Some databases are powering AI agents, And AI agents are powering other databases.

AI’s next leap isn’t about 𝘀𝗶𝘇𝗲. It’s about 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆. We’re moving from single, oversized models to 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁𝘀 — each with a clear role, purpose, and tools.

𝗖𝗥𝗔𝗙𝗧: 5 𝗦𝘁𝗲𝗽𝘀 𝗼𝗳 𝗔𝗴𝗲𝗻𝘁 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 ✅ 𝗖 – 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝗱 ✅ 𝗥 – 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗲 ✅ 𝗔 – 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 ✅ 𝗙 – 𝗙𝘂𝗹𝗳𝗶𝗹 ✅ 𝗧 – 𝗧𝘂𝗻𝗲

Career BLINDSPOTS

Generative AI Application Reference Architecture. Comment "interested" for detailed breakdown and high-res image.

Data hygiene is critical to models. Avoidance leads to diarrhoea.

Agent2Agent Protocol is an Overkill. Until your architecture demands it.

AI-fluencers started calling LLMs "traditional", I am waiting to see who will name them "Legacy" first.

You are not late. You are just waiting too long.

Animate your architecture diagrams, infographics, seamlessly and smoothly with https://t.co/lThpk28erx

Stop Guessing Your Data Model. Start engineering it with intent. Most system failures don’t start in code, they start in data design. Performance bottlenecks? Analytics that don’t scale? Integration nightmares? These are symptoms. The root cause? Poor data modelling.

Key positions in ML projects. Understand the lifecycle of ML-projects and the roles involved in the lifecycle.

If you are into Software Development, you must know these. The API Architecture styles you must know. Each style serves a specific purpose.

Was it helpful? Don't forget to follow and repost.

Key Takeaways

Agentic-AI in Smart Systems

RAG in Action

Real-world Applications

RAG vs Agentic-AI: The Difference

Understand Agentic-AI