Trusting Your System¶
You've Built a Delegation System¶
Look at what you have now. Acceptance criteria that define exactly what "done" looks like (Lift 1). Decomposition and skills that organize the work (Lift 2). Automated tests that verify results without you checking every line (Lift 3). Background execution that lets AI build while you move on (Lift 4).
That's not a collection of tricks — it's a system. And the reason you can delegate with confidence is that every piece reinforces the others. Your AC tells AI what to build. Your tests confirm it was built correctly. Your project context file keeps AI aligned with your conventions every time you start a new conversation. You don't need to watch every step because you've set up guardrails that catch problems for you.
Batching Similar Work¶
Here's a practical way to use parallel delegation: batching similar tasks.
Say your backlog has three new information cards to add to your UI — a weather summary card, a recent observations card, and a snowpack conditions card. Each one is independently buildable. Each one follows the same pattern. Each one can have its own acceptance criteria and tests.
Instead of building them one at a time, you can write three user stories, send each one to AI in its own conversation, and let them build simultaneously. When they come back, you check each one against its acceptance criteria and make sure the tests pass. If one comes back wrong, you fix that one — the others aren't affected because they were built independently.
This works because the delegation-ready test (Section 1) already told you these tasks are safe to run in parallel: bounded scope, clear criteria, independently buildable.
Here's when not to batch: If two tasks change the same part of your application — say, both modify the main dashboard layout — running them in parallel will create conflicts. Build one, verify it, then build the next. Parallel is for independent work; sequential is for work that overlaps.
Team Activity: Plan Your Final Sprint¶
Format: Mob Session Time: ~4 minutes Setup: Gather around one screen. One person drives, everyone navigates.
Your Final Sprint challenge: integrate live avalanche forecast data, add weather enrichment, build observation trend analysis, and create a trip report generator. Plus whatever else your team wants to tackle.
Step 1: Look at your feature list and apply the delegation-ready test from Section 1. Which features can you write clear acceptance criteria for right now?
Step 2: For the features that are ready, ask your AI coding assistant:
Here are the features we want to build for our final sprint: [list them]. For each one, write a user story with 2-3 acceptance criteria.
Step 3: Review the stories as a team. Are the criteria specific enough? Could any of these be built at the same time without stepping on each other?
Discuss: How many features could you realistically ship in the time you have? What would you tackle first?
Save your progress:
Save my progress and sync it.
The Blue Square Journey¶
Take a moment to see how far you've come.
Lift 1 gave you the language — Three Pillars, delegation contracts, acceptance criteria, the Explore → Plan → Implement → Verify workflow. You learned to communicate with AI clearly enough to delegate a single feature.
Lift 2 gave you scale — decomposition, skills, and criteria-based review. You went from delegating one feature to managing a body of work.
Lift 3 gave you confidence — automated tests, TDD, and deployment. You stopped checking everything manually and started shipping verified, tested applications.
Lift 4 gave you delegation at speed — judgment about what's ready, background execution, and the trust to let AI build while you move on.
You came in doing the work. You're leaving as a curator — someone who shapes the environment that AI works in. The acceptance criteria, the skills, the tests, the project context file — that's your garden. The better you tend it, the more you can trust what grows. What you take on from here just got a lot bigger.
Key Insight¶
The shift from doer to delegator is the culmination of everything you've learned. You can delegate with confidence because you've built a system that catches problems: acceptance criteria define success (Lift 1), decomposition keeps tasks bounded (Lift 2), automated tests verify results (Lift 3), and background execution lets you move faster (Lift 4). Your judgment about what's ready to delegate — that's the skill no AI can replace.
Up next: Run 4 — the Final Sprint. Everything you've built comes together. Delegate with judgment, trust your tests, and ship something worth demoing.