilot Extensions integrate third-party tools like Octopus Deploy for deployment dashboards within chat. Bernason also covers prompt engineering tips—specificity improves suggestions—and highlights Copilot's support for any language, even COBOL, for modernization.

Enhancing Quality and Security in CI: Gunjan Patel

Nov 27, 2024 · 18:27

Gunjan Patel, Director of Engineering at Palo Alto Networks, presents Ghost Pilot, an AI-powered CI pipeline that outsources boring software development tasks to enable self-evolving code. Unlike real-time Copilots, Ghost Pilot operates as a 'slow system' in CI, iteratively improving variable names and code comments, then generating unit tests by first listing edge cases and personalizing them via team context files. For security, it simulates three AI roles—Red Team engineer, developer, and engineering manager—who debate identified vulnerabilities, prioritize fixes by risk and effort, and propose changes with citations. Patel shares a reusable CI template with a Bring-Your-Own-LLM option, demonstrated by catching a logical Kubernetes bug missed by static analysis tools.