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Hey there! I'm currently working as an Associate DevOps Engineer, and I'm diving into popular DevOps tools like Azure Devops,Linux, Docker, Kubernetes,Terraform and Ansible. I'm also on the learning track with AWS certifications to amp up my cloud game. If you're into tech collaborations and exploring new horizons, let's connect!
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DevOps Journey with M Hassan
174 posts
I am writing these blogs because I recently completed a comprehensive DevOps course where I gained in-depth knowledge of the topics mentioned. As I progressed through the course, I realized the importance of having a concise and accessible resource to revise and reinforce my understanding of each topic. Therefore, I decided to create cheat sheets in the form of blog posts. These cheat sheets will not only serve as a handy reference for myself but also benefit others who are also interested in mastering DevOps concepts. By documenting each topic and providing concise explanations, I aim to create a valuable resource that simplifies complex concepts and facilitates hands-on practice. This way, I can solidify my own understanding while helping others on their DevOps journey.
The world of AI is rapidly evolving. We are moving from simple chatbots that answer questions to intelligent agents that can think, plan, and take actions.
In this blog, I’ll break down the key concepts from Lecture 1, including:
Chatbot vs Agent differences
Agent architecture
Why the “Agent Era” has started
Operational challenges
Scaling AI agents in production
Let’s dive in.
The lecture begins by explaining the core difference.
A chatbot is reactive — it waits for input and produces a response.
An AI agent, however, is proactive — it can decide, plan, and execute tasks.
According to the slide on page 1, the key differences are:
Role: Interface
Function: Reactive
Output: Answers questions
Status: No real decision-making
Role: Operator
Function: Goal-driven
Output: Executes workflows
Status: Makes decisions
👉 The best summary from the slide:
“One waits for input; the other takes action.”
1
The architecture is completely different.
As shown on page 2, chatbot processing is simple:
Input → LLM Prediction → Output
Characteristics:
Stateless
Single-shot response
No memory
No tool usage
Agents operate in a continuous loop:
Perception (understanding input)
Reasoning (planning)
Tool usage
Action
Reflection (self-correction)
This makes agents:
Stateful
Multi-step
Self-correcting
Capable of retries
1
According to page 3, three major technological advancements made AI agents possible:
Modern models like GPT-4 can plan tasks efficiently.
Universal function calling allows agents to interact with APIs easily.
Vector databases allow agents to retain context and history.
Because of these changes, AI is evolving:
From AI as a Copilot → To AI as a Teammate.
1
A powerful diagram on page 4 explains the four core components.
Let’s break them down.
Inputs from:
Databases
Slack messages
Webhooks
APIs
Handles:
Planning
Tool selection
Reflection
Stores:
Chat history
Vector embeddings
State information
Executes tasks:
API calls
Scripts
File operations
1
A very important concept appears on page 5.
Deterministic behavior
Predictable latency (~200ms)
Fixed cost
Probabilistic behavior
Variable latency (reasoning loops)
Cost varies widely
This means:
👉 Agents are powerful but harder to predict.
Running agents in production introduces new risks.
According to page 6, major hazards include:
Infinite reasoning loops can burn budget quickly.
Traditional CPU metrics are insufficient — we need to track decision paths.
Agents may have shell or system access, creating risk.
Maintaining memory consistency across failures is difficult.
1
To manage agents safely, a new discipline is emerging: AgentOps.
The diagram on page 7 highlights key principles:
Flight Recorder → Capture reasoning chains
Circuit Breaker → Stop runaway loops
Guardrails → Prevent dangerous actions
Goal:
Operate autonomous systems reliably and securely.
1
According to page 8, real-world deployments require handling:
10,000 parallel agent loops
50+ external APIs
Distributed state management
This shows:
👉 Deploying agents is easy.
👉 Orchestrating them at scale is the real challenge.
The final slide (page 9) gives a powerful insight:
Yesterday → Managing stateless microservices
Tomorrow → Managing stateful decision systems
This changes the DevOps role dramatically:
You are no longer just keeping servers up — you are keeping intelligence aligned.
1
AI agents represent a massive shift in computing:
From reactive systems → To autonomous decision makers
From static workflows → To dynamic reasoning loops
From infrastructure management → To intelligence management
This is why we are entering the Agent Era.