What is Agentic AI? A Technical Guide for Enterprise Software Teams

1. Generative AI vs. Agentic AI

While first-generation Generative AI (e.g., standard ChatGPT or Claude text completions) accepts a prompt and yields a static response, Agentic AI introduces a closed loop of environment interaction.

An agent is provided with an objective (e.g., "Reconcile pending vendor invoices in Odoo against incoming bank statements in Dynamics 365"). The agent breaks down the objective into discrete actions, inspects system states, executes function calls, and confirms reconciliation status.

2. The ReAct Architecture (Reasoning + Acting)

The most widely adopted paradigm for autonomous agent execution is the ReAct loop. At each iteration, the underlying Large Language Model (LLM) or Small Language Model (SLM) executes three steps:

3. On-Device & Client-Side Agent Frameworks (Vantura)

Traditional agent frameworks (such as LangChain or AutoGen) run primarily on centralized Python cloud servers. DataDaur engineered Vantura—an open-source Flutter & Dart framework—to run stateful agentic loops on-device (iOS, Android, desktop, edge).

• Low Latency: Local tool calling without round-trip network overhead for every loop step.
• Privacy & Security: Sensitive enterprise data remains on the device, passing through local SLMs (e.g., Llama 3.2 1B/3B, Phi-3.5).
• Offline Resiliency: Autonomous agent loops continue operating in low-connectivity industrial or agricultural environments.