I help founders and leaders build AI products that earn trust, scale responsibly, and keep humans in the loop.

AI, which I call Synthetic Agency (SA), is a great technology in the right context, is a harmful technology in the wrong context, and is a low ROI feature in most contexts.

I've built synthetic agency in products from the ground up, launched ventures in the field, performed academic research and contributed to think tanks, wrote The Ethics of AI, and advised 100+ leaders over the past 12 years.
I’ve learned that, while most people attribute failure modes to technicalities or talent, the true failure is philosophical.

The products that succeeded long term had teams who did not skip the hard questions. They built synthetic agency when they had to, not because they just could. They solved real problems by listening to their customers without chasing trends.

If you're working on AI, with AI or are figuring out how it impacts your work, and want to:
• Build something people actually want to use 
• Think through the ethical stuff without getting paralyzed
• Understand how your brand discovery and promotion should change with AI engines in town

I might be able to help.

I also write for 15,000 people on LinkedIn and for 3,000 on my newsletter about honest tips to make sense of this technological shift.

I'm also launching a monthly private newsletter. Invite-only. For Founders, CMOs, and Heads of Growth who need to control their brand voice in AI conversations (before it becomes too late or too expensive). DM me if you want to get an invite.

🎤 Open to speaking, podcasts, and collaborations—DM me or visit albertochierici.com

Alberto Chierici

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

Rahul Agarwal

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.

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.

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.

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.

taplio

The Battle

Alberto Chierici

Rahul Agarwal

Followers

Posts

Engagements

Likes

Performance ScoreRahul Agarwal's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Alberto Chierici

Rahul Agarwal

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Rahul Agarwal's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreRahul Agarwal's LinkedIn Performance