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Privacy isn't dead. It's just being rebuilt. The old internet promised us "move fast and break things." We moved fast. We broke things. Now we're realizing what we broke: privacy. Every LLM you use wa
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Muhammad Zulqarnain | Full Stack AI Engineer & Geospatial Developer
16 posts
A blog by Muhammad Zulqarnain — Full Stack AI Engineer & Geospatial Developer based in Turku, Finland. I write about RAG systems, LLMs, Prompt Engineering, Next.js, TypeScript, and geospatial development. Practical insights, deep dives, and real-world AI solutions.
The AI revolution isn't about better models anymore. It's about agents that actually do work.
We're entering the agentic era of AI.
For years, AI meant asking ChatGPT questions. It responded. You acted.
Now: You ask Claude. It:
Writes code
Tests it
Debugs it
Deploys it
Monitors it
No manual steps. No waiting for human approval at each stage.
That's the difference between a copilot and an agent.
1. Goal Definition You tell it what you want done. Not how to do it. "Build me a landing page" vs "Open VS Code, create an HTML file..."
2. Tool Use Agents have access to tools:
Code execution
Database queries
API calls
File systems
Browser automation
3. Autonomous Decision Making Agents decide:
Which tool to use
When to use it
What to do if it fails
Whether it solved the problem
4. Feedback Loops Agents observe results and adapt: "The test failed. Here's why. Let me fix it."
Today's world:
Dev writes feature: 4-8 hours
QA tests: 2-4 hours
Debugging: 1-2 hours
Total: ~6-14 hours of human time
Agentic AI world (coming):
Agent writes feature: 5 minutes
Agent writes tests: 2 minutes
Agent debugs: 1 minute
Human reviews: 5 minutes
Total: ~13 minutes
That's 30x faster. For software development.
1. Reasoning Models Claude 3.5 Sonnet, GPT-4o can reason through problems. They don't just generate. They think.
2. Tool APIs Make everything callable:
GitHub API
Slack API
Stripe API
Your internal APIs
3. Orchestration Layer Agents need to know:
What tools exist
When to call them
How to handle failures
Whether to ask humans
4. Memory Systems Agents that learn from runs:
Store what worked
Store what failed
Apply learnings to future tasks
"Won't agents replace engineers?"
No. But engineers who use agents will replace engineers who don't.
The engineers who:
Build agentic systems
Design tool ecosystems
Set agent constraints
Handle edge cases agents miss
They're the future. Not the ones pretending agents don't exist.
Reliability - Agents hallucinate. They make mistakes. Not 100% trusted yet.
Cost - Running Claude for complex multi-step tasks costs money. Sometimes more than hiring.
Integration - Your legacy systems don't have APIs. Agents need well-defined interfaces.
Safety - An agent with database access is powerful. And dangerous.
Observability - What did the agent do? Why did it fail? Hard to debug.
Specialized Agents - Not general Claude. Agents trained for:
Code generation
Data analysis
Customer support
Content creation
Cheaper Inference - Token costs drop. Agentic workflows become profitable even for simple tasks.
Better Reliability - Models improve. Hallucinations decrease. Agents go from 85% to 95%+ accuracy.
Standardized APIs - Every service gets agent-ready APIs. Tool ecosystem explodes.
Agentic Platforms - Companies like Anthropic, OpenAI, others will release agent frameworks. Similar to:
How everyone uses React for frontend
How everyone uses Docker for containerization
Replit Agent - Autonomous coding
Cursor - AI-native IDE with agents
Glean - Search agents for enterprise
Harvey - Legal agents
GitLab's AI - Coding agents
They're not selling AI. They're selling agents that do actual work.
If you're building:
Learn prompt engineering (understand agent reasoning)
Learn tool design (APIs agents will call)
Learn orchestration (how to chain agents)
Build one agentic product. Just one. Ship it.
You don't need to be an AI researcher. You need to be the engineer who understands:
What agents can do
What they can't do yet
How to make them more reliable
How to build value on top of them
By 2030:
Most coding is agentic
Most content is AI-assisted
Most analysis is agentic
Most operational tasks are agentic
Not because agents are perfect. But because they're good enough. And they're 10x faster.
The question isn't "Will agents replace me?"
It's "Will I be the one building agents?"
Choose wisely.omous Intelligence
