If you’re tired of feeling behind, confused, or stuck when it comes to AI, you’re exactly who I create for.

Most people fall into one of two categories when it comes to AI:
1️⃣ Curious but disconnected i.e. want to learn, but don’t know where to start.
2️⃣ Overwhelmed and burnt out i.e. too many tools, trends, and no clarity.

So they pause… and put it off.

But ignoring AI today is like ignoring the internet in early 2000's. 
The truth? AI is not optional anymore. And avoiding it won’t make it go away. 

The real solution? Start small. Stay consistent. Learn fast. Apply faster.

That’s where I come in.

I simplify AI for students, working professionals, and founders.

I focus on:
• GenAI & Agentic AI
• AI Tools for Careers & Education
• Startup Execution & Product Strategy

As a startup founder and AI strategist, I work at the intersection of GenAI, agentic systems, product strategy, and execution. My background includes a Computer Science degree from the University of Hong Kong ($100K+ scholarship), building startups across borders, and creating content to help people.

If you want to use AI, not just read about it —> follow me.
Reach out: rahul@scholarbaniya.site

Most non-tech people learning AI don’t get this.

I've explained it in a simple way below.

1. 𝗨𝘀𝗲𝗿
Everything starts with the 𝗨𝘀𝗲𝗿.
• The user wants something done
• Example: “𝘍𝘪𝘯𝘥 𝘵𝘩𝘦 𝘣𝘦𝘴𝘵 𝘭𝘢𝘱𝘵𝘰𝘱 𝘶𝘯𝘥𝘦𝘳 $1000” or “𝘞𝘳𝘪𝘵𝘦 𝘢𝘯 𝘦𝘮𝘢𝘪𝘭”

2. 𝗤𝘂𝗲𝗿𝘆
The user’s input becomes a 𝗤𝘂𝗲𝗿𝘆.
• This is the raw request sent to the AI Agent
• It can be a question, instruction, or task
Think of it as: “𝘞𝘩𝘢𝘵 𝘥𝘰𝘦𝘴 𝘵𝘩𝘦 𝘶𝘴𝘦𝘳 𝘸𝘢𝘯𝘵?”

3. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 (𝗧𝗵𝗲 𝗕𝗿𝗮𝗶𝗻 / 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗿)
The 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 is the central decision-maker.
It:
• Understands the query
• Decides what to do next
• Chooses whether to use memory, tools, or the LLM
• Manages the full flow from input → output
It doesn't just answer. It plans, thinks, and acts.

4. 𝗠𝗲𝗺𝗼𝗿𝘆
The AI Agent connects to 𝗠𝗲𝗺𝗼𝗿𝘆 to remember things.
𝗮) 𝗦𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝗠𝗲𝗺𝗼𝗿𝘆
• Remembers current conversation context
• Example: what the user said 2 messages ago
𝗯) 𝗟𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗠𝗲𝗺𝗼𝗿𝘆
• Stores important information for future use
• Example: user preferences, past tasks, saved facts
Memory helps the agent give 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗮𝗻𝗱 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 answers.

5. 𝗧𝗼𝗼𝗹𝘀
If the task needs external help, the agent uses 𝗧𝗼𝗼𝗹𝘀.
𝗮) 𝗪𝗲𝗯 𝗦𝗲𝗮𝗿𝗰𝗵
• To get real-time or updated information
• Example: latest news, prices, documentation
𝗯) 𝗔𝗣𝗜𝘀
• To interact with other software or services
• Example: send emails, fetch data, book meetings
Tools allow the agent to 𝘁𝗮𝗸𝗲 𝗮𝗰𝘁𝗶𝗼𝗻𝘀.

6. 𝗟𝗟𝗠 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹)
The AI Agent sends instructions to the 𝗟𝗟𝗠.
The LLM is responsible for:
• Understanding language
• Generating text
• Reasoning and logic
But the LLM 𝗼𝗻𝗹𝘆 𝗿𝗲𝘀𝗽𝗼𝗻𝗱𝘀 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝘄𝗵𝗮𝘁 𝗶𝘁’𝘀 𝗴𝗶𝘃𝗲𝗻.

7. 𝗣𝗿𝗼𝗺𝗽𝘁
To guide the LLM, the agent builds a 𝗣𝗿𝗼𝗺𝗽𝘁.
The prompt contains:
𝗮) 𝗥𝗼𝗹𝗲
• Defines 𝘸𝘩𝘰 the AI should act as
• Example: “You are a financial expert”
𝗯) 𝗧𝗮𝘀𝗸
• Defines 𝘸𝘩𝘢𝘵 needs to be done
• Example: “Analyse growth trends”
A good prompt = better output.

8. 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴
The LLM then performs 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴.
This includes:
𝗮) 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴
• Breaking the task into steps
• Deciding the best approach
𝗯) 𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻
• Checking if the answer makes sense
• Improving clarity or correctness
This is what makes AI feel “smart”.

9. 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲
After reasoning, the LLM generates a 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲.
• The agent reviews it
• May store useful info in memory
• Then sends it back to the user

10. 𝗕𝗮𝗰𝗸 𝘁𝗼 𝗨𝘀𝗲𝗿
Finally, the 𝗨𝘀𝗲𝗿 𝗿𝗲𝗰𝗲𝗶𝘃𝗲𝘀 𝘁𝗵𝗲 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲.
• The loop continues if the user asks again
• Memory and context keep improving the experience

This is quite basic but many beginners still struggle with it

✅ Repost for non-technical people in your network learning AI.

Rahul Agarwal

What AI skills should you master in 2026?

I've explained each with my learnings below.

𝗦𝘁𝗲𝗽 1 – 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
• Uses clear, structured, goal-driven instructions.
• Adds context, constraints, and expected outputs.
𝗘.𝗴: ChatGPT, Claude, Gemini.

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:
Good prompts don’t ask for answers.
They 𝗹𝗶𝗺𝗶𝘁 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗔𝗜 𝗶𝘀 𝗮𝗹𝗹𝗼𝘄𝗲𝗱 𝘁𝗼 𝗱𝗼.
___________
𝗦𝘁𝗲𝗽 2 – 𝗔𝗜 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻
• Connects AI with business tools and apps.
• Automates repetitive tasks end-to-end.
𝗘.𝗴: Zapier, Make, n8n.

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:
Automation breaks more often than it runs.
The skill is designing 𝘀𝗮𝗳𝗲 𝗿𝗲𝗰𝗼𝘃𝗲𝗿𝘆, not perfect flows.
___________
𝗦𝘁𝗲𝗽 3 – 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀
• Agents can plan, remember, and collaborate.
• Suitable for complex, multi-step workflows.
𝗘.𝗴: CrewAI, LangGraph, AutoGen.

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:
Agents fail silently.
If you can’t explain 𝘸𝘩𝘺 an agent acted, it’s already dangerous.
____________
𝗦𝘁𝗲𝗽 4 – 𝗥𝗔𝗚
• Connects AI to private or real-world data.
• Grounds answers in documents, not guesses.
• Reduces hallucinations and errors.
𝗘.𝗴: LangChain, Vectara, LlamaIndex.

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:
Most RAG systems fail before retrieval.
The real problem is 𝗯𝗮𝗱 𝗰𝗵𝘂𝗻𝗸𝗶𝗻𝗴, 𝗻𝗼𝘁 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀.
____________
𝗦𝘁𝗲𝗽 5 – 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 & 𝗖𝘂𝘀𝘁𝗼𝗺 𝗚𝗣𝗧𝘀
• Adapts models for specific tasks or brands.
• Improves relevance and consistency.
𝗘.𝗴: OpenAI GPT Builder, Hugging Face, Cohere.

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:
Fine-tuning rarely improves intelligence.
It mainly improves 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆.
____________
𝗦𝘁𝗲𝗽 6 – 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗔𝗜
• AI understands text, images, audio, and video.
• Works across multiple formats in one flow.
𝗘.𝗴: GPT-4, Gemini, Grok.

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:
Multimodal isn’t about images or audio.
It’s about 𝘀𝗵𝗮𝗿𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗮𝗰𝗿𝗼𝘀𝘀 𝗳𝗼𝗿𝗺𝗮𝘁𝘀.
____________
𝗦𝘁𝗲𝗽 7 – 𝗔𝗜 𝗦𝗲𝗮𝗿𝗰𝗵 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
• Replaces traditional SEO for AI-powered search.
• Helps AI engines find and recommend information.
𝗘.𝗴: Profound, Perplexity.

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:
AI search doesn’t rank content.
It ranks 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆.
_____________
𝗦𝘁𝗲𝗽 8 – 𝗔𝗜 𝗧𝗼𝗼𝗹 𝗦𝘁𝗮𝗰𝗸𝗶𝗻𝗴
• Combines multiple AI and productivity tools.
• Builds complete end-to-end workflows.
𝗘.𝗴: LangChain, Zapier.

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:
Every added tool increases hidden complexity.
The skill is knowing 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗿𝗲𝗺𝗼𝘃𝗲, not what to add.
_____________
𝗦𝘁𝗲𝗽 9 – 𝗟𝗟𝗠 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁
• Measures accuracy, cost, and consistency.
• Prevents silent failures and bad outputs.
𝗘.𝗴: Helicone, PromptLayer, TruLens.

𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:
Accuracy metrics lie.
Cost and latency usually reveal problems first.

Most people surprisingly still don't know these skills even today.

✅ Repost for others so they can learn these AI skills.

I have a strong interest in the intersection of technology and business, with a specific focus on leveraging innovative technologies to enhance organizational performance and solve real-world problems for people.

Let's connect and collaborate on inspiring ventures that merge the realms of technology, business, and innovation🤝#Tech #AI #Business #Entrepreneurship #ProductManagement #Innovation

AI Agents | GenAI Insights | Agentic AI Strategist | Mentor | 10x Your Career with AI Tools | Simplifying AI | Future of Work | Helping You Land Jobs

Akinwumi Fakokunde

Most people overlook these basics of AI Agents.

I've explained it in a very simple way below.

1. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁
An AI system that observes its environment, information, makes decisions, and takes actions to achieve a goal.

2. 𝗟𝗟𝗠𝘀 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀)
The language models which are the agent’s brain. It understands language, thinks through problems, and generates responses.

3. 𝗟𝗥𝗠𝘀 (𝗟𝗮𝗿𝗴𝗲 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀)
Models designed for deeper, step-by-step reasoning and complex decision-making.

4. 𝗧𝗼𝗼𝗹𝘀
Internal or external APIs an agent uses to perform actions like searching, calling tools, or updating systems.

5. 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁
The space where an agent operates, including data, tools, users, and external systems.

6. 𝗦𝘁𝗮𝘁𝗲
The agent’s current situation, what it’s doing right now and what information it has.

7. 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻
How an agent reads, interprets, and understands inputs from its environment.

8. 𝗔𝗰𝘁𝗶𝗼𝗻
Any step the agent takes such as replying to a user, calling a tool, or completing a task.

9. 𝗠𝗲𝗺𝗼𝗿𝘆
Storage used to retain context, past interactions, and useful information over time.

10. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲
Extra information the agent can refer to, like documents, FAQs, or company data to generate accurate outputs.

11. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴
The agent decides what steps to take and in what order to finish a task.

12. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻
Managing how different components or agents work together from input to output.

13. 𝗧𝗵𝗶𝗻𝗸 → 𝗔𝗰𝘁 → 𝗖𝗵𝗲𝗰𝗸 (𝗥𝗲𝗔𝗰𝘁)
The agent thinks, takes an action, checks the result, and then decides the next step.

14. 𝗖𝗼𝗧 (𝗖𝗵𝗮𝗶𝗻 𝗼𝗳 𝗧𝗵𝗼𝘂𝗴𝗵𝘁)
Breaking down complex problems into logical, intermediate steps.

15. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
The overall design that defines how an agent’s components connect and communicate including brain, memory, tools, and logic.

16. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
Measuring how well an agent performs, makes decisions, and achieves its objectives.

17. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺
Multiple agents working together in a shared environment, each handling part of the task.

18. 𝗦𝘄𝗮𝗿𝗺
Agents coordinate on their own and solve problems together without strict control.

19. 𝗛𝗮𝗻𝗱𝗼𝗳𝗳𝘀
Transferring tasks or responsibilities from one agent to another when needed.

20. 𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝗯𝗮𝘁𝗲
Structured discussions between agents to compare ideas and improve outcomes.

This is useful for anyone who wants to understand the very fundamentals of AI and AI agents.

✅ Repost for others who can benefit from this.

Most people building modern AI systems miss these steps.

I've explained each step in a simple way below.

1. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆
How multiple AI agents work together as a system.

Step-by-step:
• 𝗨𝘀𝗲𝗿 𝗥𝗲𝗾𝘂𝗲𝘀𝘁: A user asks a question or gives a task.
• 𝗣𝗿𝗶𝗺𝗮𝗿𝘆 𝗔𝗴𝗲𝗻𝘁: One main agent receives the request and decides what needs to be done.
• 𝗧𝗮𝘀𝗸 𝗕𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻: The task is split into smaller subtasks.
• 𝗔𝘂𝘅𝗶𝗹𝗶𝗮𝗿𝘆 𝗔𝗴𝗲𝗻𝘁𝘀: Other agents handle search, data fetching, analysis, or reasoning.
• 𝗔𝗴𝗲𝗻𝘁-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁 (𝗔2𝗔): Agents communicate securely and share context.
• 𝗥𝗲𝘀𝘂𝗹𝘁 𝗠𝗲𝗿𝗴𝗶𝗻𝗴: All agent outputs are combined into one final answer.

Without this layer, AI systems behave like isolated tools, not intelligent teams.
_______________________
2. 𝗦𝗟𝗠 𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗠𝗶𝗰𝗿𝗼 𝗔𝗴𝗲𝗻𝘁𝘀
Small, specialized AI workers instead of one giant brain.

Step-by-step:
• 𝗟𝗮𝗿𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 (𝗟𝗟𝗠): Acts as high-level intelligence.
• 𝗧𝗮𝘀𝗸 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿: Decides which micro-agent should do which task.
• 𝗠𝗶𝗰𝗿𝗼 𝗔𝗴𝗲𝗻𝘁𝘀 (𝗦𝗟𝗠𝘀): Small, custom-trained models focused on one job.
• 𝗧𝗼𝗼𝗹 𝗨𝘀𝗮𝗴𝗲: Each micro-agent uses its own tools (APIs, databases, automations).
• 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁: Tasks run safely and efficiently.
• 𝗥𝗲𝘀𝘂𝗹𝘁 𝗥𝗲𝘁𝘂𝗿𝗻: Outputs go back to the orchestrator.

This makes AI systems 𝗳𝗮𝘀𝘁𝗲𝗿, 𝗰𝗵𝗲𝗮𝗽𝗲𝗿, 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲.
_______________________
3. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
Giving AI the 𝘳𝘪𝘨𝘩𝘵 information at the 𝘳𝘪𝘨𝘩𝘵 time.

Step-by-step:
• 𝗜𝗻𝗽𝘂𝘁 𝗥𝗲𝗾𝘂𝗲𝘀𝘁: User sends a query.
• 𝗦𝗺𝗮𝗿𝘁 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗙𝗲𝘁𝗰𝗵𝗶𝗻𝗴: Only relevant memory and data are retrieved.
• 𝗠𝗲𝗺𝗼𝗿𝘆 𝗟𝗮𝘆𝗲𝗿: Short-term and long-term memory are accessed.
• 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗟𝗮𝘆𝗲𝗿: Stores historical and factual information.
• 𝗧𝗼𝗼𝗹 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸𝘀: Ensures tools behave correctly.
• 𝗘𝘃𝗮𝗹𝘀: Outputs are evaluated for accuracy, fairness, and bias.

Good context engineering prevents hallucinations and wrong answers.
_______________________
4. 𝗙𝗶𝗻𝗮𝗹 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻
Delivering the response to the user.

Step-by-step:
• 𝗖𝗼𝗺𝗯𝗶𝗻𝗲𝗱 𝗖𝗼𝗻𝘁𝗲𝘅𝘁: All relevant data is passed to the model.
• 𝗟𝗟𝗠 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴: The model reasons only over provided context.
• 𝗙𝗶𝗻𝗮𝗹 𝗢𝘂𝘁𝗽𝘂𝘁: A clear, grounded, and safe response is generated.

Why Modern AI Architecture Matters?
<> Models alone don’t build great AI systems.
<> Architecture decides 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝘁𝗿𝘂𝘀𝘁.
<> The best AI products are built with 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁.

✅ Repost for others so they can understand the modern AI architecture.

4 ways how AI systems communicate and coordinate.

I've explained each one in detail below.

1. 𝗠𝗖𝗣 (𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹)
• User submits a request: “Summarize today’s Slack messages.”
• MCP Client receives input: Interface between user and AI system.
• Request forwarded to MCP Host: Host manages execution and control.
• Model selection: MCP Host decides which LLM handles request.
• Tool permission check: MCP Host defines allowed tools (Slack, DBs).
• LLM processes intent: Understands task and decides if tool needed.
• MCP Server executes tool calls: Communicates with Slack, Qdrant, Brave.
• Tool data returned: Real-world data flows back to model.
• Final response generated: Tool → LLM → MCP Client → User.

𝗠𝗖𝗣 = 𝗦𝗮𝗳𝗲, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝘄𝗮𝘆 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀 𝘁𝗼 𝘂𝘀𝗲 𝘁𝗼𝗼𝗹𝘀
_____________

2. 𝗔2𝗔 (𝗔𝗴𝗲𝗻𝘁-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹)
• User gives a complex task: “Research competitors and summarize insights.”
• Primary agent receives task: Acts as coordinator.
• Agent discovery begins: System identifies agents with relevant skills.
• A2A protocol establishes communication: Defines how agents exchange messages.
• Task delegation happens: Research, analysis, summary agents.
• Agents work independently: Each may use different models or tools.
• Progress shared between agents: Partial results stream back.
• Results aggregated: Primary agent combines outputs.
• Final response delivered.

𝗔2𝗔 = 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗻𝗴 𝗹𝗶𝗸𝗲 𝘁𝗲𝗮𝗺𝗺𝗮𝘁𝗲𝘀
____________

3. 𝗔𝗖𝗣 (𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹)
• User sends request via ACP Client: Entry point to agent system.
• ACP Client routes request to Agent 1: First responding agent.
• Agent exposes profile: Capabilities, tools, limits.
• ACP defines message structure: Standard metadata and formats.
• Agent discovery occurs: Other compatible agents identified.
• Second agent joins: May use different tools or frameworks.
• Protocol ensures understanding: All agents speak same “language”.
• Structured communication happens: No custom formats or assumptions.
• Response flows back: Clean and predictable output.

𝗔𝗖𝗣 = 𝗣𝗿𝗼𝗳𝗶𝗹𝗲 + 𝗵𝗮𝗻𝗱𝘀𝗵𝗮𝗸𝗲 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗳𝗼𝗿 𝗮𝗴𝗲𝗻𝘁𝘀
____________

4. 𝗔𝗡𝗣 (𝗔𝗴𝗲𝗻𝘁 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹)
• User submits a real-world request: “Plan a complete trip.”
• Coordinator agent activated: Understands overall goal.
• Relevant agents identified: Travel, hotel, weather agents.
• ANP manages interactions: Routing requests and responses.
• Agents share partial results: Availability, prices, forecasts.
• Dependencies resolved: One agent’s output feeds another.
• Network stays synchronized: Information remains consistent.
• Final solution assembled: End-to-end plan created.
• User receives outcome.

𝗔𝗡𝗣 = 𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘁𝗲𝗮𝗺𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀

✅ Repost for other people in your network so they can understand this.

Prompt vs Context vs RAG.

I've explained it in a simple way below.

1. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
Prompt Engineering is about 𝗰𝗹𝗲𝗮𝗿 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀, not magic words.

• 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗳𝗶𝗻𝗮𝗹 𝗴𝗼𝗮𝗹: What exactly do you want the AI to produce?
• 𝗔𝘀𝘀𝗶𝗴𝗻 𝗮 𝗰𝗹𝗲𝗮𝗿 𝗿𝗼𝗹𝗲: Example: “Act as a legal expert”.
• 𝗚𝗶𝘃𝗲 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Show the AI what a good answer looks like
• 𝗨𝘀𝗲 𝗳𝗲𝘄-𝘀𝗵𝗼𝘁 𝘀𝗮𝗺𝗽𝗹𝗲𝘀: Provide multiple examples so the pattern is clear
• 𝗦𝗲𝘁 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀: Limit length, tone, or structure
• 𝗛𝗮𝗻𝗱𝗹𝗲 𝗲𝗱𝗴𝗲 𝗰𝗮𝘀𝗲𝘀: Tell the AI what to do if data is missing or unclear
• 𝗗𝗲𝗳𝗶𝗻𝗲 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗳𝗼𝗿𝗺𝗮𝘁: JSON, table, bullets, etc.
• 𝗔𝗽𝗽𝗹𝘆 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀: Explicitly state what the AI should NOT do
• 𝗦𝘁𝗮𝘁𝗲 𝗮𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻𝘀: Clarify what the AI can assume
• 𝗧𝗲𝘀𝘁 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗽𝗿𝗼𝗺𝗽𝘁𝘀: Try variations to get the best output
_____________

2. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
Context Engineering is about 𝗳𝗲𝗲𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗯𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱 𝗱𝗮𝘁𝗮, not everything.

• 𝗗𝗲𝗳𝗶𝗻𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗻𝗲𝗲𝗱𝘀: Decide what information is required to answer correctly
• 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗱𝗮𝘁𝗮 𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Docs, notes, chat history, APIs, user data
• 𝗦𝗲𝗹𝗲𝗰𝘁 𝘂𝘀𝗲𝗳𝘂𝗹 𝘀𝗶𝗴𝗻𝗮𝗹𝘀: Keep only what helps the task
• 𝗥𝗲𝗳𝗶𝗻𝗲 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲: Remove irrelevant information
• 𝗞𝗲𝗲𝗽 𝗸𝗲𝘆 𝗽𝗼𝗶𝗻𝘁𝘀: Focus on essential facts
• 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗲 𝗽𝗮𝘆𝗹𝗼𝗮𝗱: Structure context clearly
• 𝗦𝗵𝗿𝗶𝗻𝗸 𝗰𝗼𝗻𝘁𝗲𝘅𝘁: Compress data to fit model limits
• 𝗦𝘆𝘀𝘁𝗲𝗺-𝗹𝗲𝘃𝗲𝗹 𝗰𝗼𝗻𝘁𝗲𝘅𝘁: Global rules like tone, behavior, style
• 𝗧𝗮𝘀𝗸-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗰𝗼𝗻𝘁𝗲𝘅𝘁: Information needed only for this task
• 𝗧𝗿𝗮𝗰𝗸 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲: Maintain memory across conversations
_____________

3. 𝗥𝗔𝗚
RAG is used to bring 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 into AI responses.

• 𝗟𝗼𝗮𝗱 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝘀: PDFs, websites, internal docs, databases
• 𝗦𝗽𝗹𝗶𝘁 𝗶𝗻𝘁𝗼 𝗰𝗵𝘂𝗻𝗸𝘀: Break large text into small pieces
• 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀: Convert chunks into vectors (meaning-based numbers)
• 𝗦𝗮𝘃𝗲 𝘃𝗲𝗰𝘁𝗼𝗿𝘀: Store them in a vector database
• 𝗕𝘂𝗶𝗹𝗱 𝘀𝗲𝗮𝗿𝗰𝗵 𝗶𝗻𝗱𝗲𝘅𝗲𝘀: Make data searchable by meaning
• 𝗥𝗲𝗰𝗲𝗶𝘃𝗲 𝘂𝘀𝗲𝗿 𝗾𝘂𝗲𝗿𝘆: User asks a question
• 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗲 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗽𝗮𝘀𝘀𝗮𝗴𝗲𝘀: Find the closest matching chunks
• 𝗔𝗽𝗽𝗹𝘆 𝗺𝗲𝘁𝗮𝗱𝗮𝘁𝗮 𝗳𝗶𝗹𝘁𝗲𝗿𝘀: Narrow results to relevant data
• 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗮𝗻𝘀𝘄𝗲𝗿: LLM uses retrieved context to respond
• 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: Measure relevance and usefulness

𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀?
<> 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 controls how you ask
<> 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 controls what the AI knows
<> 𝗥𝗔𝗚 controls where the knowledge comes from

✅ Repost for others so they can understand these key differences.

Raymund ITIL®

Which is more crucial today: AI or ML Engineer?

I've explained it in a simple way below.

𝗔𝗜 𝗘𝗡𝗚𝗜𝗡𝗘𝗘𝗥
• Builds complete AI-powered products end to end
• Brings AI into real-world apps and business workflows
• Works on inference, APIs, agents, and orchestration
• Uses pre-trained models and adapts them for production
• Focuses on latency, scalability, and user experience
• Connects models with databases, tools, and external APIs
• Thinks more like a product + systems engineer
• Ships AI features that users actually interact with
• Often works closely with frontend, backend, and product teams

𝗠𝗟 𝗘𝗡𝗚𝗜𝗡𝗘𝗘𝗥
• Focuses on training, fine-tuning, and improving models
• Designs custom model architectures and ML pipelines
• Handles data cleaning, preprocessing, and feature engineering
• Tunes hyperparameters and runs structured experiments
• Tracks model performance, metrics, and accuracy
• Works deeply with datasets, labels, and evaluation methods
• Thinks more like a research + optimization engineer
• Improves how smart and accurate the model itself is
• Works closely with large datasets and statistical methods

What actually changed?

𝗧𝗵𝗲 𝗚𝗲𝗻𝗔𝗜 𝗹𝗮𝘆𝗲𝗿
• LLM & generative AI systems
• Prompt and context engineering
• RAG pipelines (retrieval + generation)
• Fine-tuning foundation models for domain use
• Tool calling and function execution
• Multi-step LLM workflows and agents
• AI observability (hallucinations, drift, latency, cost)
• Human-in-the-loop systems and feedback cycles
This layer didn’t replace ML.
It 𝘀𝗵𝗶𝗳𝘁𝗲𝗱 𝘄𝗵𝗲𝗿𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗰𝗿𝗲𝗮𝘁𝗲𝗱.

𝗪𝗵𝗶𝗰𝗵 𝗿𝗼𝗹𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗳𝗶𝘁𝘀 𝘆𝗼𝘂?
• If you like 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀, 𝗮𝗻𝗱 𝘂𝘀𝗲𝗿𝘀 → AI Engineer
• If you like 𝗱𝗮𝘁𝗮, 𝗺𝗮𝘁𝗵, 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 → ML Engineer
• If you enjoy 𝘀𝗵𝗶𝗽𝗽𝗶𝗻𝗴 𝗳𝗮𝘀𝘁 𝗮𝗻𝗱 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 → AI Engineer
• If you enjoy 𝗱𝗲𝗲𝗽 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗺𝗼𝗱𝗲𝗹 𝗿𝗶𝗴𝗼𝗿 → ML Engineer

Today, most companies aren’t blocked by model quality.
They’re blocked by:
• Integration into real workflows
• Reliability in production
• Cost and latency at scale
• Usable, trustworthy user experiences
That’s why many ML roles are evolving toward AI Engineering.

✅ Repost for others in your network so they can understand this.

Devisha Tiwari

Most people even today don't know this about MCP.

I've explained it in simple way below.

AI systems fail because control logic lives inside prompts. 
MCP moves that control outside the model, where it belongs.

1. 𝗗𝗶𝗿𝗲𝗰𝘁 𝗔𝗣𝗜 𝗪𝗿𝗮𝗽𝗽𝗲𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻
This is the simplest MCP setup.

Step-by-step:
1. 𝗔𝗴𝗲𝗻𝘁 𝗿𝗲𝗰𝗲𝗶𝘃𝗲𝘀 𝗮 𝘁𝗮𝘀𝗸
Example: “Get user payment history.”
2. 𝗔𝗴𝗲𝗻𝘁 𝘂𝘀𝗲𝘀 𝗠𝗖𝗣
MCP acts as a clean interface.
3. 𝗠𝗖𝗣 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗺𝗮𝗽𝘀 𝘁𝗼 𝗮𝗻 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗔𝗣𝗜
• Server 1 → API 1
• Server 2 → API 2
4. 𝗔𝗣𝗜 𝗿𝗲𝘁𝘂𝗿𝗻𝘀 𝗱𝗮𝘁𝗮
No extra logic or transformation.
5. 𝗔𝗴𝗲𝗻𝘁 𝘂𝘀𝗲𝘀 𝘁𝗵𝗲 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲
And gives the final answer.

Why use this?
• Fast to build
• Minimal complexity
• Best for 𝘀𝘁𝗮𝗯𝗹𝗲, 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗔𝗣𝗜𝘀

Think of it as: 𝗔𝗜 → 𝗔𝗣𝗜 → 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲
________________
2. 𝗖𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗲 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻
Instead of calling 𝗼𝗻𝗲 𝗔𝗣𝗜, 
MCP combines 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗔𝗣𝗜𝘀 𝗶𝗻𝘁𝗼 𝗼𝗻𝗲 𝘀𝗺𝗮𝗿𝘁 𝘀𝗲𝗿𝘃𝗶𝗰𝗲.

Step-by-step:
1. 𝗔𝗴𝗲𝗻𝘁 𝗴𝗲𝘁𝘀 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗿𝗲𝗾𝘂𝗲𝘀𝘁
Example: “Create a user, assign a plan, and send a welcome email.”
2. 𝗔𝗴𝗲𝗻𝘁 𝗰𝗮𝗹𝗹𝘀 𝗼𝗻𝗲 𝗠𝗖𝗣 𝘁𝗼𝗼𝗹
Just one high-level command.
3. 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲𝘀 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗔𝗣𝗜𝘀
• API 1 → Create user
• API 2 → Assign plan
• API 3 → Send email
4. 𝗠𝗖𝗣 𝗰𝗼𝗺𝗯𝗶𝗻𝗲𝘀 𝗿𝗲𝘀𝘂𝗹𝘁𝘀
Handles order, dependencies, and errors.
5. 𝗦𝗶𝗻𝗴𝗹𝗲 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝘀𝗲𝗻𝘁 𝗯𝗮𝗰𝗸 𝘁𝗼 𝗮𝗴𝗲𝗻𝘁

Why use this?
• Cleaner agent logic
• Handles 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀
• Reusable business logic

Think of it as: 𝗔𝗜 → 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 → 𝗠𝗮𝗻𝘆 𝗔𝗣𝗜𝘀
________________
3. 𝗠𝗖𝗣-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁 𝗣𝗮𝘁𝘁𝗲𝗿𝗻
MCP doesn’t just call APIs, BUT
it can call 𝗼𝘁𝗵𝗲𝗿 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀.

Step-by-step:
1. 𝗠𝗮𝗶𝗻 𝗔𝗴𝗲𝗻𝘁 𝗿𝗲𝗰𝗲𝗶𝘃𝗲𝘀 𝗮 𝘁𝗮𝘀𝗸
Example: “Analyze a legal document.”
2. 𝗔𝗴𝗲𝗻𝘁 𝘂𝘀𝗲𝘀 𝗠𝗖𝗣
MCP decides this needs expert reasoning.
3. 𝗠𝗖𝗣 𝗰𝗮𝗹𝗹𝘀 𝗮 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁 𝗔𝗴𝗲𝗻𝘁
• Legal agent
• Finance agent
• Research agent
4. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁 𝗔𝗴𝗲𝗻𝘁 𝘁𝗵𝗶𝗻𝗸𝘀 𝗱𝗲𝗲𝗽𝗹𝘆
Uses its own tools or APIs if needed.
5. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁 𝗿𝗲𝘁𝘂𝗿𝗻𝘀 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀
Not raw data, actual reasoning.
6. 𝗠𝗮𝗶𝗻 𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗺𝗽𝗼𝘀𝗲𝘀 𝗳𝗶𝗻𝗮𝗹 𝗮𝗻𝘀𝘄𝗲𝗿

Why use this?
• Modular AI systems
• Better reasoning quality
• Clear separation of responsibilities

Think of it as: 𝗔𝗜 → 𝗠𝗖𝗣 → 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁 𝗔𝗜 → 𝗠𝗖𝗣 → 𝗔𝗜

✅ 𝗜𝗻 𝘀𝗵𝗼𝗿𝘁
• 𝗗𝗶𝗿𝗲𝗰𝘁 𝗔𝗣𝗜 𝗪𝗿𝗮𝗽𝗽𝗲𝗿 → Simple, fast, direct
• 𝗖𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗲 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 → One call, many APIs
• 𝗠𝗖𝗣-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁 → Delegate thinking to experts

People often don't know this but you should know:
• AI decides what to do.
• MCP decides where it goes.
• APIs or agents decide how it’s done.

✅ Repost for others in your network so they can understand MCP better.

Most AI projects fail after deployment. 

I’ve explained the core problems step by step.

𝗦𝘁𝗲𝗽 1 – 𝗗𝗮𝘁𝗮
• Collects raw information from multiple sources.
• Forms the foundation of every AI system.
𝗘.𝗴: APIs, logs, databases, user inputs.
𝗡𝗼𝘁𝗲:
AI doesn’t fail because of models.
It fails because 𝗱𝗮𝘁𝗮 𝗶𝘀 𝗶𝗻𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲, 𝗻𝗼𝗶𝘀𝘆, or biased.

𝗦𝘁𝗲𝗽 2 – 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀
• Defines how learning happens.
• Converts data into patterns and signals.
𝗘.𝗴: TensorFlow, LightGBM, XGBoost, Scikit-learn.
𝗡𝗼𝘁𝗲:
Algorithms don’t create intelligence.
They 𝗼𝗻𝗹𝘆 𝗲𝘅𝗽𝗼𝘀𝗲 what data already contains.

𝗦𝘁𝗲𝗽 3 – 𝗖𝗼𝗺𝗽𝘂𝘁𝗲
• Provides hardware and cloud infrastructure.
• Powers training and inference at scale.
𝗘.𝗴: GPUs, TPUs, AWS EC2, Azure ML Compute.
𝗡𝗼𝘁𝗲:
Most bottlenecks aren’t model-related.
They come from 𝗽𝗼𝗼𝗿 𝗰𝗼𝗺𝗽𝘂𝘁𝗲 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴.

𝗦𝘁𝗲𝗽 4 – 𝗠𝗼𝗱𝗲𝗹𝘀
• Learn patterns from data using algorithms.
• Produce probabilistic outputs, not truths.
𝗘.𝗴: GPT, BERT, LLaMA, PyTorch models.
𝗡𝗼𝘁𝗲:
Models don’t “understand.”
They 𝗽𝗿𝗲𝗱𝗶𝗰𝘁 𝗻𝗲𝘅𝘁 𝗯𝗲𝘀𝘁 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀.

𝗦𝘁𝗲𝗽 5 – 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲
• Uses trained models to generate outputs.
• Drives predictions and decisions.
𝗘.𝗴: OpenAI API, TensorRT, ONNX Runtime.
𝗡𝗼𝘁𝗲:
Training is expensive.
𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 is where real costs accumulate.

𝗦𝘁𝗲𝗽 6 – 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁
• Pushes models into production systems.
• Handles scaling, versioning, and updates.
𝗘.𝗴: Docker, Kubernetes, FastAPI.
𝗡𝗼𝘁𝗲:
A model isn’t real until deployed.
Most “AI projects” die before this step.

𝗦𝘁𝗲𝗽 7 – 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆
• Tracks accuracy, drift, latency, and errors.
• Ensures system reliability over time.
𝗘.𝗴: MLflow, Weights & Biases, Evidently.
𝗡𝗼𝘁𝗲:
Models don’t fail loudly.
They fail 𝗴𝗿𝗮𝗱𝘂𝗮𝗹𝗹𝘆 and silently.

𝗦𝘁𝗲𝗽 8 – 𝗠𝗲𝗺𝗼𝗿𝘆
• Stores context, embeddings, and history.
• Enables personalization and continuity.
𝗘.𝗴: Redis, Pinecone, Weaviate.
𝗡𝗼𝘁𝗲:
Stateless AI feels dumb.
Memory is what makes systems feel 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁.

𝗦𝘁𝗲𝗽 9 – 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻
• Coordinates workflows, tools, and agents.
• Connects AI with external systems.
𝗘.𝗴: LangChain, Airflow, Zapier, Make.
𝗡𝗼𝘁𝗲:
AI systems are not single models.
They’re 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀.

𝗦𝘁𝗲𝗽 10 – 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲
• Controls access, safety, and compliance.
• Prevents misuse and data leaks.
𝗘.𝗴: IAM, Guardrails, Content Filters.
𝗡𝗼𝘁𝗲:
The smartest AI is useless,
if it’s 𝗻𝗼𝘁 𝘁𝗿𝘂𝘀𝘁𝗲𝗱.

Most people surprisingly still don't apply these steps while designing for real-world AI systems.

✅ Repost for others so they apply these concepts and learnings.

Kapil M.

7 database types used in modern AI systems.

I’ve explained each one in simple steps.

1. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
• 𝗠𝗮𝗶𝗻 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Store embeddings so AI can search by meaning.
• 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Text/images → vectors → nearest match retrieved.
• 𝗨𝘀𝗲𝗱 𝗳𝗼𝗿: RAG, semantic search, agent memory.
• 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Fast, relevant context retrieval.
𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Pinecone, Milvus, Weaviate, Qdrant
____________
2. 𝗚𝗿𝗮𝗽𝗵 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
• 𝗠𝗮𝗶𝗻 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Store entities and their relationships for reasoning.
• 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Data stored as nodes (things) and edges (connections).
• 𝗨𝘀𝗲𝗱 𝗳𝗼𝗿: Knowledge graphs, logic-heavy copilots, agents.
• 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Understanding complex relationships.
𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Neo4j, TigerGraph, ArangoDB, Dgraph
____________
3. 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
• 𝗠𝗮𝗶𝗻 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Store text, images, audio together.
• 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Multiple data types stored in one index.
• 𝗨𝘀𝗲𝗱 𝗳𝗼𝗿: Multimodal copilots, vision/audio RAG.
• 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: AI that sees, hears, and reads.
𝗘𝘅𝗮𝗺𝗽𝗹𝗲: DeepLake, Milvus Multimodal, LanceDB
____________
4. 𝗔𝗴𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
• 𝗠𝗮𝗶𝗻 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Give AI agents short- and long-term memory.
• 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Stores past conversations and decisions.
• 𝗨𝘀𝗲𝗱 𝗳𝗼𝗿: Agentic systems, personal AI.
• 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Autonomous, long-running agents.
𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Mem0, LangGraph Memory, Zep, Letta
____________
5. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
• 𝗠𝗮𝗶𝗻 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Store structured facts and rules.
• 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Uses schemas and semantic relations.
• 𝗨𝘀𝗲𝗱 𝗳𝗼𝗿: Enterprise assistants, compliance.
• 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Fact-correct AI systems.
𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Stardog, GraphDB, Neptune, OrientDB
____________
6. 𝗞𝗲𝘆-𝗩𝗮𝗹𝘂𝗲 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
• 𝗠𝗮𝗶𝗻 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Ultra-fast storage for small data.
• 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Simple key → value lookup.
• 𝗨𝘀𝗲𝗱 𝗳𝗼𝗿: Caching, session state, agent memory.
• 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Speed-critical operations.
𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Redis, Aerospike, DynamoDB
____________
7. 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
• 𝗠𝗮𝗶𝗻 𝗣𝘂𝗿𝗽𝗼𝘀𝗲: Process data in real time.
• 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: Handles continuous event streams.
• 𝗨𝘀𝗲𝗱 𝗳𝗼𝗿: Agent telemetry, event-driven AI.
• 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿: Live, real-time systems.
𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Kafka, Amazon Kinesis, Redpanda

✅ 𝗜𝗻 𝘀𝗵𝗼𝗿𝘁
• 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗕 → Find relevant info
• 𝗚𝗿𝗮𝗽𝗵 𝗗𝗕 → Understand relationships
• 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗗𝗕 → Handle text + image + audio
• 𝗔𝗴𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆 𝗗𝗕 → Remember experiences/past actions
• 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗗𝗕 → Store facts & rules
• 𝗞𝗲𝘆-𝗩𝗮𝗹𝘂𝗲 𝗗𝗕 → Ultra-fast access
• 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗗𝗕 → React for real time events

Every type of database matters because AI needs memory, reasoning, speed, structure, and real-time awareness.

✅ Repost for others who can benefit from this.

Most people miss these principles while building AI agents.

I’ve explained everything that you should keep in mind.

1. Never run an agent without clear context.
2. Define who the agent is and what it is responsible for.
3. Always log inputs, actions, and outputs.
4. If it’s not logged, it didn’t happen.
5. Add guardrails before giving autonomy.
6. Limit what tools an agent can access.
7. Plan actions before executing them.
8. Separate reasoning from execution.
9. Make agents think before they act.
10. Measure outcomes, not just responses.
11. Optimize for results, not good-looking text.
12. Continuously evaluate agent behavior.
13. Feedback loops improve reliability.
14. Speed strongly affects user trust.
15. Reduce unnecessary model and tool calls.
16. Remember: agents are built to act, not chat.
17. Test agents in real-world scenarios, not demos.
18. Autonomy without control leads to failure.
19. Always verify tool outputs.
20. Call tools only when truly required.
21. Track cost, latency, and performance.
22. Hardcoded logic breaks over time. Keep system evolving.
23. Design workflows before choosing models.
24. One agent should handle one clear job.
25. Start with a single agent, then scale.
26. Clearly define agent roles and boundaries.
27. Assign tasks instead of repeating work.
28. Share context so agents can collaborate.
29. Use memory instead of repeating prompts.
30. Review every action to enable learning.
31. Assume failures will happen.
32. Build retries, fallbacks, and recovery paths.
33. Use standards to enable cooperation.
34. Observe behavior before optimizing.
35. Involve humans only when judgment is required.
36. Avoid hardcoding intelligence.
37. Design systems that can evolve.
38. Prefer event-driven systems over polling.
39. Make agents explain their decisions.
40. Transparency builds trust.
41. Use state to support long-running tasks.
42. Stateless agents forget too easily.
43. Optimize for scalability from day one.
44. Balance intelligence with cost and speed.
45. Treat agents as software systems, not prompts.
46. Good workflows beat powerful models.
47. Simple systems scale better than complex ones.
48. Real reliability comes from engineering discipline.
49. Test, break, fix, and repeat.
50. Build agents that survive real-world usage.

This is a solid list for anyone or any company planning to build/use AI agents and systems.

✅ Repost for people in your network who're building or learning AI agents.

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