ABCs of AI Agents

Author

Govardhana M K
January 29, 2025

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TechOps Examples

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Along with a use case deep dive, we identify the remote job opportunities, top news, tools, and articles in the TechOps industry.

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IN TODAY'S EDITION

🧠 Use Case

  • ABCs of AI Agents

🚀 Top News

👀 Remote Jobs

📚️ Resources

📢 Reddit Threads

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🧠 USE CASE

ABCs of AI Agents

McKinsey’s most recent “State of AI” survey echoes more than 72% of companies surveyed are already deploying AI solutions with a growing interest in generative AI.

This reaffirms that AI agents are going to play a vital role in AI transformation bringing improved productivity, reduced costs, enhanced decision making and a better customer experience.

And look at this….

AI Agent Engineer roles are slowly coming to eat up DevOps and many other roles.

CEOs and CTOs started asking ‘How can AI agents optimize our systems?’

This top down push is unconventional but happening.

Let this sink in, AI agents are going to:

🔶 Prepare for Kubernetes upgrades

🔶 Onboard tools pipelines and users

🔶 Analyze cloud costs and suggest actions

The use cases in DevOps, SRE and Cloud are immense.

Good thing? Kubernetes and DevOps still stay on top.

I anticipate there will be a convergence between these roles, and whoever is qualified will transition smoothly to the new AI Agents world.

The best thing to do is to understand and get prepared to tackle this evolution.

Hence, along with the usual Kubernetes, Cloud, and DevOps use cases, starting today, 1 or 2 editions out of the usual 5 editions in a week will be focused on AI agents.

When many of us don’t know the difference between an LLM and LAM, jumping into arbitrary topics like those below is pointless.

  1. AI agent workflows

  2. Building AI agents

  3. Deploying AI agents in cloud

  4. Optimizing DevOps with AI

  5. AI agents in security

We start with the basics and slowly move toward use cases.

To begin with,

What is an AI Agent ?

An AI Agent understands problems, doesn’t just analyze data but makes decisions and takes real actions to solve them.

It learns, adapts, and works independently or with humans to automate tasks in DevOps, security, cloud, customer support and many domains.

Is an AI Agent Just Another Chatbot ?

Not really. Chatbots follow scripts and only respond based on pre set rules. They are reactive and limited to what they are programmed to say.

AI Agents go beyond just replying. They analyze information, take proactive actions, learn from past interactions, and solve complex problems on their own without waiting for human input.

Examples of AI Agents

AutoGPT - AI agent for autonomous digital tasks.

MetaGPT - AI agent for multi agent collaboration and software development.

AI agents world is not standalone, so understanding the basic terminologies in AI comes with it.

It is hard to cover everything in a single visual, hence a few more important elements you need to understand.

Token → Smallest unit of text AI models process like words or parts of words.

Chunking → Splitting large texts into smaller pieces for AI processing.

Retrieval Augmented Generation (RAG) → AI model that fetches relevant data before generating responses.

Sections → Logical divisions in documents for structured AI understanding.

Foundation Model → A pre trained AI model used as a base for specialized applications.

Generative Pre trained Transformers (GPT) → Advanced AI model that generates human like text.

Prompt Engineering → Crafting prompts to get the best responses from AI models.

Transformer → The deep learning architecture behind modern AI models like GPT.

Topic Modeling → AI technique for identifying main themes in large text datasets.

With this basic knowledge, in the next episodes of this AI series, we would learn:

  • How an AI agent works

  • Types of AI agents

  • AI agent engineer roadmap

  • How to build an AI agent

  • AI agent use cases and many more…

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