Full Course: Spec-Driven Development with Coding Agents

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

Source video:
- Title: Full Course: Spec-Driven Development with Coding Agents
- URL: https://www.youtube.com/watch?v=hy8UstR2NEg
- Topic: Agent Architecture
- My current learning frame: Create or reverse-engineer a project constitution, plan and implement one feature on its own branch in small commits, validate it with tests and human diff review, synchronize any resulting spec and code changes, then decide during replanning whether the next change belongs in the constitution, the roadmap, or a new feature phase.
- Why this matters: New playlist item from DeepLearningAI; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 1:34 / Evidence 1: "write a spec is by having a conversation with an agent like cloud code or Gemini or CHB codeex to make the key architectural choices using my knowledge of how I want to make different tradeoffs. Then have..."
- 3:22 / Evidence 2: "your own agent skills to automate your spec driven workflow. You know, if you can accomplish what you need in just one short prompt, that's great. I'm definitely an advocate of lazy prompting when it works. But the..."
- 6:31 / Evidence 3: "window will fill up, often leading to more mistakes as the agent tries to cope with a full working memory. Specs persist between sessions and even agents, anchoring the agent to the core context needed to work in..."
- 8:15 / Evidence 4: "chatbot can talk about code, but the chatbot doesn't have access to your project's code, nor tools you have installed. It just responds to your prompts. Agents are different. They take your prompt, make a plan, and guide..."
- 33:16 / Evidence 5: "your specdriven development workflow across projects across your organization. For example, maybe you have a few non-technical stakeholders that want to monitor the project's progress. You'd like it to update a change log on each merge domain. Most..."
- 47:00 / Evidence 6: "codebase. The constitution will help align future code changes made by the agent with what past devs have already created. You can always add more context if you have it. For example, you might have been dropped into..."
- 57:19 / Evidence 7: "agents.md for rules, agent skills for capturing repeatable workflows with extra context, and ACP for connecting agents to clients. For example, Codeex is a leading AI agent from OpenAI. It runs in a desktop app and in editors..."

Video-aware target:
- Prompt lane: Agent harness
- Mechanism to extract: Identify what surrounding harness makes the model more useful than chat alone.
- Artifact to produce: A one-page agent harness map with tool boundaries, state ownership, and proof signals.
- Artifact must include: model role; tools; state/memory; permission boundary; verification proof

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, transcript support, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable mechanism from the video: Identify what surrounding harness makes the model more useful than chat alone. 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, state ownership, and proof signals.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: User intent -> Model role -> Tool surface -> State and memory -> Verification loop -> Reusable operating rule
   - answers to these source questions: What does the video claim the agent can do? | What surrounding system makes that claim plausible? | What proof is shown instead of merely asserted?
   - 3 concrete examples that apply the video idea to real agentic work, such as a repo-editing harness; a local research assistant; a recurring refresh agent
   - 2 failure modes the video helps prevent, chosen from the transcript evidence and these likely risks: treating model choice as architecture; ignoring tool permissions; missing verification evidence
   - a checklist for the next real workflow, focused on: tool boundaries, state ownership, done signal, recovery path
   - one practical exercise with a clear done signal: Map one current coding workflow as a harness and mark the first missing proof 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 "Full Course: Spec-Driven Development with Coding Agents", not a generic Agent Architecture essay.
- Tie each harness element to a transcript anchor that names a tool, state boundary, permission, model behavior, or verification step.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- Avoid these generic drifts: generic agent definitions; model leaderboard claims; tool list without operating boundaries.
- If evidence is weak or missing, stop and say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.