How Do AI Agents Really Work?¶

Source: https://www.wte.net/Blog/June/How-Do-AI-Agents-Really-Work
Date: June 2025

Introduction¶

AI agents have generated significant interest with promises of automation and efficiency. However, many people lack understanding of what these tools actually are. The post emphasizes that AI agents are practical instruments rather than sentient beings, comparing them to advanced farm equipment. The author references Steve Jobs' concept that customers care about outcomes ("holes"), not the mechanism, advocating for "AI that just works."

AI agents function as automated task tools, not autonomous entities
Three maturity levels exist; most businesses operate at Level 1 (basic chatbots), while greater ROI emerges at Levels 2-3
Platforms like N8N and Make.com enable workflow automation without extensive development resources
Success depends on quality data—"clean, connected, and relevant inputs are the key to accurate, business-ready results"
Strategy: start with a single use case, scale progressively

The Three Levels of Agent Maturity¶

Level One: "Robby the Robot" – Passive Helpers¶

At its core, AI functions as text processing technology. These agents excel at brainstorming and summarization but lack contextual knowledge about specific enterprise systems. They cannot retrieve information they haven't seen—asking ChatGPT about yesterday's meeting fails because the system lacks that context.

Business Applications:

Brainstorming support
Writing assistance
Data summaries
Limited effectiveness for enterprise-specific workflows without external data connections

Level Two: "Mother" – Workflow Builders¶

At this stage, AI transitions from reactive responses to executing multi-step, directed workflows. The example provided describes using Make.com to automate social media content through:

Retrieving articles from Google Sheets
Summarizing via Perplexity AI
Generating posts with Claude
Scheduling through Buffer

This represents "Retrieval Augmented Generation" (RAG)—AI performs searches before generating responses, integrating existing business data for contextual accuracy. Benefits include emerging ROI and manageable risk through human-defined rules.

Level Three: "J.A.R.V.I.S." – Autonomous Decision-Making¶

These agents reason, adapt, and respond independently in real time. An example demonstrates customer support automation that:

Addresses product defect inquiries beyond templated responses
Cross-references customer orders
Opens CRM tickets automatically
Applies goodwill discounts
Escalates unusual patterns to human leadership

At this level, AI independently learns and iterates.

Tools Behind Effective Agents¶

N8N¶

Visual workflow design enables developer management and debugging
Handles enterprise-scale tasks cost-effectively
Superior to alternative automation platforms for serious enterprise workflows

Make.com¶

Offers no-code and low-code automation
Intuitive debugging supports non-developers
Accessible for small-to-medium businesses

WTE Solutions' Approach: The company experiments with N8N for agent workflows, progressing beyond basic implementations toward Level Three agents that analyze customer sentiment across channels and auto-deploy improvements.

Use Case: Supply Chain Automation¶

An example workflow demonstrates agent capabilities:

Monitor inventory across global warehouses
Flag critically low stock levels
Generate vendor quotes from pre-approved suppliers
Prepare logistics summary reports

Best Practices¶

Prioritize Data Quality: Clean, connected information determines accuracy
Start Small: Begin with manageable workflows before scaling
Choose Appropriate Platforms: Terminology matters less than functional effectiveness
Focus on Team Effectiveness: Evaluate tools based on productivity and customer satisfaction improvements

Closing Statement¶

The post encourages businesses to determine their target maturity level and invites readers to explore Make.com, N8N, or contact WTE Solutions for implementation support.