feat: OSVauco GCP Agent Master Repo — Phase 5 initial files (docs, env, readme)

.env.example

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+# .env.example — Copy to .env and fill in your values
+# Usage: cp .env.example .env && source .env
+# NEVER commit .env to version control
+
+# ── Required ─────────────────────────────────────────────────
+export PROJECT_ID="your-gcp-project-id"
+export REGION="us-central1"
+export BILLING_ACCOUNT_ID="XXXXXX-XXXXXX-XXXXXX"
+
+# ── Service Accounts ─────────────────────────────────────────
+export AGENT_SA="agent-runner@${PROJECT_ID}.iam.gserviceaccount.com"
+
+# ── Cloud Run ────────────────────────────────────────────────
+export CLOUD_RUN_SERVICE="gcp-orchestrator"
+export ARTIFACT_REPO="agent-images"
+
+# ── VPC / Security ───────────────────────────────────────────
+export VPC_NETWORK="default"
+export CMEK_KEY_RING="agent-keyring"
+export CMEK_KEY_NAME="agent-key"
+
+# ── Vertex AI RAG Engine ─────────────────────────────────────
+export RAG_CORPUS_DISPLAY_NAME="gcp-agent-corpus"
+export RAG_CORPUS_NAME=""
+
+# ── Memory Bank ──────────────────────────────────────────────
+export MEMORY_INSTANCE_DISPLAY_NAME="${PROJECT_ID}-memory-bank"
+export MEMORY_BANK_INSTANCE=""
+
+# ── CI/CD ────────────────────────────────────────────────────
+export GITHUB_OWNER="your-github-username"
+export GITHUB_REPO="GCP_Agent_Master_Repo"
+
+# ── Observability ────────────────────────────────────────────
+export ALERT_EMAIL="your-email@example.com"
+
+# ── Agent Models ─────────────────────────────────────────────
+export ORCHESTRATOR_MODEL="gemini-2.0-flash"
+export SUBAGENT_MODEL="gemini-2.0-flash"
+
+# ── Optional: LiteLLM ────────────────────────────────────────
+# export OPENAI_API_KEY=""
+# export ANTHROPIC_API_KEY=""

docs/Cost_Management_Rules.md

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+# Cost Management Rules
+**OSVauco-NMTMD-GCOS | GCP Agent Master Repo**
+
+## 1. Budget Alerts
+
+```bash
+gcloud billing budgets create \
+  --billing-account="${BILLING_ACCOUNT_ID}" \
+  --display-name="Agent Platform Budget" \
+  --budget-amount=500USD \
+  --threshold-rule=percent=0.5,basis=CURRENT_SPEND \
+  --threshold-rule=percent=0.8,basis=CURRENT_SPEND \
+  --threshold-rule=percent=1.0,basis=CURRENT_SPEND
+```
+
+## 2. Resource Labeling
+
+All GCP resources MUST carry:
+
+| Label Key | Example | Purpose |
+|-----------|---------|--------|
+| env | prod/dev/staging | Environment |
+| team | osvaucoe | Cost attribution |
+| cost-center | ai-platform | Finance |
+| agent | orchestrator/rag | Per-agent tracking |
+
+## 3. Cloud Run Cost Controls
+
+```bash
+gcloud run deploy ${SERVICE} \
+  --max-instances=10 \
+  --concurrency=80 \
+  --timeout=300
+```
+
+- CPU throttling on for non-latency-critical agents
+- min-instances=1 only for latency-sensitive services
+
+## 4. Vertex AI Cost Controls
+
+- Use gemini-2.0-flash for high-volume sub-agent calls
+- Context caching for repeated prompts (up to 75% savings)
+- max_output_tokens=2048 for most tasks
+- Monitor: aiplatform.googleapis.com/prediction/online/token_count
+
+## 5. RAG Engine Cost Controls
+
+- Delete stale documents from corpus
+- Batch embedding for initial ingestion
+- top_k=5 (not higher)
+- Archive cold corpora to Cloud Storage lower tier
+
+## 6. Memory Bank Cost Controls
+
+- TTL: 7 days for short-lived session memories
+- Firestore-backed for most use cases
+- Index only user_id and session_id
+
+## 7. Networking Cost Controls
+
+- Private Google Access to avoid Vertex AI egress charges
+- VPC routing for all agent traffic
+- Cloud NAT only where unavoidable
+
+## 8. Storage Lifecycle
+
+```json
+{
+  "rule": [{
+    "action": { "type": "Delete" },
+    "condition": { "age": 7 }
+  }]
+}
+```
+Apply with: `gcloud storage buckets update gs://BUCKET --lifecycle-file=lifecycle.json`
+
+## 9. End-of-Day Teardown Checklist
+
+- [ ] Run 03-teardown.sh
+- [ ] Verify no active Agent Runtimes: `gcloud ai agent-engines list --region=us-central1`
+- [ ] Verify no active Vertex AI endpoints: `gcloud ai endpoints list --region=us-central1`
+- [ ] Check billing dashboard for anomalies

docs/GCP_Best_Practices.md

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+# GCP Best Practices for Multi-Agent AI Systems
+**OSVauco-NMTMD-GCOS | Last Updated: 2026-05**
+
+## 1. Multi-Agent Architecture Patterns
+
+### 1.1 Coordinator + Sub-Agent Pattern (ADK)
+Use a root orchestrator agent that delegates to specialized sub-agents.
+
+```python
+root_agent = Agent(
+    name="orchestrator",
+    model="gemini-2.0-flash",
+    sub_agents=[greeting_agent, rag_agent, memory_agent, farewell_agent],
+    instruction="Delegate tasks to the appropriate sub-agent.",
+    before_model_callback=before_model_callback,
+    before_tool_callback=before_tool_callback,
+)
+```
+
+### 1.2 Graph Workflow Pattern
+- SequentialAgent: ordered pipelines
+- ParallelAgent: concurrent sub-tasks
+- LoopAgent: retry/polling patterns
+
+### 1.3 Session State and Memory
+- Use session.state for short-lived conversation context
+- Use Vertex AI Memory Bank for persistent long-term memory
+- Tag memories with user_id and session_id for retrieval scoping
+
+## 2. Model Selection
+
+| Use Case | Recommended Model |
+|----------|------------------|
+| Complex reasoning / orchestration | gemini-2.0-flash or gemini-2.5-pro |
+| Fast sub-agent calls | gemini-2.0-flash |
+| Non-Google models | LiteLLM via LiteLlmModel |
+| Embedding generation | text-embedding-005 |
+| Code generation | gemini-2.5-pro |
+
+## 3. Tool Design
+
+- Single-responsibility per tool function
+- Full docstrings — ADK uses these for LLM instructions
+- Validate inputs inside tools; never trust LLM output blindly
+- Use before_tool_callback for argument guardrails
+- Return structured dicts, not raw strings
+
+## 4. Vertex AI Integration
+
+### 4.1 RAG Engine
+**CONFIRMED** (source: Vertex AI RAG Engine SDK, google/adk-python samples, 2026-05):
+- Use RagCorpus for document grounding
+- Embedding model: text-embedding-005
+- Chunking: 512 tokens, 50-token overlap
+- Retrieval: top_k=5, vector_distance_threshold=0.7
+
+**UNKNOWN / UNDOCUMENTED** (as of 2026-05):
+- Full regional availability (eu-west3 issues known)
+- Whether rag.import_files() is idempotent for same GCS path
+- See: docs/Doc_Gaps_and_Open_Questions.md OQ-02
+
+### 4.2 Memory Bank
+**CONFIRMED** (source: Gemini Enterprise Agent Platform Memory Bank setup, 2026-05):
+- load_memory_tool + preload_memory_tool from google.adk.tools
+- Required role: roles/aiplatform.user
+- SDK: google-cloud-aiplatform >= 1.111.0
+
+**UNKNOWN / UNDOCUMENTED** (as of 2026-05):
+- gcloud CLI equivalent for client.agent_engines.create()
+- Whether tools auto-discover Memory Bank instance via project/region
+- See: docs/Doc_Gaps_and_Open_Questions.md OQ-01, OQ-06
+
+### 4.3 Security Controls Matrix
+
+| Component | Data Residency | CMEK | VPC-SC | Access Transparency |
+|-----------|---------------|------|--------|-------------------|
+| Agent Platform | YES | YES | YES | YES |
+| RAG Engine | NO | NO | YES | NO |
+| Vector Search | YES | NO | YES | NO |
+
+## 5. Observability
+
+- Cloud Trace: end-to-end request tracing
+- Cloud Logging: structured JSON logs with session_id; never log PII
+- Cloud Monitoring: latency p50/p95/p99, error rates
+- Error Reporting: auto-alert on new exception types
+
+## 6. Deployment
+
+- Cloud Run services (serverless, auto-scaling)
+- Artifact Registry for container images
+- min-instances=1 for latency-sensitive agents
+- Cloud Build for CI/CD; --no-traffic deploy + HITL traffic switch
+- Label all resources: env, team, cost-center, agent
+
+## 7. MCP Integration
+
+```python
+from google.adk.tools.mcp_tool.mcp_toolset import MCPToolset, StdioServerParameters
+
+tools, exit_stack = await MCPToolset.from_server(
+    connection_params=StdioServerParameters(
+        command="npx",
+        args=["-y", "@modelcontextprotocol/server-filesystem", "/tmp"]
+    )
+)
+```
+
+## 8. A2A Protocol
+
+- Agents expose AgentCard at /.well-known/agent.json
+- A2A enables multi-vendor agent interoperability
+- Secure A2A calls with service account tokens + VPC-SC
+
+## 9. Handling Missing or Incomplete GCP Documentation
+
+*Implements OSVauco-NMTMD-GCOS section 1.5.*
+
+| Situation | Action |
+|-----------|--------|
+| API confirmed in GA docs or stable samples | Use it; cite source |
+| API only in preview docs / blog post | Mark PREVIEW — validate before prod |
+| API not found in any authoritative source | Omit; add to Open Questions |
+
+## References
+- ADK: https://google.github.io/adk-docs/
+- Vertex AI RAG: https://cloud.google.com/vertex-ai/generative-ai/docs/rag-overview
+- Gemini Enterprise Agent Platform: https://cloud.google.com/gemini-enterprise-agent-platform
+- A2A: https://google.github.io/A2A/