Pi Agent (Full Course)

You are helping me turn one specific YouTube video into real, durable learning.

Source video:
- Title: Pi Agent (Full Course)
- URL: https://www.youtube.com/watch?v=yAPKzHrx3eo
- Topic: Agent Architecture
- My current learning frame: Install Pi, configure two providers via OpenRouter and a subscription, then build a session tree (branch, fork, restore) on a real project while swapping in a custom system.md to constrain the agent's tools.
- Why this matters: This deepens the atlas beyond individual tools into how agent runtimes are structured and extended.

Transcript anchors from this exact video:
- 1:41 / Evidence 1: "it doesn't make sense to increase our context window with tons of uh tokens. If we compare the the size of the system prompts with other tools like cloud code, it's like orders of magnitude less than um..."
- 4:29 / Evidence 2: "security around your PI agent. There's many other ways to do that like using sandboxes using some other guards that you can use. So if you want security you can do that uh through some extensions as well."
- 10:10 / Evidence 3: "that you want. There's another important point here. You can also use local models and it's going to start happening more and more especially now that we have open source models that can run locally and for AI..."
- 11:57 / Evidence 4: "there. So everything that you configure in pi or install is going to be inside of this home.py/ aent folder. All the extensions, skills, prompts, teams, sessions, models or JSON or settings JSON that you have for PI..."
- 18:32 / Evidence 5: "put everything related to tech guidelines, important source files or documentations that you want to bring to the agent like architecture or design decisions that you have. It's also the place where you can instruct the agent on..."
- 20:03 / Evidence 6: "you're going to completely replace the original system prompt of PI. That's really great because it's really flexible. You can also call that through the system prompt argument in PI. And here I'm just like saying pay you..."
- 22:48 / Evidence 7: "basically create them as markdown files inside of PI agent prompts and they have this support for argument and you can use the argument inside of the markdown just like a template. So if we load that in..."

Your task:
1. Use the transcript anchors above as the primary source packet. If you add outside context, label it clearly as outside context and keep it secondary.
2. Create a source-check table with columns: timestamp, claim, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable claims from the video. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A one-page agent harness map with tool boundaries and proof signals.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Intent -> Model -> Harness -> Tools -> Verifier -> Artifact
   - 3 concrete examples that apply the video idea to real agentic work
   - 2 failure modes the video helps prevent
   - a checklist I can use the next time I run Codex or Claude
   - one practical exercise with a clear done signal
6. Add a "learning transfer" section: what changes in my workflow tomorrow if I actually learned this?
7. Add a "source check" section that cites which transcript anchor supports each major takeaway.

Quality bar:
- Make this specific to "Pi Agent (Full Course)", not a generic Agent Architecture essay.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- If evidence is weak, say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.