I truly believe in continuous learning. I’ve been diving deep into the world of artificial intelligence: reading blogs, attending webinars, and AI solutions and white papers to build better mental models. My goal is to build the foundational building blocks and a clear mental model to understand how AI has evolved and where different companies are positioning themselves in this fast-moving landscape.
I’ve come to the realization that AI is not a single leap but a series of transformational stages. Each step unlocks new capabilities and redefines what machines can do. My technical breadth and depth is allowing me to go deeper on this topic and why and how is this happening. Today, I’m just trying to build a simple mental model for AI evolution. In future blogs, I’ll talk about challenges and potential opportunities as well.
Stage 1: IFTTT – The Foundation of Automation
It started with IFTTT, i.e., If This Then That, a simple and powerful concept that introduced millions to the possibilities of automation. Many startups have leveraged this phenomenon, such as Zapier. With basic conditional logic such as “If it rains, then send me a text,” IFTTT demonstrated how connecting different services could create useful workflows.
Key characteristics:
- Simple trigger-action relationships
- Rule-based automation
- Single-step or multi-step reactions
- User-defined conditions
While basic, IFTTT established the crucial foundation that different digital services could work together seamlessly.
Stage 2: LLM – The Language Revolution
Before 2022, AI was a distant milestone. No one in mainstream imagined the power of AI. In 2022, the introduction of Large Language Models (LLMs) such as GPT, Claude, and LLaMA brought a quantum leap in AI capabilities. These systems could understand and generate human language with unprecedented sophistication, engaging in complex conversations and reasoning about abstract concepts.
Key breakthrough:
- Natural language understanding and generation
- Complex reasoning capabilities
- Contextual awareness
- Creative and analytical thinking
I remember using the initial version of ChatGPT. It was using 2-year-old data, and LLMs showed the potential. Having said this, LLMs had a significant limitation: they were isolated systems with no access to real-time information or external tools.
Gordon Moore’s theory, known as Moore’s Law, predicted that the number of transistors on a microchip would double approximately every two years, while the cost of computers would decrease.
Everyone assumed the same trajectory, but tremendous progress in AI has surpassed Moore’s law so far.
Stage 3: RAG – Bridging Knowledge Gaps
Retrieval-Augmented Generation (RAG) solved the knowledge limitation by combining LLMs with external information retrieval systems. AI could access current data, documents, and databases to provide up-to-date and contextually relevant responses.
RAG was a trigger point for me to evaluate an AI solution for my personal use case, and I started experimenting with RAG and LLMs.
The enhancement:
- Real-time information access
- Integration with external knowledge bases and started driving adoption inside organizations
- More accurate and current responses
- Reduced hallucinations
RAG transformed LLMs from static knowledge repositories into dynamic, informed assistants.
Stage 4: AI Agents – Tools in Action
AI agents were a natural progression, i.e., combining LLM intelligence with the ability to use tools and APIs. These systems could perform multi-step tasks, access databases, send emails, and interact with various software applications. Again, OpenAI led this stage as well.
New capabilities:
- Multi-step task execution
- Tool and API usage
- Structured workflows
- Basic autonomous operation
AI Agents moved beyond conversation to actual task completion, but still operated within defined parameters.
Stage 5: Agentic AI – Autonomous Intelligence
Honestly, I wasn’t expecting the next evolution to come so soon. Agentic AI systems exhibit true autonomy. It can break down complex problems, adapt new strategies, and work toward long-term objectives with minimal human supervision.
Advanced features:
- Self-directed planning and execution
- Adaptive problem-solving
- Goal persistence across sessions
- Dynamic strategy adjustment
These systems began to exhibit behavior resembling human-like reasoning and decision-making. The AWS Summit provided me with a quick glimpse of the realm of possibilities, challenges, and potential as well. On flipside, can a single agent adapt and become like Eagle Eye in the future?
Stage 6: Multi-Agent Systems – The Collaborative Future
The next frontier involves multiple AI agents working together seamlessly. Instead of single powerful systems, most likely an ecosystem of specialized agents that can communicate, coordinate, and collaborate to tackle complex challenges. It is very much applicable in a corporate scenario where redundant activities can be eliminated and efficiency can be gained. For example, a data analyst performing a number crunching to generate a business report can be delegated to an ecosystem of specialized agents.
The paradigm shift:
- Distributed intelligence across multiple agents
- Specialized roles and capabilities
- Inter-agent communication and coordination
- Emergent collective behaviors
- Scalable and fault-tolerant systems
To contextualize all these evolutions through a simple example would be easier for everyone to comprehend and build their own mental model. So, decided to contextualize AI evolution using a weather example.
“What’s the weather in Atlanta this weekend?”
This simple request shows how AI systems become more capable and intelligent at each step.
Stage 1: IFTTT – Basic Automation
Rule set: “If it rains tomorrow in Atlanta, send me a text to remind me to carry an umbrella.”
- Only follows preset rules
- No understanding or reasoning
- One-off, static automation
Stage 2: LLM – Language Understanding
You ask: “What’s the weather in Atlanta this weekend?”
- The AI understands the question perfectly
- But without access to the latest info, it can’t give live answers
- It replies: “Sorry, I don’t have current weather info.”
Stage 3: RAG – Live Data Retrieval
You ask: “What’s the weather in Atlanta this weekend?”
The AI queries a weather API and replies: “It will rain on Saturday and be sunny on Sunday.”
- Combines language understanding with real-time data
- Accurate, current answers
- But no action, just information
Stage 4: AI Agent – Acting on the Info
You ask: “Should I move my outdoor event to Saturday?”
- AI checks the weather
- Suggest Sunday due to better weather
- May also check your calendar and offer to reschedule
- Uses tools, executes tasks, but waits for your prompts
Stage 5: Agentic AI – Planning & Autonomy
You ask: “Should I reschedule my outdoor event this weekend?”
- AI understands your goal (a successful outdoor event)
- Checks multiple forecasts
- Reviews venue availability
- Notifies attendees of the new date
- Order stuff from the e-commerce site
- All done autonomously
Stage 6: Multi-Agent Systems – Teamwork Behind the Scenes
You ask: “Should I reschedule my outdoor event this weekend?”. Multiple agents now collaborate:
- Weather Agent fetches forecasts
- Calendar Agent finds availability
- Logistics Agent contacts the venue
- Messaging Agent drafts and sends invites
You simply get a message:
“Event rescheduled to Sunday at 4 PM. Venue and guests have been notified.”
Why This Evolution Matters
This progression represents more than just technological advancement; it is a fundamental shift in how we think about artificial intelligence. Each stage has built upon the previous, creating increasingly sophisticated systems that can:
- Understand context better than ever before
- Access and process information from multiple sources
- Take autonomous action to achieve goals
- Collaborate effectively with other AI systems and humans
Looking Ahead
It is clear now that we are all entering the next phase of true multi-agent systems. These are not just better tools; they are collaborative AI entities that can work together, reason across domains, and augment both human creativity and decision-making.
This shift is helping me build a clearer mental model of disruption and opportunity.
As an investor, it sharpened my view on which services might be replaced or reimagined for greater efficiency.
As a professional, it influences how I approach a solution using AI and moving away from legacy toolchains toward adaptive, forward-looking methods.
Rather than feeling anxious about the pace of change, I see adaptation and continuous learning as the way forward.