Agentic AI - Simple to complex
AI agents are everywhere today, but not all of them are similar. Some AI agents can only answer simple questions but others can perform multiple tasks, coordinate with other systems, or act with minimum human input. In this blog, I will take you through different AI models, from simple to complex.
Rule-Based Automation: The Simplest “AI”
Rule based automations are the systems that follow simple logic like ‘If this then that’ logic. There is no real thinking or learning involved, they just do what they’re told in the predefined conditions. Like FAQ chatbots with prefilled answers,or automated email responses. Most of the agents previously used to be on this level.
These types of agents are cheap and predictable but not intelligent. They are …
Agentic AI - Simple to complex
AI agents are everywhere today, but not all of them are similar. Some AI agents can only answer simple questions but others can perform multiple tasks, coordinate with other systems, or act with minimum human input. In this blog, I will take you through different AI models, from simple to complex.
Rule-Based Automation: The Simplest “AI”
Rule based automations are the systems that follow simple logic like ‘If this then that’ logic. There is no real thinking or learning involved, they just do what they’re told in the predefined conditions. Like FAQ chatbots with prefilled answers,or automated email responses. Most of the agents previously used to be on this level.
These types of agents are cheap and predictable but not intelligent. They are useful for simple automation but are not capable of decision making.
Co-Pilots and Routers: Smart Helpers
These types of agents operate on machine learning systems. They can classify, route or recommend things based on the previous data patterns, but they still can’t act independently. You are supposed to direct the workflow in order to make them work properly. For eg. Email auto-sorting, or support model that can redirect the request to the correct department ( eg - telecom or banking service agents).
With these types of agents, you can decide what the next step would be, but you are still the one with your hands on the steering wheel. They are helpful but are not autonomous.
Tool-Using Agents
This is where things get interesting. These types of agents can break tasks into steps, call external APIs, and maintain context throughout a workflow. This is where real agentic AI lives in the current scenario. For eg, an agent that searches a database, summarizes the findings and updates a website/blog automatically. At this level, we can give Large Language Models (LLMs) more context by connecting them to external knowledge bases, allowing them to retrieve up-to-date, factual information before generating answers, making outputs more accurate, specific, and trustworthy, without needing costly retraining.
Multi-Agent Systems
In a multi agent system, instead of one agent doing everything,multiple agents act simultaneously. Multiple specialized agents work together, review each other’s outputs, and improve over time. One agent gathers data while another analyzes it, and a third reviews the results for quality control. This is quite powerful but needs a lot of experiments and sophisticated design principles. For eg. Agents like Droidrun, Browser use etc.
Fully Autonomous AGI
This is the most advanced AI system which is still theoretical. It is believed to set its own goals, adapts across any domain, and operates without human oversight. True AGI would think and plan like a human expert. The technology is not there yet. Most of the AI systems currently are either tool using agents or multi agent systems.
Real-World Applications
Agentic AI is already creeping into our daily lives through:
Smart Homes: Systems that coordinate temperature and security. Self-Driving Software: Multiple models working together to navigate roads safely. Virtual Assistants: Scheduling meetings and managing calendars across different apps.