src/agents/triage_agent.py

# How to Build and Ship Your First Enterprise-Ready AI Agent with Nvidia NemoClaw

NemoClaw is Nvidia’s open-source AI agent platform that lets developers create autonomous agents capable of orchestrating complex workflows across any hardware while delivering the security and privacy controls enterprises demand.

The announcement (reported by Wired and picked up by Tom’s Hardware, CNBC, and others) signals Nvidia’s serious move into the agentic AI layer. Unlike many research prototypes, NemoClaw is being positioned for production use, pitched directly to Adobe, Cisco, CrowdStrike, Google, and Salesforce. It works on any cloud or on-prem infrastructure and will be fully open source, lowering the barrier for customization and compliance.

For builders this is a rare moment: a major infrastructure player is open-sourcing the orchestration layer that turns today’s LLMs into reliable, auditable workers. The timing is especially relevant because OpenAI recently hired Peter Steinberger (the mind behind the original OpenClaw/Clawdbot family), making enterprise-grade alternatives even more valuable.

Why this matters for builders

NemoClaw lets you build agents that can plan, use tools, call APIs, and execute multi-step tasks with minimal human intervention while satisfying corporate requirements around data residency, auditability, and security.

Previous agent frameworks popularized the idea but suffered from two big problems: (1) they often required specific hardware or closed ecosystems, and (2) they lacked the governance features enterprises need before they will allow agents to touch production systems. NemoClaw is designed to solve both.

Because it is hardware-agnostic and open source, you can run it on AWS, GCP, Azure, your own Kubernetes cluster, or even a beefy workstation. This removes vendor lock-in and lets you start small and scale to thousands of agent instances without rewriting your stack.

When to use NemoClaw

Use NemoClaw when you need:

• Autonomous workflow automation that spans multiple internal tools (ticketing, CRM, monitoring, CI/CD)
• Secure, auditable agents that can operate on sensitive customer or financial data
• Multi-vendor environments where you cannot standardize on one cloud or one LLM provider
• Rapid customization of agent behavior for domain-specific processes (legal review, security operations, sales orchestration)
• A future-proof foundation that can incorporate new models as they appear without re-architecting the agent loop

It is less suitable for throwaway consumer chatbots or single-turn reasoning tasks where cost and governance are not primary concerns.

The full process

1. Define the goal (1–2 hours)

Start by writing a one-page spec. Answer these questions:

• What is the exact business outcome the agent should achieve?
• Which systems does it need to touch (APIs, databases, internal tools)?
• What are the success criteria and failure modes?
• What compliance or security constraints exist (data residency, audit logging, human-in-the-loop requirements)?
• Which LLM(s) will power the reasoning?

Example goal statement
“Build a security incident triage agent that ingests alerts from CrowdStrike, pulls context from internal runbooks and CMDB, decides severity, drafts a response in ServiceNow, and only executes remediation after human approval.”

Write this spec in a Markdown file called AGENT-SPEC.md. Keep it under 400 words. This document becomes the source of truth for all future prompts.

2. Shape the spec into prompts (30–45 min)

Good agent prompts are different from chatbot prompts. Structure them as:

You are an enterprise security operations agent called SecTriageAgent.

Core principles:
- Never take destructive action without explicit human approval
- Always log every tool call with timestamp, input, output, and reasoning
- Prefer reading documentation over guessing
- Escalate to human when confidence < 70%

Available tools:
- get_crowdstrike_alert(alert_id)
- lookup_runbook(keyword)
- create_servicenow_incident(...)
- ...

Current task: {{user_request}}
Context: {{alert_details}}

Respond in this exact JSON format:
{
  "reasoning": "...",
  "next_tool": "tool_name",
  "tool_args": {...},
  "requires_human_approval": true/false,
  "confidence": 0-100
}

Save this as system-prompt.md. You will iterate on it heavily during development.

3. Scaffold the project (1 hour)

Create a clean repository:

mkdir nemoclaw-triage-agent && cd nemoclaw-triage-agent
git init
mkdir -p src/tools src/agents config tests
touch AGENT-SPEC.md system-prompt.md requirements.txt docker-compose.yml

Add a minimal pyproject.toml or requirements.txt with:

• langgraph or crewai (or the official NemoClaw SDK once released — check the repo)
• pydantic
• httpx
• structlog
• pytest

Create a config/agent.yaml for environment-specific settings (LLM endpoint, tool credentials, approval channels).

4. Implement core agent loop (2–4 hours)

Because NemoClaw is not yet publicly released, begin with a LangGraph or LlamaIndex workflow that mirrors the expected architecture. This gives you a working prototype you can migrate to the official NemoClaw runtime later.

Starter agent skeleton (LangGraph style)

from langgraph.graph import StateGraph, END
from pydantic import BaseModel
import structlog

logger = structlog.get_logger()

class AgentState(BaseModel):
    alert_id: str
    reasoning: str = ""
    next_tool: str | None = None
    requires_approval: bool = False
    confidence: int = 0
    history: list = []

def reasoning_node(state: AgentState):
    # Call your LLM with the system prompt + current state
    response = llm.invoke(build_prompt(state))
    parsed = parse_json_response(response.content)
    
    state.reasoning = parsed["reasoning"]
    state.next_tool = parsed["next_tool"]
    state.requires_approval = parsed["requires_human_approval"]
    state.confidence = parsed["confidence"]
    state.history.append({"step": "reasoning", "output": parsed})
    
    logger.info("reasoning_complete", confidence=state.confidence)
    return state

# Add tool nodes for each capability...

Wire the graph, then expose a simple FastAPI endpoint that accepts a new alert and returns the current state.

5. Add enterprise guardrails (2–3 hours)

This is where NemoClaw is expected to differentiate. Implement:

• Human-in-the-loop approval via Slack/Teams/Email with one-click approve/reject
• Structured logging of every thought, tool call, and decision (immutable audit trail)
• Tool permission system — each tool has an explicit allow-list per agent role
• Rate limiting and cost controls per agent instance
• Data residency checks — reject tool calls that would send PII to unapproved regions

Create a src/guardrails/ directory and unit test each rule.

6. Validate and test (3–5 hours)

Run these tests before considering the agent production-ready:

• Unit tests for each tool adapter
• Prompt robustness tests — feed the agent 20 variations of the same incident and measure consistency
• Failure mode testing — simulate API outages, malformed responses, low-confidence situations
• Security review — static analysis + manual check for prompt injection vectors
• Load test — run 50 parallel incidents and verify logging, memory usage, and approval queue behavior

Write a test_scenarios.md file with 8–10 real-world examples and their expected behavior.

7. Ship safely

Production checklist:

• Deploy behind authentication and network controls (do not expose the agent API publicly)
• Start with “shadow mode” — the agent runs and logs but all actions require manual confirmation
• Add a kill switch / circuit breaker
• Set up monitoring for cost, latency, approval rate, and escalation frequency
• Document the agent’s capabilities and limitations in an internal wiki page
• Create an on-call rotation that understands how to debug agent decisions

Once the official NemoClaw repository is public, migrate the orchestration layer in a single PR. Because the prompt and tool definitions are already cleanly separated, the switch should be mechanical.

Copy-paste prompts and snippets

System prompt template (starter)

You are {{agent_name}}, an enterprise {{domain}} agent built on NemoClaw.
Your job is to {{primary_goal}} while strictly following these rules:
{{rules}}

You have access to the following tools: {{tool_list}}

Always respond with valid JSON containing: reasoning, next_tool, tool_args, requires_human_approval, confidence.

Approval webhook example

@app.post("/approve")
async def approve_action(payload: ApprovalRequest):
    if payload.approved:
        await execute_approved_tool(payload.action_id)
    else:
        await record_rejection(payload)
    return {"status": "recorded"}

Pitfalls and guardrails

### What if the agent starts taking actions without approval?
Add a guardrail node that checks requires_approval before any state transition that calls a mutating tool. Fail closed. Log the violation and notify the security team.

### What if prompts become inconsistent across environments?
Store the canonical system prompt in version control and load it at startup. Never edit prompts directly in production code. Use prompt versioning (v1, v2) so you can roll back behavior.

### What if costs explode?
Implement a token budget per agent run. Reject any plan that exceeds the budget and force the agent to simplify or ask for human help.

### What if the chosen LLM hallucinates tool names?
Use Pydantic output parsing with strict JSON mode and a retry loop that feeds the error back to the model. After three failures, escalate to human.

### What if we need to support multiple LLMs?
Abstract the LLM call behind a simple interface (LLMProvider). NemoClaw’s open-source nature should make swapping models straightforward.

What to do next

After shipping your first agent:

• Measure actual time saved vs manual process
• Collect feedback from the team that uses the output
• Add one new tool or capability every sprint
• Monitor for prompt drift and retrain the system prompt monthly
• Explore multi-agent collaboration patterns once NemoClaw supports them
• Contribute any useful tools or guardrails back to the open-source project

The agent space is moving extremely fast. The builders who ship reliable, auditable agents first will own the next layer of enterprise automation.

Start small, instrument everything, and keep the human in the loop until trust is earned.

Sources

• Wired original reporting on Nvidia’s NemoClaw plans (primary source)
• Tom’s Hardware coverage: “Nvidia reportedly building its own AI agent to compete with OpenClaw”
• CNBC, Gizmodo, and other secondary reports confirming open-source intent and enterprise focus
• Public statements regarding OpenAI’s acquisition of Peter Steinberger and the continued open-source status of the original OpenClaw project

All implementation advice in this guide is based on current open-source agent patterns that align with the described NemoClaw architecture. Update to the official NemoClaw SDK and documentation once released.