DEV Community: Nilesh Barla

The 5 Levels of AI Agents: From Simple Bots to AGI

Nilesh Barla — Fri, 25 Jul 2025 19:36:28 +0000

AI agents are changing how we build software. They're moving from simple chatbots to smart systems that can work on their own.

Understanding these levels helps you pick the right tool for your project. It also prevents over-engineering simple problems.

What Makes AI "Agentic"?

Regular AI just responds to prompts. Agentic AI systems are different. They follow a sense-think-act cycle.

These systems remember past actions. They learn from results. Then they change their future behavior.

Think of it like having an AI assistant that gets better at helping you over time.

Level 1: Basic Rule-Based Systems

These are your simplest bots. They follow if-then rules without any real intelligence.

Examples:

Password reset bots
Simple FAQ chatbots
Basic customer service scripts

Pros: Predictable, cheap to build, handles high volume
Cons: Can't handle unexpected questions, breaks easily

Most legacy chatbots work at this level. They detect keywords and spit out pre-written responses.

Level 2: Smart Routers and Co-Pilots

Level 2 adds machine learning to basic automation. These systems make smarter decisions using patterns from data.

Microsoft Copilot is a great example. It suggests what to do next but doesn't take control.

Key features:

Learns from historical data
Helps humans make decisions
Routes requests intelligently

Think of these as smart assistants. They help you work faster but you stay in control.

Level 3: The Current Sweet Spot

This is where most AI agents live in 2025. These systems can handle multi-step tasks on their own.

They use large language models (LLMs) for planning. They break big goals into smaller tasks. They use external tools when needed.

What makes Level 3 special:

Maintains context across conversations
Uses tools like web search and databases
Learns from feedback in real-time

Real examples:

ChatGPT Code Interpreter
AutoGPT
OpenAI's Model Context Protocol (MCP)

These agents can analyze data, write code, and create reports. All with minimal human help.

Level 4: Multi-Agent Teams

Level 4 systems coordinate multiple specialized agents. Each agent has a specific role, like a team of experts.

Imagine having a CEO agent, engineer agent, and reviewer agent working together. They communicate through messages and shared memory.

Advanced features:

Self-improvement through reflection
Computer-using agents that control software directly
Distributed learning across agent teams

OpenAI's Operator is pushing toward Level 4. It can browse websites and fill out forms like a human would.

Level 5: The AGI Dream

Level 5 is full artificial general intelligence. These agents would work independently in any field.

They'd set their own goals. Solve completely new problems. Show creativity and self-awareness.

Reality check: We're nowhere near this yet. Current systems still need lots of human oversight.

Which Level Should You Use?

For simple automation: Start with Level 1 or 2
For complex workflows: Level 3 is your best bet
For specialized teams: Consider Level 4 pilot projects
For AGI: Wait a few more years (or decades)

The Bottom Line

Most production systems today use Level 3 agents. They offer the best balance of autonomy and reliability.

Level 4 is emerging for complex use cases. Level 5 remains theoretical.

Start with Level 3 for immediate wins. Build expertise before moving to more advanced levels.

The key is matching the right level to your specific needs. Don't over-engineer simple problems.

PS: I have written an article on "The 5 Levels of Agentic AI" with much details. Do check it out here.

How AI Superintelligence Will Transform Our Products

Nilesh Barla — Mon, 21 Jul 2025 12:57:01 +0000

The superintelligent AI, or any current AI, for that matter, is as good as the questions you ask it or the task/prompt you give it. You will be more productive and result-oriented if you know what you want. That means you should have a thorough knowledge of your domain.

The idea being, when we build a superintelligent AI system, it will use its general intelligence across all the disciplines and then align it with the user’s needs, behavior, and pain points. If we are able to prompt it with the right context of what we want, it will provide us with holistic data.

It will be crucial when developing effective products. You need to be extremely definite about what your customers want and what you want.

Read the full article on “How AI Superintelligence Will Transform Our Products” here: https://go.adaline.ai/Yr2innc

Evaluating AI Agents in 2025

Nilesh Barla — Thu, 10 Jul 2025 16:31:35 +0000

We’re all building AI agents now. But here’s the problem nobody talks about: most of us have no clue how to evaluate them.

Most teams build AI agents without knowing how to measure if they work.

We grab familiar metrics like accuracy scores. But agents don't just answer questions. They take action.

Traditional NLP metrics, such as BLEU scores, are ineffective for agents because they fail to capture autonomous behavior over time.

Your agent needs to browse websites, write code, and solve problems step by step.

A simple accuracy score won't tell you if it can do that.

What a good evaluation looks like:
OpenAI's Deep Research hits 51.5% on web browsing by examining hundreds of websites.

That's persistence. That's what separates good agents from bad ones.

Different models dominate different tasks - Gemini crushes coding while others lead reasoning.

Your web scraping agent needs different tests than your coding assistant.

The key insight is to match your benchmark to your use case, or you’ll get useless results.

I wrote this article that will help you to understand how to "Evaluate your AI Agents".