# hncb-fusion-deid-demo

Demo of the HNCB use case: a **reversible PII de-identification round trip**
through **Axway Amplify AI Gateway (Fusion)**, with the reasoning done by an
**Amazon Bedrock AgentCore** agent. The advisor types a query containing a real
name; the cloud only ever sees a token; the real identity is restored before the
answer is shown.

> **Working with Claude Code? Read `CLAUDE.md` first** — it has the context,
> commands, and task backlog.

> **Status: unrun scaffold.** Authored, not executed. Review everything, pin
> dependency versions, and adjust AgentCore specifics to the current CLI.
> Demo-grade, not production-grade.

## Fusion is shared SaaS
Fusion is **not deployed by this repo** — it's a shared Amplify AI Gateway SaaS
instance, configured in its console (`fusion/POLICY_SETUP.md`). Because it's SaaS,
anything it calls must be a **public HTTPS endpoint with auth**. So the AWS side's
job is to expose two endpoints Fusion calls — **`/tokenize`** (ingress) and
**`/restore`** (egress) — plus host the detector, vault, RAG tool, and agent.
Those two endpoints are the main remaining build (tasks T1/T2 in `CLAUDE.md`).

## What it demonstrates
Raw PII (a Chinese name + a Taiwan ROC ID) is detected on ingress, replaced with a
**random, format-safe token**, and only the tokenized prompt goes to the cloud
agent. The agent tool-calls back "on-prem" with the token, gets a de-identified
evidence package, and writes talking points. Fusion restores the identity on the
way out. **The money shot:** show the Bedrock request / AgentCore trace live — the
cloud only ever saw `CUST_000123`, never `王小明`.

## Trust zones are logical
Everything is one AWS account. The "on-prem" zone is tag-labelled resources
(`Zone = on-prem-VPC-A`) standing in for HNCB's branch data centre. Proves
data-flow behaviour, not physical residency — say so on camera.

## Component → service map (their 8 steps)
| Step | Component | Service in this repo |
|---|---|---|
| 1, 8 | Advisor UI | `ui/index.html` on S3+CloudFront (or local) |
| 2, 3-route, 8 | **Fusion AI Gateway** (the product) | **shared SaaS** — configured via `fusion/POLICY_SETUP.md` (not deployed) |
| 2 (ingress), 8 (egress) | `/tokenize` + `/restore` endpoints Fusion calls | **TODO** `gateway_api/` (tasks T1/T2) |
| 2 | PII **detector** (typed findings, not redaction) | Presidio on Fargate — `presidio/` |
| 2, 4, 8 | Token **vault** (reversible map) | DynamoDB — `terraform/main.tf` |
| 3, 7 | Cloud **agent** + model | AgentCore Runtime + Bedrock — `agent/` |
| 4 | Tool bridge (Lambda → MCP tool) | AgentCore Gateway — `scripts/agentcore_setup.sh` |
| 4-6 | On-prem **RAG tool** + data | Lambda + DynamoDB — `lambda_rag/`, `seed/` |
| all | Observability | AgentCore Observability + CloudWatch |

## Repo layout
```
CLAUDE.md            start here if using Claude Code (context + task backlog)
terraform/           DynamoDB (vault + customers), RAG Lambda, IAM, Presidio hosting
lambda_rag/          RAG tool: token resolve -> de-identified evidence package
gateway_api/         TODO: /tokenize + /restore endpoints Fusion SaaS calls (T1/T2)
agent/               Strands agent for AgentCore Runtime + tool schema
presidio/            PII detector service (typed findings) + Dockerfile
seed/                fake customer (Wang Xiaoming) + seed script
ui/                  advisor UI (restored-vs-tokenized split view)
scripts/             deploy.sh, agentcore_setup.sh, teardown.sh
fusion/              POLICY_SETUP.md (SaaS console config — the manual part)
```

## Prerequisites
`aws-cli` (creds + region — this demo runs in `ap-southeast-1`), `terraform >= 1.5`,
`podman` or `docker` (Presidio image), `python3` + a venv with `boto3`,
`bedrock-agentcore-starter-toolkit` (`pip install`, provides `agentcore`), Bedrock
access for the **apac** Claude 3.5 Sonnet v2 *inference profile*
(`apac.anthropic.claude-3-5-sonnet-20241022-v2:0` — the raw id is not on-demand in
this region), and — for the production path — the shared **Fusion SaaS** instance.

## Deploy
The live stack was brought up in this order (authoritative runbook: the task notes
in `CLAUDE.md`, which record the exact sequencing and gotchas):
1. `terraform -chdir=terraform apply` core infra (DynamoDB, RAG + tokenize/restore/
   orchestrator Lambdas, API Gateway, Presidio on Fargate, UI on S3+CloudFront).
   Presidio needs its image in ECR and `presidio_url` set to the task's **private** IP.
2. Seed the synthetic customer + the demo vault token (`seed/seed.py`).
3. `scripts/agentcore_setup.sh` → MCP Gateway + RAG tool target.
4. `agentcore configure` + `agentcore launch` → Runtime ARN; then grant the runtime
   execution role `bedrock-agentcore:InvokeGateway` (command in `agentcore_setup.sh`).
5. Put the Runtime ARN in `terraform/local.auto.tfvars` (`agent_runtime_arn`) and
   re-apply so `/demo` can invoke it.

## Demo script (Fusion-less dry run — maps to the 8 steps)
```bash
cd terraform && DEMO=$(terraform output -raw demo_url) && UI=$(terraform output -raw ui_url); cd ..

# 1-2, 4-8: advisor query -> tokenize -> agent (on tokens) -> restore, in one call:
curl -s -X POST "$DEMO" -H 'content-type: application/json' \
  -d '{"query":"請幫我整理王小明最近三個月的理財往來，並給我下次拜訪話術。"}' | python3 -m json.tool
#  -> deidentified_prompt: "...CUST_xxxxxx..."   (what left for the cloud)
#     agent_tokenized:     talking points, tokens only
#     final:               "（客戶：王小明）..."   (identity restored on-prem)

# 3. money shot — the cloud runtime trace only ever shows the token:
LG=/aws/bedrock-agentcore/runtimes/<runtime-id>-DEFAULT
aws logs filter-log-events --log-group-name "$LG" --filter-pattern '"王小明"'  --query 'length(events)'  # 0
aws logs filter-log-events --log-group-name "$LG" --filter-pattern '"CUST_"'   --query 'length(events)'  # >0

# 8 (visual): open the split-view UI and submit the same query
echo "$UI"
```

## Teardown
```bash
bash scripts/teardown.sh   # deletes Gateway/Runtime/Memory + `terraform destroy` + ECR repos
```
Leftover-but-free after teardown: the hand-made default VPC, the
`AmazonBedrockAgentCoreSDKRuntime-*` role, and a CodeBuild project (delete by hand
if you want it spotless).

## Honest caveats
- **zh-TW detection is demo-narrow** — tuned to the scripted entities, not
  production recall. That remains the real-engagement risk.
- **Per-request randomization** lives in the `/tokenize` mint step; confirm it
  satisfies HNCB's "different each time" requirement.
- **The agentic token-resolution loop** (agent tool call → on-prem RAG resolves the
  token) is custom orchestration by design — where Fusion's depth is the argument.