What is Generative AI?
Definition
Generative AI is a type of Artificial Intelligence that can create new content based on what it has learned from existing data.
It doesn’t just analyze or classify data — it generates:
- Text
- Images
- Code
- Audio
- Video
In simple words:
Generative AI creates something new rather than just responding with predefined answers.
How Generative AI Works (Simplified)
Generative AI models:
- Learn patterns from large datasets
- Understand relationships between words, images, or sounds
- Generate new output that follows those learned patterns
Most generative AI systems are based on:
- Large Language Models (LLMs) for text
- Diffusion / GAN models for images
Examples of Generative AI
1️⃣ Text Generation
✅ ChatGPT, Copilot, Gemini
Example:
- Writing emails
- Creating documentation
- Generating SQL queries
- Summarizing reports
You ask:
“Write an email to request database downtime”
Generative AI creates a new email, not copied from anywhere.
2️⃣ Code Generation
✅ GitHub Copilot
Example:
- Auto‑generates Python, SQL, Java code
- Suggests optimized queries
📌 Useful for DBAs:
- Generate scripts
- Write monitoring queries
- Create automation logic
3️⃣ Image Generation
✅ DALL·E, Midjourney
Example:
- Create system architecture diagrams
- Generate design mockups
You describe → AI generates a new image.
4️⃣ Database & IT Example
Generative AI can:
- Generate SQL tuning suggestions
- Create incident RCA summaries
- Write SOP or runbooks
✅ Example:
Automatically generating a root cause analysis after a database outage
Key Characteristics of Generative AI
| Aspect | Description |
|---|---|
| Output | New content |
| Creativity | High |
| Autonomy | Limited |
| Decision Ownership | Human |
What is Agentic AI?
Definition
Agentic AI refers to AI systems that can act autonomously to achieve a goal by:
- Planning steps
- Making decisions
- Taking actions
- Adjusting based on results
In simple words:
Agentic AI doesn’t just generate content — it decides what to do next and does it.
How Agentic AI Works
An Agentic AI system typically has:
- Goal – What needs to be done
- Planning capability – Breaks goal into tasks
- Tools access – APIs, databases, scripts
- Feedback loop – Learns from results
- Decision logic – Chooses next action
It often uses Generative AI as one component, but adds autonomy.
Real‑World Examples of Agentic AI
1️⃣ AI Operations Agent (IT / DBA Example) ✅
🔹 Goal: “Keep database running optimally”
Agentic AI actions:
- Monitor CPU, memory, waits
- Detect abnormal behavior
- Analyze historical patterns
- Decide: scale resources / kill session / raise incident
- Execute automatically or seek approval
📌 Unlike Generative AI:
- It does not wait for a prompt
- It acts on its own
2️⃣ Self‑Healing Systems
✅ Common in modern DevOps
Example:
- App crashes
- Agentic AI detects failure
- Restarts service
- Verifies recovery
- Notifies team
No human prompt required.
3️⃣ Autonomous Customer Support Agent
🔹 Goal: “Resolve customer issues”
Steps:
- Understand issue
- Query CRM
- Reset password
- Update ticket
- Close issue
📌 A chatbot that only replies is Generative AI
📌 A bot that resolves the issue end‑to‑end is Agentic AI
4️⃣ AI Shopping Agent
You say:
“Buy the cheapest laptop with 16GB RAM”
Agentic AI:
- Searches websites
- Compares prices
- Applies filters
- Makes decision
- Places order (with rules)
Key Characteristics of Agentic AI
| Aspect | Description |
|---|---|
| Output | Actions + decisions |
| Autonomy | High |
| Goal‑oriented | Yes |
| Tool usage | Yes |
| Self‑correction | Yes |
Generative AI vs Agentic AI (Clear Comparison)
| Feature | Generative AI | Agentic AI |
|---|---|---|
| Core Purpose | Create content | Achieve goals |
| Autonomy | Low | High |
| Requires Prompt | Yes | Often No |
| Takes Actions | ❌ No | ✅ Yes |
| Uses Tools | Limited | Extensive |
| Example | ChatGPT writing email | AI auto‑healing a DB |
Simple Real‑Life Analogy
✅ Generative AI
Like a skilled writer who creates content when asked.
✅ Agentic AI
Like a project manager who decides what to do, assigns tasks, and ensures results.
Combined Example (Very Important)
Most modern systems use both together:
📌 Agentic AI + Generative AI
- Agent plans and decides
- Generative AI produces text, code, explanations
✅ Example: AI SRE Agent:
- Detects incident (Agentic)
- Diagnoses cause (Agentic)
- Generates RCA document (Generative AI)
- Executes fix (Agentic)
- Sends summary email (Generative AI)
Interview‑Ready Summary (2–3 lines)
“Generative AI focuses on creating new content like text, code, or images based on learned patterns. Agentic AI goes a step further by autonomously planning, deciding, and taking actions to achieve a goal, often using Generative AI as part of its decision process.”
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