# Four Stages to Build an AI Agent from Scratch: From Bare API Calls to a Secure and Controllable Production-Grade System

> This article deeply analyzes how to build a production-grade AI Agent in four stages: from bare API calls to a complete Agent loop, then to reinforcing guardrails, and finally to achieving deterministic workflows. It includes practical code using Google Vertex AI + Claude Opus and a secure deployment solution with NVIDIA OpenShell sandbox.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-12T16:45:52.000Z
- 最近活动: 2026-06-12T16:50:34.811Z
- 热度: 132.9
- 关键词: AI Agent, Claude Opus, Vertex AI, Agent Loop, 安全防护, 沙箱, 生产部署, NVIDIA OpenShell
- 页面链接: https://www.zingnex.cn/en/forum/thread/ai-agent-0a700c37
- Canonical: https://www.zingnex.cn/forum/thread/ai-agent-0a700c37
- Markdown 来源: floors_fallback

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## Introduction | Four-Stage Framework and Security Practices for Building Production-Grade AI Agents from Scratch

- **Original Author/Maintainer**: dhshah13
- **Source Platform**: GitHub
- **Original Title**: how-to-create-an-agent
- **Original Link**: https://github.com/dhshah13/how-to-create-an-agent
- **Publication Date**: 2026-06-12

This article analyzes the four key stages of building a production-grade AI Agent (bare API calls → Agent loop → guardrail reinforcement → deterministic workflows). Combined with practical code using Google Vertex AI + Claude Opus and a secure deployment solution with NVIDIA OpenShell sandbox, it provides a migration path from demo to production and addresses core confusion in transitioning from chatbots to task-executable systems.

## Background | Cognitive Leap in Agent Development: Core Confusion from Concept to Production

AI Agents are moving from proof of concept to production, but developers often wonder: why is their "Agent" just a chatbot instead of a production system? The answer lies in a shift in development paradigm—evolving from single API calls to a complete Agent loop, then to deterministic workflows. Based on practical projects, this article outlines a four-stage path and reveals challenges and solutions at each level.

## Methodology | Key Steps for Building a Production-Grade AI Agent in Four Stages

### Stage 1: Bare Calls (Root of Hallucinations)
Directly calling large model APIs without tool interaction capabilities, prone to fabricating data (e.g., Jira ticket IDs). Essentially a chatbot, far from production-ready.

### Stage 2: Agent Loop (Empowering Action Capabilities)
Introduce a loop mechanism of model selecting tools → execution feedback → reasoning. Can interact with external systems (Jira/Slack), but autonomous behavior poses security risks.

### Stage 3: Guardrails (Security Boundary Design)
Add four layers of protection: allowlist (tool restriction), output validation, human confirmation, and iteration limit. Resist prompt injection attacks and build in application-layer security controls.

### Stage 4: Deterministic Workflow (Ultimate Form)
Refactor multi-step decisions into a fixed pipeline. The model runs only once, steps are predictable, inspectable, and auditable—suitable for standardized tasks.

## Evidence | Practical Solutions for Sandbox Deployment and Production Migration

#### Sandboxed Deployment: NVIDIA OpenShell Security Practice
Restrict file system access, process creation, and network connections at the kernel level. Adopt a "sandbox + policy" combination to isolate the Agent environment (only outbound access to Vertex AI, no code push credentials). Even if the model is compromised, sensitive information cannot be stolen.

#### Three Replacement Points from Demo to Production
1. Replace simulated tools with real API calls;
2. Enable posting operations under the premise of maintaining human approval;
3. Retain guardrail mechanisms such as allowlists and human confirmation (security bottom lines cannot be omitted).

## Conclusion and Recommendations | Essence of Agent Engineering and Team Practice Guide

### Conclusion
Building a production-grade Agent is a systems engineering task involving tool architecture, security design, workflow planning, and multi-layer defense strategies. The four-stage path provides a clear cognitive framework.

### Recommendations
- Teams start prototype development from Stage 2;
- Establish a complete guardrail mechanism in Stage 3;
- Evaluate Stage 4 workflows based on task characteristics;
- Security runs through the entire development process, not as an afterthought patch.