You already pay for AI Agents. Most teams use about a fraction of what it can do. The teams that figured out the rest are shipping faster, reviewing less, and trusting AI changes more, because their agents start with repo memory, follow team rules, work from issues, run the CLI, open reviewable PRs, and learn from CI failures. We run the same task twice on stage. Run one is what most teams ship today. Run two is what agent-native teams ship now. You leave with the small set of habits and config files that close the gap with things you didnt know your agent could do.

The most dangerous gap in 2026 is not AI adoption. It is underuse. Developers already use agents, but many teams still dont utilize it to its fullest potential, brief the task, paste logs, ask for tests, inspect the pull request, repeat tomorrow. Meanwhile, agent-native teams are building a second gear where your agent springs into life and starts with Plan, repo memory, follows team rules, works from issues, moves effortlessly through the CLI, opens reviewable PRs, auto review and fixes them as well while learning from past failures.

This workshop is built as a reveal of using the latest unknown features. We start with a normal agent session that works, but feels disposable: no repo rules, no shared task pattern, no clean path into review. Then the hidden pieces snap into place.

First, repository instructions change the default answer before anyone types a prompt.
Second, custom agents and skills, turn "help me" into a repeatable role: Planner, Implementor, reviewer, tester, performance.
Third, Agent moves into the terminal through CLI workflows, reading files, running checks, and explaining failures in the same loop.
Fourth, issue context turns a vague ask into a scoped handoff.
Fifth, pull request review makes AI-generated changes easier to inspect.
Sixth, Actions checks give the agent a reality check grounded in CI output, not vibes.
The wow moment is replaying the same task.

Run one: capable.
Run two: repo-aware, issue-aware, terminal-backed, reviewable, and CI-grounded.

Attendees learn things they can apply immediately:

how to give their Agent durable repo context with skills, prompts, custom mcp servers, and agents;
how to connect Agents across VS Code, Codespaces, GitHub, and the terminal; and how to use Agent enabled PR reviews and auto build / merge fixes so AI-assisted changes stay understandable and owned by humans.

Materials include a live Codespaces with all the dependencies such as repo, VS Code, CLI, Actions workflows. skills, agent definitions & mcp servers.

The leap is not a giant platform rebuild. It is a handful of these little things (some would be features that you never knew existed) targeted things & habits that compound: one instruction file, one focused agent, one custom skill, one CLI habit, one review workflow, one CI feedback loop. Everyone is talking about agents; this session shows the missing operating loop that makes them feel inevitable.