Cloud Agent for Regulated Teams: A Practical Guid
Code and compute never leave your infrastructure. Cloud Agent runs as a Kubernetes Job inside your VPC. For teams that cannot compromise on data residency.
The problem regulated teams face
You want AI-powered test generation. You want gap analysis. You want defect patterns.
But your code cannot leave your infrastructure. Your data residency requirements are non-negotiable. Your compliance team will not approve anything else.
So you are blocked. You cannot use hosted AI services. You cannot upload your code to the cloud. You are stuck.
That is the problem Cloud Agent solves.
What Cloud Agent is
Cloud Agent is WalnutAI running entirely inside your infrastructure. Not in our cloud. In yours.
Deployment: a Kubernetes Job. You deploy it into your VPC. Your code never touches our servers. Our analysis never touches your network.
Result: you get all the AI capabilities. You keep all the data residency compliance.
How it works
The architecture
Cloud Agent is a containerized service that runs in your Kubernetes cluster. It has:
LLM inference engine (runs locally, no cloud calls)
Gap analysis engine (local computation)
Defect patterns analysis (local storage)
Test generation (local model)
Everything runs inside your VPC. No data egress. No compliance concerns.
Deployment: a Kubernetes Job
Deploy it like any other service:
YAML example: apiVersion: batch/v1
kind: Job
metadata:
name: walnutai-cloud-agent
spec:
template:
spec:
containers:
- name: agent
image: walnutai/cloud-agent:latest
env:
- name: VPC_MODE
value: "true"
volumeMounts:
- name: code
mountPath: /app/code
That is it. Kubernetes handles the rest. The agent runs. Your code stays local.
Integration: read your code, write results
Cloud Agent mounts your code repository. It reads the code like a local tool would.
When analysis is complete, it writes results back to your infrastructure: gap analysis reports, test case files, defect pattern databases.
All local. All yours.
Why this matters for regulated teams
HIPAA compliance
Healthcare cannot send patient data or code that touches it to external services. Cloud Agent runs inside your infrastructure. HIPAA approved.
PCI-DSS compliance
Payment processing teams cannot send code to the cloud. Data residency must be maintained. Cloud Agent keeps everything local.
SOC2 Type II
Audit requirements mandate that sensitive code stays within your infrastructure. Cloud Agent does not violate that.
Data sovereignty
Some jurisdictions (EU, China) have strict data residency rules. Cloud Agent respects them by running locally.
The trade-off: local compute
Running inference locally means you need local compute capacity. The LLM inference engine requires GPU or substantial CPU.
Budget: a single GPU node costs $200-400 per month. You can handle this.
Setup: add a GPU node to your cluster. Assign it to the Cloud Agent Kubernetes Job. It runs.
Performance: local inference is slightly slower than cloud (100ms-500ms latency overhead). Most teams do not notice.
It is a trade-off. You get compliance. You give up a bit of latency.
What you keep with Cloud Agent
You do not sacrifice capability:
Gap analysis phase 1, 2, 3: same quality, runs locally
AI test case generation: same output, runs locally
Defect patterns engine: same clustering, analysis, guardrails
Delivery Intelligence Dashboard: same metrics, local data
Everything works. Just runs inside your VPC instead of ours.
Compliance by design
Cloud Agent is not just "WalnutAI, but on-premise." It is designed for compliance.
No data leaves your infrastructure
Audit logging built-in (all analysis operations logged locally)
Access control integrated with your Kubernetes RBAC
Network isolation: Cloud Agent does not call external APIs by default
You pass compliance review. You can show the auditor: here is the containerized image, here is where it runs, here is the data flow.
For compliance officers
Cloud Agent is how you approve AI-powered tools for your engineering org. No data residency compromise. No regulatory risk.
You can say yes to your engineers. You can say yes to productivity tooling without saying yes to compliance risk.
For engineering leaders
You finally have access to the same tools as non-regulated teams. Your QA can use AI test generation. Your architects can run gap analysis. Your teams can ship faster.
The compliance constraint is no longer a blocker.
Deployment scenarios
Scenario 1: Healthcare organization
HIPAA-required data residency. Healthcare code cannot leave the network. Cloud Agent deployed in their AWS private VPC. Gap analysis runs on their code repository. Results stay in their infrastructure. Audit-ready.
Scenario 2: Fintech startup
PCI-DSS regulated. Credit card data and payment processing code in their infrastructure. Cloud Agent runs as a sidecar service in their Kubernetes cluster. Test generation, gap analysis, all local. Compliance team approves.
Scenario 3: EU-based company
GDPR data residency. Customer data must stay in EU data centers. Cloud Agent deployed in their EU infrastructure. Never touches US servers. Compliant by default.
The honest trade-off
You get compliance. You lose some latency. You need local GPU compute. You have to manage the deployment.
It is worth it if compliance is a hard requirement. If you are a regulated team, Cloud Agent is the only option.
If you are not regulated, the hosted service is simpler.
Performance and scale
Cloud Agent can handle:
2-5 concurrent analysis jobs per GPU node
Codebase analysis on repos up to 1M lines of code
Gap analysis on 100+ stories per run
Test generation at the same throughput as cloud version
For most teams, one GPU node is enough. Large organizations might need 2-3.
Next week: deeper dives
This week we will cover:
How to deploy Cloud Agent in your Kubernetes cluster
How to configure access control and audit logging
Performance tuning for your specific infrastructure
How to ensure compliance during and after deployment
For now: if you are regulated, Cloud Agent is available. If you have been blocked by compliance requirements, you are unblocked.
See Cloud Agent in action. Deploy it in your infrastructure. https://www.walnutai.ai/
