Going from AI Chat to AI Agents

Estimated read time: 5 minutes 30 seconds

I've spent the last two weeks diving deep in academic papers, YouTube videos and articles on AI to catch up on the advancements made in the last few years. It's not because I cared for all the technicalities, but from the lens of trying to understand what actually matters for founders building today.

Here's what I've learned. AI literacy in 2025 is not about understanding all the technical details. It's about knowing the fundamental shifts that change what is possible using these tools.

Going Beyond Chat Interfaces

There's a lot of buzz about "AI agents" versus chatbots. Let's be clear: this isn't some revolutionary technical breakthrough. It's mostly about how we're using the same underlying AI technology. The key difference is that while chatbots are limited to conversation, these "agents" are set up to actually take actions - sending emails, analyzing data, or updating systems. It's less about new technology and more about new applications of existing AI capabilities.

Think of ChatGPT as a brilliant intern who can write and analyze. But now imagine that intern could also access your systems, run analyses, and execute complex tasks. That's what AI agents represent – not just intelligence, but capability.

The Evolution to Multi-Agent Systems

We're moving beyond single AI assistants to teams of specialized AI agents. Imagine having a digital dream team: one agent handling customer service, another analyzing market data, and a third managing operations. Each specializes in what it does best, working together to solve complex problems.

This isn't science fiction. Companies are already building systems where multiple AI agents collaborate, like:

• Financial planning platforms where different agents handle investments, budgeting, and risk analysis

• Customer service systems where specialized agents handle different types of queries

The Building Blocks

When going through all this reading material, I kept encountering terms like "attention mechanisms" and "transformer models." But here's what founders actually need to understand:

1. Context is King

• AI can now process entire documents, conversations, and codebases

• Companies like Anthropic, OpenAI, and Google are continuously pushing the boundaries of how much context their models can handle

• What started as processing a few paragraphs is now expanding to entire books

• This isn't just a technical achievement - it means AI can understand and work with increasingly larger amounts of information, making it more useful for real-world applications

2. Thinking in Steps

• Through techniques like "chain of thought" prompting

• Instead of one-shot answers, AI can break down complex problems

• This enables more reliable, traceable decision-making

• More importantly, it lets AI tackle complex tasks by breaking them down into smaller steps

• When you see AI agents scheduling meetings or analyzing reports, it's this step-by-step thinking that makes it possible

• The AI isn't just following a script - it's thinking through the process like a human would

3. Exploring Possibilities

• Using approaches like "tree of thoughts"

• Instead of following a single path to a solution, AI can now explore multiple approaches simultaneously

• Think of it like having a team brainstorm different solutions, but at AI speed

• This is why AI agents can suggest multiple strategies for a problem

• It's also why they're getting better at creative tasks - they can explore and evaluate different possibilities

• When combined with step-by-step thinking, this makes AI capable of handling complex, open-ended tasks

The Democratization of AI

One of the most exciting shifts is that you don't need to be a technical expert anymore. The rise of no-code AI platforms means founders can build AI-powered solutions without writing code. It's like website builders – you focus on what you want to create, not how to code it.

Tools like Bubble's AI Actions, Zapier and Make's AI tools are making it possible to build AI-powered applications without deep technical expertise. Even Microsoft's Copilot and Google's Vertex AI are bringing AI capabilities to familiar interfaces. These platforms handle the complex technical work, letting founders focus on solving business problems.

This democratization means:

• More founders can experiment with AI

• Faster prototyping and iteration

• Focus on solving problems rather than technical implementation

How AI is Actually Changing Building

1. Products Launch More Complete

• AI helps handle edge cases early

• Better error handling and user experiences from day one

• Less "we'll add that later" because AI can help build it now

2. Teams Look Different

• Few specialized AI engineers vs. many general developers

• More focus on prompt engineering and AI orchestration

• Product people need to understand AI capabilities

3. Development Cycles Shift

• AI handles multiple iterations simultaneously

• Testing happens faster with AI-generated test cases

• Updates can roll out more frequently

Making Practical Decisions

After all this research, here's my framework for making AI decisions:

1. Start with the Problem, Not the Technology

• This is very similar to how you should think about generally any startup

• Don't ask "How can we use AI?"

• Ask "What problems could AI help us solve?"

• Focus on clear use cases, not capabilities

2. Understand the Limitations

• AI is incredibly powerful but not magic

• Know what current AI can and cannot reliably do

• Build with these limitations in mind

3. Think in Systems, Not Features

• AI isn't just a feature to add

• It's a capability that can transform entire workflows

• Consider the full impact on your product and users

Looking Forward

The most important thing I've learned? Catching up on AI advancements isn't a one time learning task – it's a journey. The field is moving too fast for anyone to know everything. The key is understanding enough to make informed decisions while staying flexible enough to adapt.

For you, this means:

• Focus on principles over specific implementations or tools

• Build for flexibility and iteration

• Stay curious but pragmatic

The winners in the space won't be those who know the most about AI. They'll be the ones who best understand how to apply it to solve real problems.

The technical details will keep changing. But these fundamentals – understanding context, thinking in steps and exploring possibilities – these are the advancements that can continue to expand what is possible with AI.