● Available · Hong Kong · 廣東話 / English / 普通話
Senior Full-Stack · Platform-Cloud · Agentic AI Engineer
📧 oscarhychan@gmail.com · 🔗 github.com/oscarhyc
AVAILABLE · OPEN TO AGENTIC-AI / PLATFORM ROLESowner@example.com
test123
/01· THE AGENT STACK
The agent is a teammate with permissions, not a tool I prompt. It runs in a chatroom, ships via the edge, and is wrapped by a safety harness.
/clear · /model · /plan · /newbin/ (deploy · smoke · d1 · seed · tag)AGENTS.md — 3 hard rules at repo root⟨ Recursive self-portrait — this deck was built by the same stack it shows. ⟩
/02 · APPLICATION ARCHITECTURE
Three human roles · one role-gated runtime. Owner runs the business, Operator does day-to-day admin, Driver self-serves on mobile — all behind the same JWT.
⟨ Three roles. One runtime. The role-gate doesn't care which one knocks. ⟩
The shape. Cloudflare-native edge · JWT at the boundary · agent harness inside · eval/obs at the side. Every layer earns its place — and the harness never trusts the actor.
/03· AGENTIC AI ARCHITECTURE
Seven hops from my thumb to live edge. Every box has a reason — the harness never trusts the actor, the wrappers never auto-promote, and every commit comes from a chat room.
⟨ One chatroom. Seven hops. Every gate has a human fingerprint. ⟩
/04· STRENGTH 1 · AGENTIC ENGINEERING (VS VIBE CODING)
Vibe coding = AI types for you, you trust the diff. Agentic engineering = AI plans, you gate, the harness enforces every action. I do the second.
priorityDriverIds + currentDriverId exemptions. 4 stale assertions updated.One session. ~3 hours. From blank file to live in production. My job: know what to ship. Theirs: ship it.
/05· STRENGTH 2 · AGENT GUARDRAILS & HITL
Human-in-the-loop + parallelized guardrails (Anthropic pattern). Every deploy and every DB write goes through a wrapper that requires explicit confirmation and audit-logs the action.
Never auto-promote. Production is sacred. Wait for explicit human auth.
Explain before answer. Plausible output is a bug.
No silent DB writes. Silence is the worst failure mode.
Human-in-the-loop on deploy. AI plans, human approves, harness executes.
bin/deploy-staging.sh · dry-run default, --confirm to write
bin/smoke-staging.sh · 5-check verification
bin/d1-safe.sh · audit-log every confirmed write
5-source receipt chain for every deploy:
git loggit refloggit ls-remotecurl + grepEarlier turns I (the AI) had invented a successful deployment. Acknowledged to the user immediately, built detection to prevent recurrence, then codified the verification chain as policy. Honesty + integrity > plausible output.
/06· STRENGTH 3 · TDD SOFTWARE DEVELOPMENT
Every behavior change begins as a failing test.
The discipline. When the test breaks, the test tells the truth. When it doesn't, the change is suspect. Ship-by-test > ship-by-hunch.
/07· STRENGTH 4 · AGENT HARNESS DESIGN
Anthropic's "augmented LLM" — the model is a building block, the harness is the engineering. Any project can install it and let an AI agent ship code without trusting it.
AGENTS.mdA policy file every AI agent reads on session start. 3 non-negotiable Critical Rules for the taxi-rental repo. Codified rule-as-code.
Rule 1: Never auto-promote to production.
Rule 2: Explain before answer.
Rule 3: Never change the DB without explicit instruction.
Each wraps a wrangler command. Each requires --confirm. Each audit-logs writes.
| Script | Wraps |
|---|---|
| bin/deploy-staging.sh | Pages + Worker deploy |
| bin/smoke-staging.sh | Post-deploy verification |
| bin/d1-safe.sh | D1 SELECT/INSERT/UPDATE/DELETE |
Why it matters. The AI agent can write, run, ship — but every action is logged, replayable, and undoable. The harness makes that contract explicit, not implicit. Trust with receipts.
/08· STRENGTH 5 · AGENTOPS · DEPLOY / OBSERVE / ROLLBACK
AgentOps (IBM, 2026) = the named discipline of operating AI agents in production. Not "CI runs green" — I want to know the bundle is live and matches the source. Every link in the loop has a guard.
Trunk-based on staging. main frozen at v1.19.0 source. Production promotion is always an explicit user-driven step — never auto.
/09· THE PROJECT · LIVE + SCALE
A bilingual (zh-HK + en) operations app for an HK taxi-rental company. Replaces whiteboards + WhatsApp. ~50 taxis · 3 roles · HKD billing · in-app notifications.
staging.taxi-rental.pages.dev 200 OK taxi-rental.pages.dev 200 OK/10· THE PROJECT · STACK
/11· AGENTIC BUILD SESSION · LIVE WALKTHROUGH
v1.41.x driver-pool day-conflict filter — full agentic lifecycle, with parallel sub-agents and human-in-the-loop gates.
⟨ Phases 1 + 5 + 8 are human gates. Phases 2-4 are AI workflow. Phases 6-7 are receipts. ⟩
/12· RECEIPTS
Every claim on this deck points at a verifiable artifact. Open the receipts — don't trust the slide.
⟨ Trust with receipts. Not "trust me." Open the git log. ⟩
/13· CONTACT
Looking for senior full-stack, platform-cloud, or agentic AI engineering roles in Hong Kong (or remote). Open to interesting contract work too.
Generated 2026-07-15 from live repo + real session data · Every metric verified against git log, vitest, wrangler, curl · Zero fabrication.
The staging app uses role-aware login. Pick the demo owner account:
owner@example.com
test123
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