Case Study

NemoClaw vs. OpenClaw

Choosing an AI agent framework for enterprise deployment — How we selected NemoClaw to address OpenClaw's security gaps.

0

Security Incidents

47

Violations Blocked

52

Active Users

15min

Time to First Agent

01 / Context

A US-based enterprise software company (Series B, 150 employees) was exploring autonomous AI agents to automate internal workflows: customer support ticket triage, code review assistance, and sales outreach personalization. The team had built a POC using OpenClaw, but security concerns made production deployment impossible.

02 / Problem

No Built-in Guardrails

OpenClaw allowed arbitrary shell commands. Baseline defense rate against adversarial attacks was only 17%.

17%

No Policy Controls

No way to enforce role-based access. Agents could access sensitive channels or execute destructive commands.

No RBAC

Auditability Gap

No native logging at compliance level. Couldn't answer "what did the agent access, and who authorized it?"

No Audit
03 / Constraints

Custom Security

4 engineers, no months to build sandbox

Zero Trust

Least privilege, complete action logs

Hybrid Deployment

Laptops + cloud infrastructure

Model Flexibility

Anthropic, OpenAI, open-source

04 / Approach

Architecture Comparison

OpenClaw (3-Layer)

Orchestrator

Cloud LLM (Claude, GPT) — task decomposition

Gateway

Local WebSocket server — protocol translation

Executor (Pi-embedded)

Runs with full user permissions — NO policy enforcement

NemoClaw (Adds Security Layer)

Privacy Router

Strips PII, applies DLP policies

OpenShell Runtime

Sandboxed containers, policy-based guardrails

One-Command Deploy

nemo install sets up entire stack

OpenClaw + Custom

3-4 months engineering effort for sandboxing, audit, policy engine

Anthropic Computer Use

Closed ecosystem, limited to Anthropic models, less flexible

Selected: NemoClaw

Pre-built security stack, any coding agent, NVIDIA ecosystem

05 / Implementation

10-Week Rollout

Three phases with fallback capability

10 Weeks

Total Timeline

Week 1-3

Sandbox Evaluation

Deployed on 2 RTX workstations. 80-120ms overhead. 14 blocked violations in first week.

Week 4-7

IT-Approved Pilot

20 users across engineering, sales, support. Role-specific policies. Nemotron 3 Super for local inference.

Week 8-10

Production Rollout

45 agents in cloud (AWS). SIEM integration (Splunk). 90-day log retention.

Technical Details

OpenShell Sandboxing

Isolated containers with resource limits and syscall filtering. Policies must be defined before deployment.

Privacy Router

Strips PII from prompts sent to cloud models. Local Nemotron processes sensitive data on-prem.

06 / Results
Metric OpenClaw (POC) NemoClaw (Production)
Security incidents 3 (1 successful) 0
Policy violations blocked N/A 47
Audit trail Manual log grep Full SIEM, 90-day
IT approval time 4-6 weeks 3 days
User adoption 8 users 52 users
Time-to-first-agent 2-3 days 15 minutes

Monthly Overhead

$200

OpenShell runtime (50 agents, RTX)

Hardware Investment

$15K

5 RTX workstations

Agent Uptime

99.8%

Post-stabilization

07 / Key Insight

The choice is about trust infrastructure, not capability.

OpenClaw solves the hard problem of connecting LLM reasoning to real-world execution—but it leaves security to the user. NemoClaw provides the missing enterprise layer: policy-based guardrails, sandboxed execution, and audit trails. The real bottleneck for agent adoption is not intelligence but trust.

Choose OpenClaw

  • • Developer/researcher building personal agents
  • • Have expertise to add your own security layer
  • • Running in isolated, non-sensitive environments

Choose NemoClaw

  • • Corporate environment with compliance needs
  • • Need policy controls out of the box
  • • Want supported path to production

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