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

• 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.

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.

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.

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

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.