CS Manager-Agentic AI Architect@IBM| #35 Favikon Top Creator🔥| 240K+ FB |Thought Leader| Keynote Speaker| Board Member| Podcast Host| Publisher| IBM ATE Top 0.05%| IBM Tech Top 1%| Multiple Patents| Multiple OTA Awards

A recognized thought leader in Generative AI and Agentic AI, Jothi Moorthy is an IBM Senior Customer Success Manager and AI Architect. She helps organizations deploy generative and agentic AI solutions, including building AI-powered coding assistants to accelerate developer productivity, using generative AI to access and summarize insights from legacy data sources, and connecting legacy enterprise systems with modern generative and agentic AI ecosystems. She also designs multi-agent workflows that automate manual, time-consuming processes, freeing employees to focus on higher-value tasks and significantly improving enterprise productivity.

She is part of the elite top 0.05% in IBM’s Advanced Technical Eminence program and among the top 1% of IBM technologists recognized at IBM Tech 2024. She holds multiple patents and has been awarded multiple Outstanding Technical Achievement Awards (OTAs) for her innovative solutions.

Ranked #35 on Favikon’s Top 200 Creators and in the Top 5% of LinkedIn voices, Jothi has built a strong global following, including 240K+ Facebook followers. She is a sought-after keynote speaker and serves as a Board Member for the Global Skill Development Council (GSDC), shaping conversations at the intersection of AI, leadership, and enterprise transformation.

She is the creator of Agentic AI News & Use Cases, one of the fastest-growing newsletters on Generative AI and Agentic AI with 3,000+ subscribers, delivering weekly news and in-depth analysis.

Her Generative AI Revolution Podcast brings together industry experts to highlight cutting-edge implementations and future possibilities. It is available on Spotify, YouTube, and Apple Podcasts.

As Publisher and Editor-in-Chief of Women in Tech Chronicles Magazine (non commercial), she amplifies the voices of women leaders in technology, celebrating authentic journeys and inspiring the next generation.

Views expressed are my own & don't represent my employer.

𝐌𝐨𝐬𝐭 𝐭𝐞𝐚𝐦𝐬 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐚𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈…
Very few know how to make it production ready.

This blueprint shows the exact architecture that enterprises use to deploy scalable, safe, and reliable agentic systems:

𝟏. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫
Users interact with the app, which routes requests through an enterprise API gateway, enabling secure access to the agent ecosystem.

𝟐. 𝐀𝐠𝐞𝐧𝐭 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐑𝐮𝐧𝐭𝐢𝐦𝐞
* Orchestrator agent handles planning
* Planner agent breaks tasks down
* Action agent executes tool calls
* Third party agents extend functionality
* All connected through event driven autoscaling and a message broker.

𝟑. 𝐓𝐨𝐨𝐥 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐚𝐧𝐝 𝐋𝐨𝐨𝐤𝐮𝐩
The system automatically discovers tools from registries and resolves the best one for each step in the task.

𝟒. 𝐓𝐨𝐨𝐥 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧
Agents issue tool calls through the MCP gateway, where guardrails ensure safety before invoking MCP compliant systems such as Google Drive, PostgreSQL, Docker, Kubernetes, Snowflake, or Git.

𝟓. 𝐌𝐞𝐦𝐨𝐫𝐲 𝐋𝐚𝐲𝐞𝐫
A dedicated memory module stores long term memory, short term memory, and knowledge graphs.
The system retrieves past context to improve reasoning and updates memory after each task.

𝟔. 𝐀𝐈 𝐆𝐚𝐭𝐞𝐰𝐚𝐲
Provides access to LLMs, finetuned models, retrieval modules, and safety guardrails so every agent action is controlled and fully observable.

𝟕. 𝐒𝐮𝐩𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦𝐬
DataOps, LLMOps, telemetry, agent benchmarking, human in the loop workflows, and strong security ensure the system remains trustworthy and maintainable.

This is what it takes to build real production grade agentic AI.
Not just an agent that answers a prompt, but an entire ecosystem that coordinates tools, memory, safety, and orchestration.

𝐖𝐡𝐢𝐜𝐡 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐭𝐡𝐢𝐬 𝐛𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐝𝐨 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐞𝐱𝐩𝐥𝐨𝐫𝐞 𝐝𝐞𝐞𝐩𝐞𝐫?

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#AgenticAI #AIAgents #AIArchitecture #LLMOps #AIEngineering

Jothi Moorthy

𝐖𝐡𝐲 𝐀𝐈 𝐆𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬 𝐀𝐫𝐞 𝐍𝐨𝐧-𝐍𝐞𝐠𝐨𝐭𝐢𝐚𝐛𝐥𝐞 𝐟𝐨𝐫 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐒𝐲𝐬𝐭𝐞𝐦𝐬
In the world of production AI, accuracy alone is not enough. What matters even more is control. That’s where AI Guardrails come in. Think of them as seatbelts and traffic rules for AI systems, ensuring every output stays safe, compliant, and useful.

AI Guardrails are the rules and checks that prevent models from producing harmful, biased, or off-topic responses. Without them, your system is just one bad output away from compliance risks, data breaches, or broken workflows.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐡𝐨𝐰 𝐀𝐈 𝐠𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬 𝐰𝐨𝐫𝐤 𝐢𝐧 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞:
1. Start with defining what to validate: RAIL Spec, Pydantic Model, or String. This sets the expected output structure.
2. Connect it with the LLM callable, which generates the raw answer.
3. Add prompt instructions so the LLM knows exactly what format and constraints to follow.
4. Initialize the guard from the spec so the system can enforce the rules.
5. When the guard is invoked, it runs both the LLM and the validation layer together.
6. The guard then calls the LLM API and receives the response.
7. The output is validated against your rules.
8. If it’s valid, it passes through. If not, guard takes the specified action (reask, filter, fix, refrain, or no-op).
9. Every step is logged for traceability and compliance.

This gives teams a robust control layer without constantly re-engineering the model itself.

𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:
* Prevents unsafe or non-compliant outputs
* Maintains format consistency in critical systems
* Reduces operational risks and escalations

For teams scaling AI beyond prototypes, guardrails are not optional they’re foundational.

How are you currently validating outputs in your AI systems?

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𝟏𝟔 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬 𝐄𝐯𝐞𝐫𝐲 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐌𝐮𝐬𝐭 𝐊𝐧𝐨𝐰 𝐢𝐧 𝟐𝟎𝟐𝟓:

1. Feedforward Neural Networks
Simple input to output mapping with no memory. Used for classification and regression. Examples include TensorFlow Dense Layers and PyTorch Linear.

2. Convolutional Neural Networks
Use convolution filters to detect spatial patterns. Ideal for image and object detection. Examples include ResNet and EfficientNet.

3. Recurrent Neural Networks
Sequence aware models with temporal memory. Used for time series and text tasks. Examples include Simple RNN and GRU.

4. Long Short Term Memory Networks
Handle long dependencies and prevent vanishing gradients. Used in speech recognition and translation. Examples include Seq2Seq and LSTM networks.

5. Transformer Networks
Use attention for parallel processing. Powering LLMs and summarization. Examples include GPT, BERT, T5.

6. Graph Neural Networks
Learn on graph structures with node and edge embeddings. Common in fraud detection and recommendations. Examples include GraphSAGE and GNN libraries like DGL.

7. Autoencoders
Learn to compress and reconstruct data for anomaly detection and denoising. Examples include Sparse AE and Denoising AE.

8. Variational Autoencoders
Learn probabilistic latent spaces and generate new data. Used in image generation. Examples include VAE models in TensorFlow and PyTorch.

9. GANs
Use a generator and discriminator to synthesize realistic data. Used for deepfakes and super resolution. Examples include DCGAN and StyleGAN.

10. Residual Networks
Use skip connections to build very deep models. Great for computer vision benchmarks. Examples include ResNet50 and ResNet101.

11. Recurrent Convolutional Networks
A hybrid of CNN and RNN for video and activity recognition. Common example is the RCNN variant.

12. Capsule Networks
Use dynamic routing and spatial hierarchy for complex object recognition. Known from Hintons CapsNet.

13. Siamese Networks
Shared weight networks for similarity scoring. Used for face verification. Examples include Siamese CNN and matching networks.

14. Reinforcement Learning Networks
Use agents and rewards to optimize decisions. Applied in robotics and game playing. Examples include DQN and policy gradient models.

15. Mixture of Experts Networks
Combine multiple experts for dynamic routing and scaling LLMs. Examples include Switch Transformer and Mixtral.

16. Neural ODE Networks
Continuous time modeling for physics simulations and forecasting. Examples include Neural ODE in PyTorch.

Mastering these architectures will help you understand how modern AI systems are built and which model fits the problem you are solving.

𝐖𝐡𝐢𝐜𝐡 𝐧𝐞𝐮𝐫𝐚𝐥 𝐧𝐞𝐭𝐰𝐨𝐫𝐤 𝐭𝐲𝐩𝐞 𝐝𝐨 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐞𝐱𝐩𝐥𝐨𝐫𝐞 𝐧𝐞𝐱𝐭?

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#NeuralNetworks #DeepLearning #AIEngineering #AgenticAI

𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬? 𝐒𝐭𝐚𝐫𝐭 𝐇𝐞𝐫𝐞 (𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐑𝐨𝐚𝐝𝐦𝐚𝐩):

𝟏. 𝐃𝐞𝐟𝐢𝐧𝐞 𝐖𝐡𝐚𝐭 𝐘𝐨𝐮𝐫 𝐀𝐠𝐞𝐧𝐭 𝐖𝐢𝐥𝐥 𝐃𝐨
* Decide on the specific tasks your AI agent will handle (e.g., managing emails, qualifying leads).
* Set clear goals for what your agent will achieve.
* Example: An AI assistant that qualifies leads and drafts emails.

𝟐. 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐄𝐱𝐜𝐡𝐚𝐧𝐠𝐞 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥
* Create structured data models for your agent using tools like Pydantic, JSON Schema, and TypeScript interfaces.
* Ensure the inputs and outputs are clear and easy to work with.

𝟑. 𝐖𝐫𝐢𝐭𝐞 𝐈𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐒𝐲𝐬𝐭𝐞𝐦
* Define clear instructions for the agent on what to do and how it should behave.
* Test different instruction styles to see what works best.
* Tools: Mistral, Custom Prompt Templates, IBM Granite.

𝟒. 𝐀𝐝𝐝 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐄𝐱𝐭𝐞𝐫𝐧𝐚𝐥 𝐀𝐜𝐭𝐢𝐨𝐧𝐬
* Enable your agent to make decisions and take actions based on external data (e.g., browsing the web, performing calculations).
* Tools: OpenAI Functions, Custom APIs, Anthropic Tools.

𝟓. 𝐖𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐀𝐠𝐞𝐧𝐭𝐬 (𝐢𝐟 𝐍𝐞𝐞𝐝𝐞𝐝)
* Design agents that specialize in different tasks and collaborate with each other.
* Tools: IBM Watson Orchestrate, CrewAI, Custom Orchestration.

𝟔. 𝐀𝐝𝐝 𝐌𝐞𝐦𝐨𝐫𝐲 𝐚𝐧𝐝 𝐂𝐨𝐧𝐭𝐞𝐱𝐭
* Let your agent remember previous conversations and actions for more context-aware interactions.
* Tools: RAG Buffers, Conversation Buffers, Vector Databases.

𝟕. 𝐀𝐝𝐝 𝐌𝐮𝐥𝐭𝐢𝐦𝐞𝐝𝐢𝐚 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬
* Allow your agent to process speech, voice commands, and analyze images and videos.
* Tools: Whisper, DALL·E, GPT-4 Vision.

𝟖. 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐃𝐞𝐥𝐢𝐯𝐞𝐫 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 𝐂𝐥𝐞𝐚𝐫𝐥𝐲
* Ensure that your agent’s results are easily understood by both humans and machines.
* Tools: Markdown Processors, PDF Generators, Structured Data Formats.

𝟗. 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐔𝐬𝐞𝐫 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞 𝐨𝐫 𝐀𝐏𝐈
* Create an easy-to-use web interface or API for users to interact with the agent.
* Tools: React, FastAPI, Streamlit, Gradio.

By following this roadmap, you can build intelligent, efficient, and interactive AI agents that meet real-world needs. Each step equips you with the knowledge and tools to create a more capable and versatile AI.

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#GenAI #AIAgents #AgenticAI #AI

Cloud certified Big Data Solutions Architect with 15+ years of experience driving information management strategy. Experienced in database technologies, Big Data architecture, Business Intelligence technologies, AI/ML and Cloud architecture. Extensive corporate experience influencing organizations to adopt innovative solutions to meet market demand. Exceptional at cultivating and diligently maintaining alliances with senior executives and customers. Expert at leading technical teams to provide solutions and exceed organizational objectives. 

Areas of expertise:
•	Data Platform                         •	Data Warehouse.          •	Big Data
•	Consulting                              •	Client Lead                    •	Scrum Master
•      ETL architecture	                •	Cloud Computing         •	Machine Learning 
•	Business Intelligence            •	Data Governance          •	Data Modeling
•	DevOps                                  •	Cloud Data pipeline      •	Artificial Intelligence
•	BI Conversational Assistant  •	Cognitive services.       •	Performance & Scalability 
•	Data Security & Compliance •	Data Migration.             •	Development Lead

𝐖𝐚𝐧𝐭 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐀𝐈 𝐅𝐫𝐨𝐦 𝐒𝐜𝐫𝐚𝐭𝐜𝐡 𝐢𝐧 𝟐𝟎𝟐𝟓 / 𝟐𝟎𝟐𝟔? 
Start with YouTube.

No paid course.
No complicated roadmap.
Just the right creators consistently teaching real AI, ML, LLMs, and data science.

𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐓𝐨𝐩 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥𝐬 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧 𝐭𝐚𝐤𝐞 𝐲𝐨𝐮 𝐟𝐫𝐨𝐦 𝐛𝐞𝐠𝐢𝐧𝐧𝐞𝐫 → 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝:

1. Sentdex: Practical Python, AI & ML tutorials
Link: https://lnkd.in/gJCTeqJa

2. Krish Naik: Hands-on ML & AI implementation
Link: https://lnkd.in/grbYZnHg

3. Codebasics: Beginner-friendly data science & AI tutorials
Link: https://lnkd.in/gg4JuJsK

4. Data School: Super clear data science concepts
Link: https://lnkd.in/gBJZiE7w

5. Andrej Karpathy: Deep learning & LLM lectures
Link: https://lnkd.in/gSCeXw4Q

6. DeepLearning.AI: In-depth AI courses with experts
Link: https://lnkd.in/gNgcTUzd

7. IBM Technology: Enterprise AI, cloud & infra content
Link: https://lnkd.in/gQjbbWef

8. Machine Learning Street Talk (MLST): Technical deep dives with researchers
Link: https://lnkd.in/gq-U4tuz

9. Hamel Husain: RAG, evals & fine-tuning explained practically
Link: https://lnkd.in/gzc3XWbt

10. 3Blue1Brown: Stunning math & ML explanations
Link: https://lnkd.in/g3VzA7x3

11. StatQuest with Josh Starmer: ML/statistics made fun and simple
Link: https://lnkd.in/gtsw38sh

12. AI Explained: Breakdowns of new AI developments
Link: https://lnkd.in/gdxNgWrV

13. Two Minute Papers: Bite-sized AI research explained
Link: https://lnkd.in/gtri2Yyp

14. AI Anytime: Latest LLMs, tools & AI projects
Link: https://lnkd.in/g-jt4yHC

15. Lex Fridman: Long-form conversations with top AI thinkers
Link: https://lnkd.in/g6byncfY

16. Yannic Kilcher: AI research papers explained
Link: https://lnkd.in/gDkvUQnb

If you are serious about AI start learning from the people who teach it best.

📌 Save this list. Share it with your team.
Your AI journey can literally start today.

𝐖𝐡𝐢𝐜𝐡 𝐜𝐫𝐞𝐚𝐭𝐨𝐫 𝐡𝐞𝐥𝐩𝐞𝐝 𝐲𝐨𝐮 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭? 👇

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𝐄𝐯𝐞𝐫𝐲 𝐭𝐞𝐜𝐡 𝐥𝐞𝐚𝐝𝐞𝐫 𝐭𝐨𝐝𝐚𝐲 𝐢𝐬 𝐫𝐚𝐜𝐢𝐧𝐠 𝐭𝐨 𝐝𝐞𝐩𝐥𝐨𝐲 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬.
But here is the truth most teams realize *too late*:

👉 AI does not fail because it is not smart enough.
It fails because it is not protected enough.

That is where guardrails come in.

From prompt leaks to harmful outputs, or even rogue tool actions, AI agents need the same rigor you apply to production systems: security, validation, monitoring, and human-in-the-loop oversight.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐰𝐡𝐚𝐭 𝐞𝐯𝐞𝐫𝐲 𝐭𝐞𝐜𝐡 𝐥𝐞𝐚𝐝𝐞𝐫 𝐌𝐔𝐒𝐓 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐰𝐡𝐢𝐥𝐞 𝐝𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬:

𝟏. 𝐌𝐮𝐥𝐭𝐢-𝐋𝐚𝐲𝐞𝐫𝐞𝐝 𝐒𝐚𝐟𝐞𝐭𝐲

Start with known risks, then keep adding layers as new vulnerabilities appear.
Guardrails aren’t a one-time setup, they’re an evolving safety architecture.

𝟐. 𝐂𝐨𝐧𝐭𝐞𝐧𝐭, 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 & 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐂𝐨𝐧𝐭𝐫𝐨𝐥𝐬

Use rules-based protections, moderation filters, relevance checks, and PII filters to ensure the model stays safe, compliant, and predictable.

𝟑. 𝐎𝐮𝐭𝐩𝐮𝐭 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧

Your agent should NEVER execute a tool or action unless the response matches expected values.
This reduces hallucination-driven failures dramatically.

𝟒. 𝐓𝐨𝐨𝐥-𝐋𝐞𝐯𝐞𝐥 𝐒𝐚𝐟𝐞𝐠𝐮𝐚𝐫𝐝𝐬

Every tool your agent calls must be risk-scored (low/medium/high).
High-risk tools (like refund APIs, financial actions, or database writes) require extra layers of vetting.

𝟓. 𝐇𝐮𝐦𝐚𝐧 𝐎𝐯𝐞𝐫𝐬𝐢𝐠𝐡𝐭 𝐟𝐨𝐫 𝐄𝐝𝐠𝐞 𝐂𝐚𝐬𝐞𝐬

Even the best agent needs a human fallback
especially for:

* Exceeding failure thresholds, and
* High-risk, irreversible actions.

𝐓𝐡𝐞 𝐠𝐨𝐚𝐥?

Not to restrict your AI agent.
But to enable it to operate reliably at scale.

Because in enterprise AI…
✨ Stability > Speed
✨ Safety > Smartness

If you want AI that performs consistently in production, build guardrails before you build features.

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𝐓𝐡𝐞 𝟔 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐁𝐞𝐡𝐢𝐧𝐝 𝐄𝐯𝐞𝐫𝐲 𝐑𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦

These design patterns are the foundation of every production grade agentic system. 
Skip them and your agents will break the moment complexity appears.

𝟏. 𝐒𝐞𝐪𝐮𝐞𝐧𝐭𝐢𝐚𝐥 𝐏𝐚𝐭𝐭𝐞𝐫𝐧
Each agent completes a step and passes the result to the next one.
Ideal for workflows that depend on strict order.
Used for multi step tasks like research, summarization, or approvals.

𝟐. 𝐏𝐚𝐫𝐚𝐥𝐥𝐞𝐥 𝐏𝐚𝐭𝐭𝐞𝐫𝐧
The query is split into independent tasks like web search and database search, processed simultaneously.
Speeds up answers and improves completeness when data comes from multiple sources.

𝟑. 𝐑𝐨𝐮𝐭𝐞𝐫 𝐏𝐚𝐭𝐭𝐞𝐫𝐧
A single entry agent decides which specialist agent should handle the task.
Perfect for customer support, billing, or tech troubleshooting where queries vary widely.

𝟒. 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐍𝐞𝐭𝐰𝐨𝐫𝐤
A coordinator agent manages a builder agent and a reviewer agent in a continuous improvement loop.
Useful for generating high quality output through iterative refinement.

𝟓. 𝐀𝐮𝐭𝐨 𝐁𝐮𝐢𝐥𝐝𝐞𝐫 𝐏𝐚𝐭𝐭𝐞𝐫𝐧
The system divides the task, generates documentation, writes code, and produces tests using different agents.
Ideal for software generation, workflow creation, and automation design.

𝟔. 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐠𝐞𝐧𝐭 𝐏𝐚𝐭𝐭𝐞𝐫𝐧
Multiple agents collaborate without supervision, handling planning, execution, and validation end to end.
This is the closest pattern to fully autonomous AI teams.

If you want your agents to scale reliably, these patterns are the architectural starting point.

𝐖𝐡𝐢𝐜𝐡 𝐩𝐚𝐭𝐭𝐞𝐫𝐧 𝐝𝐨 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭 𝐰𝐢𝐭𝐡 𝐟𝐢𝐫𝐬𝐭?

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#AIAgents #AgentDesign #AgenticAI #AIEngineering

𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 𝐂𝐡𝐞𝐚𝐭𝐬𝐡𝐞𝐞𝐭

Vector databases are everywhere in modern AI systems, but most people still do not understand how they actually work.
Here is the the key concepts, index types, and mistakes to avoid when building high performance retrieval systems:

𝟏. 𝐊𝐞𝐲 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬.
* Vectors represent text, images, and code numerically.
* Similarity search finds nearest vectors by distance.
* Embedding models generate these vector representations.
* Distance metrics like cosine, dot product, and L2 help compare vectors.
* Indexes accelerate search.
* Hybrid and metadata filtering combine structured search with vector retrieval.

𝟐. 𝐈𝐧𝐝𝐞𝐱 𝐓𝐲𝐩𝐞𝐬.
* HNSW builds multi layer graphs for fast high dimensional search.
* LSH hashes similar vectors into buckets for large scale retrieval.
* IVF and IMI cluster vectors to speed up search.
* Annoy uses random projection trees for approximate neighbors.
* Spectral hashing compresses vectors into compact binary codes.

𝟑. 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐮𝐬𝐞 𝐇𝐍𝐒𝐖, 𝐈𝐕𝐅, 𝐨𝐫 𝐏𝐐.
* HNSW is great for low latency, high recall search on small to medium datasets.
* IVF works well for huge datasets but needs training and handles multi tenant systems effectively.
* PQ reduces memory footprint for massive vector sets by compressing vectors, trading some recall for scalability.

𝟒. 𝐒𝐜𝐡𝐞𝐦𝐚 𝐃𝐞𝐬𝐢𝐠𝐧 𝐓𝐢𝐩𝐬.
* Use metadata for filtering.
* Store original text with embeddings.
* Use one embedding model and consistent dimensions.
* Normalize vectors when using cosine similarity.
* Version embeddings and use semantic chunking.

𝟓. 𝐓𝐡𝐢𝐧𝐠𝐬 𝐭𝐨 𝐀𝐯𝐨𝐢𝐝.
* Mixing embedding models breaks consistency.
* Tiny chunks reduce meaning.
* Wrong distance metrics degrade retrieval.
* HNSW can get memory heavy.
* Weak filtering or missing metadata hurts accuracy.

Mastering these principles is essential for building fast, reliable RAG and semantic search systems.

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#VectorDatabases #RAG #AIagents #LLMEngineering

𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 𝐯𝐬 𝐌𝐢𝐱𝐭𝐮𝐫𝐞 𝐨𝐟 𝐄𝐱𝐩𝐞𝐫𝐭𝐬 (𝐌𝐨𝐄) - 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞

𝟏. 𝐁𝐚𝐬𝐢𝐜 𝐈𝐝𝐞𝐚
- Transformers: Like a team where everyone works on every task together.
- MoE: Like specialists where only a few are called in for a specific task.

𝟐. 𝐇𝐨𝐰 𝐓𝐡𝐞𝐲 𝐖𝐨𝐫𝐤
- Transformers: All parameters are active for every input. Attention selectively focuses on relevant parts but the whole network is engaged.
- MoE: A router selects a few relevant experts for each input, and only those process the data.

𝟑. 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲
- Transformers: All parameters are active which means higher compute cost per token.
- MoE: Only a subset of experts is active, making it faster and more resource-efficient.

𝟒. 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐔𝐬𝐞
- Transformers: Useful when every detail in the input must be processed by the entire model.
- MoE: Useful when different inputs require different skills, allowing for cost savings.

𝟓. 𝐒𝐢𝐳𝐞 𝐨𝐟 𝐌𝐨𝐝𝐞𝐥
- Transformers: Can be large and costs rise as the model scales.
- MoE: Can be very large in capacity but uses fewer active parameters per input.

𝟔. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐀𝐧𝐚𝐥𝐨𝐠𝐲
- Transformers: The whole class works together on every subject’s project.
- MoE: Math students do the math project, art students do the art project.

𝟕. 𝐃𝐨𝐰𝐧𝐬𝐢𝐝𝐞𝐬
- Transformers: Expensive to run at large scale.
- MoE: Harder to train as the router must select the right experts consistently.

𝟖. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬 𝐨𝐟 𝐋𝐋𝐌𝐬
- Transformers: GPT-3, BERT, T5, LLaMA.
- MoE: Google Switch Transformer, GLaM, Mixtral.

Both architectures are shaping the future of AI but they represent different trade-offs. Transformers offer universal thoroughness while MoE focuses on targeted efficiency.
𝐖𝐡𝐢𝐜𝐡 𝐨𝐧𝐞 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐰𝐢𝐥𝐥 𝐝𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐀𝐈 𝐦𝐨𝐝𝐞𝐥𝐬?

𝐁𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐟𝐨𝐫 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰
Building an agentic AI workflow is like designing a city where data, models, and applications work together seamlessly. Here is a breakdown of the key components from the blueprint:

𝟏. 𝐃𝐚𝐭𝐚 𝐒𝐨𝐮𝐫𝐜𝐞 𝐒𝐲𝐬𝐭𝐞𝐦𝐬
- Databases like IBM DB2
- Data Lakes like Snowflake, IBM COS, Delta Lake
- Streams and Caches like Kafka, Redis

𝟐. 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬
- Ingestion with Kafka
- Quality checks with IBM Databand
- Transformation with IBM DataStage

𝟑. 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐒𝐭𝐨𝐫𝐞
- Open source options like Feast
- Managed options like Tecton

𝟒. 𝐌𝐨𝐝𝐞𝐥 𝐋𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞
- Model experiments with Weights and Biases
- Model store options from open source and enterprise tools like Hugging Face and MLflow
- Model serving through platforms like SageMaker

𝟓. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐂𝐨𝐫𝐞
- Foundation models from providers like OpenAI
- Agent frameworks such as LangChain

𝟔. 𝐌𝐨𝐝𝐮𝐥𝐚𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬
- Build with Docker
- Orchestrate with Kubernetes
- Messaging via Kafka

𝟕. 𝐅𝐫𝐨𝐧𝐭-𝐄𝐧𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬
- Web/app frameworks like React, Next.js
- Data visualization with D3.js, IBM Cognos Analytics
- Internal tools like Power BI, Retool

𝟖. 𝐒𝐞𝐫𝐯𝐞𝐫𝐥𝐞𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬
- Cloud serverless with AWS Lambda
- Edge runtime with Cloudflare Workers

𝟗. 𝐇𝐲𝐛𝐫𝐢𝐝 𝐂𝐥𝐨𝐮𝐝 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞
- Platforms like IBM Cloud
- Orchestration with Kubernetes
- Infrastructure as code with Terraform
- Networking with Cloudflare

𝟏𝟎. 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞
- System monitoring with IBM Instana
- App error tracking with Sentry
- Logging with Elastic (ELK)
- ML/LLM monitoring with IBM Watson OpenScale

Each block in this architecture plays a crucial role in making AI agents not just intelligent, but also reliable, scalable, and easy to integrate into business workflows.

𝐈𝐟 𝐲𝐨𝐮 𝐰𝐞𝐫𝐞 𝐭𝐨 𝐝𝐞𝐬𝐢𝐠𝐧 𝐲𝐨𝐮𝐫 𝐨𝐰𝐧 𝐀𝐈 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰, 𝐰𝐡𝐢𝐜𝐡 𝐩𝐚𝐫𝐭 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐟𝐨𝐜𝐮𝐬 𝐨𝐧 𝐟𝐢𝐫𝐬𝐭 𝐝𝐚𝐭𝐚, 𝐦𝐨𝐝𝐞𝐥𝐬, 𝐨𝐫 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭?

Most people think building with AI is about calling an LLM API and chaining a few prompts together. 

But, the real challenge starts when you leave the playground and step into production where your agents need to remember, recover, coordinate, and not burn the house down when 10 of them run in parallel.

And that is where this new open guide hits different.
📘 Agentic Design Patterns a 424-page handbook by Antonio Gulli (Director at Google) is probably the most practical resource I’ve seen on building real-world AI systems that scale.

𝐈𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐣𝐮𝐬𝐭 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐢𝐧𝐠 𝐰𝐡𝐚𝐭 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐫𝐞, 𝐢𝐭 𝐰𝐚𝐥𝐤𝐬 𝐲𝐨𝐮 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐡𝐞 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐭𝐡𝐚𝐭 𝐦𝐚𝐤𝐞 𝐭𝐡𝐞𝐦 𝐰𝐨𝐫𝐤 𝐢𝐧 𝐭𝐡𝐞 𝐰𝐢𝐥𝐝:
* How to chain prompts and route tasks with structure
* How to manage memory across long-running sessions
* How to recover gracefully when agents get stuck mid-task
* How to build safety and guardrails into your workflows
* How to get multiple agents talking without chaos
* And how to integrate tools and context using MCP

𝐈𝐭 𝐢𝐬 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐞𝐝 𝐢𝐧𝐭𝐨 𝟐𝟏 𝐝𝐞𝐬𝐢𝐠𝐧 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 𝟒 𝐛𝐢𝐠 𝐚𝐫𝐞𝐚𝐬:
1. Foundational: prompt chaining, routing, planning
2. Advanced systems: memory, learning, monitoring
3. Production concerns: error handling, evaluation, safety
4. Multi-agent architectures: coordination, reasoning, optimization

𝐈𝐟 𝐲𝐨𝐮 𝐚𝐫𝐞 𝐚𝐧 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐚𝐬𝐤𝐢𝐧𝐠:
* “How do I persist memory across workflows?”
* “What happens when 10+ agents run at once?”
* “How do I keep things from breaking in production?”

This book has the answers. And it does not stop at theory it is packed with real code examples you can apply right now.

𝐑𝐞𝐚𝐝 𝐢𝐭 𝐡𝐞𝐫𝐞: https://lnkd.in/gabQDnDN

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𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐰𝐢𝐥𝐥 𝐧𝐨𝐭 𝐬𝐜𝐚𝐥𝐞 𝐢𝐧 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐰𝐨𝐫𝐥𝐝 𝐢𝐟 𝐭𝐡𝐞𝐲 𝐜𝐚𝐧 𝐧𝐨𝐭 𝐭𝐚𝐥𝐤 𝐭𝐨 𝐭𝐨𝐨𝐥𝐬, 𝐀𝐏𝐈𝐬, 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐲.
This is where MCP (Model Context Protocol) changes the game.

Think of MCP as the “middleware” that lets AI Agents plug into anything databases, APIs, configs, files, or even other agents. But here is the kicker: it is not a one-size-fits-all model.

𝐓𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝟖 𝐂𝐨𝐫𝐞 𝐌𝐂𝐏 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐞𝐯𝐞𝐫𝐲 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐬𝐡𝐨𝐮𝐥𝐝 𝐤𝐧𝐨𝐰:

---

1. Analytics Data Access Pattern
MCP connects AI agents to OLAP systems via tools, making large-scale analytics queries possible.
Use case: Business intelligence, dashboards, and real-time insights.

2. Configuration Use Pattern
AI agents fetch and apply configurations directly from config management services.
Use case: Dynamic system tuning, feature flagging, multi-tenant app setups.

3. Hierarchical MCP Pattern
Parent MCP servers orchestrate domain-level MCPs (payments, wallet, customer).
Use case: Enterprise architectures where domains must stay modular but interoperable.

4. Local Resource Access Pattern
Agents execute file operations (read, write, transform) through MCP tools.
Use case: Enterprise workflows with on-premise or hybrid file processing.

5. Event-Driven Integration Pattern
MCP streams events into async workflows for real-time decisioning.
Use case: Fraud detection, IoT alerts, trading signals, ops monitoring.

6. MCP-to-Agent Pattern
General AI agents delegate tasks to specialist agents via MCP.
Use case: Connecting a customer service bot to a finance-specific expert agent.

7. Direct API Wrapper Pattern
MCP tools wrap APIs, making complex API integrations simpler and uniform.
Use case: AI agents querying multiple SaaS tools (CRM, HR, billing) in one flow.

8. Composite Service Pattern
MCP orchestrates multiple APIs into one unified service layer.
Use case: Multi-step workflows like booking + payments + notifications.

---

👉 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥𝐢𝐭𝐲: 𝐊𝐧𝐨𝐰𝐢𝐧𝐠 𝐭𝐡𝐞𝐬𝐞 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐭𝐨𝐲 𝐝𝐞𝐦𝐨 𝐚𝐧𝐝 𝐝𝐞𝐩𝐥𝐨𝐲𝐢𝐧𝐠 𝐚 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐠𝐫𝐚𝐝𝐞 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦.
𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐌𝐂𝐏 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐰𝐢𝐥𝐥 𝐛𝐞𝐜𝐨𝐦𝐞 𝐭𝐡𝐞 𝐝𝐞𝐟𝐚𝐮𝐥𝐭 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐟𝐨𝐫 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝟏𝟐 𝐦𝐨𝐧𝐭𝐡𝐬?

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𝐌𝐨𝐬𝐭 𝐩𝐞𝐨𝐩𝐥𝐞 𝐭𝐡𝐢𝐧𝐤 𝐀𝐈 𝐣𝐮𝐬𝐭 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞𝐬 𝐚𝐧𝐬𝐰𝐞𝐫𝐬 𝐢𝐧 𝐨𝐧𝐞 𝐬𝐡𝐨𝐭. 𝐁𝐮𝐭 𝐭𝐡𝐚𝐭 𝐢𝐬 𝐧𝐨𝐭 𝐡𝐨𝐰 𝐭𝐡𝐞 𝐬𝐦𝐚𝐫𝐭𝐞𝐬𝐭 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐨𝐫𝐤.
Enter ReAct Agents (Reasoning + Acting).

𝐈𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐣𝐮𝐦𝐩𝐢𝐧𝐠 𝐭𝐨 𝐚𝐧 𝐚𝐧𝐬𝐰𝐞𝐫, 𝐑𝐞𝐀𝐜𝐭 𝐚𝐠𝐞𝐧𝐭𝐬 𝐠𝐨 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐚 𝐥𝐨𝐨𝐩:

1. Thought: The AI asks itself: “What should I do next?”
2. Action: It does something, like searching or using a tool.
3. Observation: It checks the result of that action.
4. Repeat: This loop continues until the AI gathers enough to give a final answer.

This makes them far smarter than single-step models, because they can reason, act, verify, and improve their responses dynamically.

𝐄𝐱𝐚𝐦𝐩𝐥𝐞: 𝐂𝐡𝐞𝐜𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐖𝐞𝐚𝐭𝐡𝐞𝐫

* Question: Do I need an umbrella today?
* Thought: I should check if it will rain.
* Action: Open weather app.
* Observation: It says “Rain in the afternoon.”
* Thought: Since it will rain, I need an umbrella.
* Final Answer: Yes, take an umbrella.

That is how AI moves from just guessing → to reasoning like humans.
𝐃𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐝𝐨𝐦𝐢𝐧𝐚𝐭𝐞𝐝 𝐛𝐲 𝐨𝐧𝐞-𝐬𝐡𝐨𝐭 𝐦𝐨𝐝𝐞𝐥𝐬 𝐥𝐢𝐤𝐞 𝐆𝐏𝐓 𝐨𝐫 𝐛𝐲 𝐚𝐠𝐞𝐧𝐭𝐢𝐜 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐭𝐡𝐚𝐭 𝐫𝐞𝐚𝐬𝐨𝐧 𝐚𝐧𝐝 𝐚𝐜𝐭 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩?

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𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐚 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐨𝐫𝐤 𝐮𝐧𝐝𝐞𝐫 𝐭𝐡𝐞 𝐡𝐨𝐨𝐝? 

We often use LLMs daily, but few understand the step-by-step intelligence behind their predictions. It is not magic it is math, language patterns, and smart engineering working together.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐚 𝐬𝐢𝐦𝐩𝐥𝐞 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐨𝐟 𝐡𝐨𝐰 𝐢𝐭 𝐚𝐥𝐥 𝐡𝐚𝐩𝐩𝐞𝐧𝐬:

𝟏. 𝐌𝐚𝐬𝐬𝐢𝐯𝐞 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐚𝐤𝐞 (𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬)
The model is trained on billions of text examples from books, articles, code, and conversations. It learns how words are used together, grammar, tone, and domain-specific knowledge.

𝟐. 𝐓𝐨𝐤𝐞𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (𝐁𝐫𝐞𝐚𝐤𝐢𝐧𝐠 𝐓𝐞𝐱𝐭 𝐢𝐧𝐭𝐨 𝐏𝐢𝐞𝐜𝐞𝐬)
Text is split into tokens (small word units). This allows the model to process language mathematically rather than as raw text.

𝟑. 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 (𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐂𝐨𝐧𝐭𝐞𝐱𝐭)
A transformer network processes the entire sentence at once to understand context, tone, and structure, not just individual words.

𝟒. 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐬𝐭𝐢𝐜 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 (𝐍𝐞𝐱𝐭 𝐖𝐨𝐫𝐝 𝐆𝐮𝐞𝐬𝐬𝐢𝐧𝐠)
The model does not “know” answers. It predicts the most likely next token based on what came before. That is why it builds sentences so fluently.

𝟓. 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 & 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭
The model is adjusted with domain-specific data and human feedback to make it more useful, safe, and reliable.

𝟔. 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 (𝐘𝐨𝐮 𝐀𝐬𝐤, 𝐈𝐭 𝐑𝐞𝐬𝐩𝐨𝐧𝐝𝐬)
When you enter a prompt, the model tokenizes it, processes through its layers, predicts the best words, and streams back a response in real time.

There is no real “understanding” or “memory” of the world here. It is advanced pattern prediction trained on massive data.

𝐖𝐡𝐚𝐭 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐭𝐡𝐢𝐬 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐟𝐢𝐧𝐝 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐟𝐚𝐬𝐜𝐢𝐧𝐚𝐭𝐢𝐧𝐠?

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𝐀𝐂𝐏 𝐯𝐬 𝐌𝐂𝐏 𝐯𝐬 𝐀𝟐𝐀 𝐯𝐬 𝐀𝐍𝐏: 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐊𝐞𝐲 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 𝐢𝐧 𝐀𝐠𝐞𝐧𝐭 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥𝐬
When building AI agent ecosystems, the way agents talk to each other is just as important as the intelligence inside them. Four major approaches stand out: ACP, MCP, A2A, and ANP. Here is how they compare:

𝟏. 𝐀𝐂𝐏 (𝐀𝐠𝐞𝐧𝐭 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 - 𝐈𝐁𝐌)

- Finds, routes, and swaps messages between many helpers.
- Mainly used for routing, finding helpers, and sending control or status info.
- Difficulty: Varies from simple to complex depending on setup.
- Network awareness: Sometimes tracks network state.
- Message types: Routing, finding helpers, control, and status updates.
- Structure: A middleman connects the client and the server.

𝟐. 𝐌𝐂𝐏 (𝐌𝐨𝐝𝐞𝐥 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 - 𝐀𝐧𝐭𝐡𝐫𝐨𝐩𝐢𝐜)

- Shares and manages background info between helpers or agents.
- Used for handling specific agent information.
- Difficulty: Easier since it focuses only on sharing information.
- Network awareness: Limited, just shares what it is told.
- Message types: Context and specific data.
- Structure: Client talks directly to a server.

𝟑. 𝐀𝟐𝐀 (𝐀𝐠𝐞𝐧𝐭-𝐭𝐨-𝐀𝐠𝐞𝐧𝐭 - 𝐆𝐨𝐨𝐠𝐥𝐞)

- Allows two helpers to talk directly to each other.
- Ideal for one-to-one simple tasks and interactions.
- Difficulty: Easier because it supports direct conversations.
- Network awareness: Usually not aware of the larger network.
- Message types: Simple ask-and-answer style.
- Structure: Central peer-to-peer.

𝟒. 𝐀𝐍𝐏 (𝐀𝐠𝐞𝐧𝐭 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 - 𝐂𝐢𝐬𝐜𝐨)

- Organizes and coordinates many helpers across a network.
- Used for communication, routing, and control across an entire system.
- Difficulty: More complex because it manages routing and network state.
- Network awareness: Fully aware of and manages the entire network.
- Message types: Routing, control, and management messages.
- Structure: Decentralized peer-to-peer.

𝐈𝐧 𝐬𝐢𝐦𝐩𝐥𝐞 𝐭𝐞𝐫𝐦𝐬

- ACP acts like a middleman connector.
- MCP focuses on sharing context.
- A2A enables direct agent conversations.
- ANP manages an entire network of agents.

Each of these protocols serves different needs depending on whether you want simplicity, context-sharing, direct conversations, or full-scale network management.

𝐈𝐟 𝐲𝐨𝐮 𝐰𝐞𝐫𝐞 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐨𝐰𝐧 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦, 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐬𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐝𝐢𝐫𝐞𝐜𝐭 𝐨𝐧𝐞-𝐭𝐨-𝐨𝐧𝐞 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐫 𝐝𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐟𝐮𝐥𝐥 𝐧𝐞𝐭𝐰𝐨𝐫𝐤 𝐜𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐢𝐨𝐧?

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𝐘𝐨𝐮𝐫 𝐑𝐀𝐆 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐫𝐞𝐭𝐮𝐫𝐧𝐬 𝟏𝟎 𝐜𝐡𝐮𝐧𝐤𝐬. 𝐎𝐧𝐥𝐲 𝟐 𝐚𝐫𝐞 𝐮𝐬𝐞𝐟𝐮𝐥.
Same concept but 10x better retrieval. Here's how:

𝟏. 𝐑𝐀𝐆 (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧)
* Fetches relevant info from knowledge sources (PDFs, DBs, APIs).
* Merges it with the user query to give context to the LLM.
* Output = context-enriched response.

𝟐. 𝐑𝐞𝐅𝐑𝐀𝐆 (𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐝 𝐅𝐢𝐧𝐞-𝐆𝐫𝐚𝐢𝐧𝐞𝐝 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥)
* Goes deeper by breaking chunks into smaller token embeddings.
* Uses an RL-trained relevance checker to refine what’s fetched.
* Keeps only the most relevant tokens, embedding them intelligently into the prompt.
* This reduces noise, improves retrieval precision, and boosts accuracy.

RAG solved hallucinations. ReFRAG solves everything RAG missed.

𝐖𝐡𝐢𝐜𝐡 𝐨𝐧𝐞 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐰𝐢𝐥𝐥 𝐝𝐨𝐦𝐢𝐧𝐚𝐭𝐞 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐑𝐀𝐆 𝐨𝐫 𝐑𝐞𝐅𝐑𝐀𝐆?

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𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐢𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐦𝐨𝐝𝐞𝐥𝐬. 
It is about fixing the pain points that quietly break your system in production. 

𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝟏𝟐 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐞𝐯𝐞𝐫𝐲 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐁𝐮𝐢𝐥𝐝𝐞𝐫 𝐟𝐚𝐜𝐞𝐬 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐭𝐨 𝐬𝐨𝐥𝐯𝐞 𝐭𝐡𝐞𝐦:

𝟏. 𝐈𝐭 𝐅𝐨𝐫𝐠𝐞𝐭𝐬 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠:
Agents lose context between interactions. Add short-term memory and vector DB retrieval to keep conversations intelligent and continuous.

𝟐. 𝐈𝐭 𝐂𝐚𝐧 𝐧𝐨𝐭 𝐓𝐡𝐢𝐧𝐤, 𝐉𝐮𝐬𝐭 𝐑𝐞𝐩𝐥𝐢𝐞𝐬:
Without reasoning, agents act like parrots. Add Chain-of-Thought, ReAct, and tool use to enable real problem-solving.

𝟑. 𝐈𝐧𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫:
Agents drift from instructions. Use clear system and task prompts to ensure structured and reliable responses.

𝟒. 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐂𝐡𝐚𝐨𝐬:
Without orchestration, agents loop or break. Assign roles and use frameworks like CrewAI or LangGraph for structured collaboration.

𝟓. 𝐌𝐞𝐬𝐬𝐲 𝐎𝐮𝐭𝐩𝐮𝐭:
Unreadable blobs are common. Use JSON schemas, Pydantic, and Markdown formatting for structured, clean responses.

𝟔. 𝐓𝐨𝐨 𝐒𝐥𝐨𝐰 𝐭𝐨 𝐑𝐞𝐬𝐩𝐨𝐧𝐝:
Long wait times break user experience. Use distilled models, caching, and asynchronous workflows for real-time performance.

𝟕. 𝐃𝐚𝐭𝐚 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐂𝐨𝐧𝐜𝐞𝐫𝐧𝐬:
Sensitive data leaks through prompts or logs. Add sanitization, local embeddings, and strict access control.

𝟖. 𝐍𝐨 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲:
Opaque answers erode trust. Show reasoning traces, summaries, and references.

𝟗. 𝐍𝐨𝐭 𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞:
Fails on edge cases. Add reflection loops, fallback tools, or human-in-the-loop for resilience.

𝟏𝟎. 𝐖𝐞𝐚𝐤 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲:
Prompt injection and data poisoning are real. Guardrails, input validation, and red-teaming are must-haves.

𝟏𝟏. 𝐏𝐨𝐨𝐫 𝐓𝐨𝐨𝐥 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧:
APIs and systems fail to connect seamlessly. Use orchestration layers and standardized connectors to stabilize workflows.

𝟏𝟐. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐋𝐨𝐨𝐩𝐬:
No metrics mean no improvement. Track performance continuously to make your agent better over time.

Every broken agent is not a dead end it is a fixable problem.
𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐩𝐚𝐢𝐧 𝐩𝐨𝐢𝐧𝐭𝐬 𝐡𝐚𝐯𝐞 𝐲𝐨𝐮 𝐟𝐚𝐜𝐞𝐝 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬?

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𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬

𝐓𝐡𝐞 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐨𝐟 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬

AI has come a long way from being simple word predictors to becoming intelligent agents that can plan, reason, and even collaborate with other AIs. Let’s walk through this journey:

𝟏. 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬: 𝐓𝐨𝐤𝐞𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐨𝐫𝐬

- The earliest AI models only predicted the next word based on patterns learned from data.
- Think of them like a predictive text keyboard.

𝟐. 𝐓𝐫𝐚𝐢𝐧𝐞𝐝 𝐭𝐨 𝐂𝐡𝐚𝐭: 𝐂𝐡𝐚𝐭𝐛𝐨𝐭𝐬

- Models were fine-tuned to interact with humans.
- They moved beyond guessing words to following instructions and holding conversations.

𝟑. 𝐓𝐫𝐚𝐢𝐧𝐞𝐝 𝐭𝐨 𝐔𝐬𝐞 𝐓𝐨𝐨𝐥𝐬: 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬

- AI began using apps, databases, and calculators to get things done.
- They could fetch information, perform calculations, and automate workflows like digital assistants.

𝟒. 𝐓𝐫𝐚𝐢𝐧𝐞𝐝 𝐭𝐨 𝐏𝐥𝐚𝐧 𝐚𝐧𝐝 𝐑𝐞𝐚𝐬𝐨𝐧: 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐠𝐞𝐧𝐭𝐬

- These agents do more than just react.
- They can observe, plan, act, and use tools to achieve goals.
- Like self-driving cars, they solve problems independently but are still evolving.

𝟓. 𝐓𝐫𝐚𝐢𝐧𝐞𝐝 𝐭𝐨 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐞: 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬

- The latest stage where multiple AI agents collaborate.
- Each agent has a specialized role, and together they act like a team solving complex tasks.

The journey of AI agents shows us that they are no longer just answering queries but becoming collaborators and problem-solvers.

𝐃𝐨 𝐲𝐨𝐮 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐰𝐨𝐫𝐤 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐬𝐡𝐚𝐩𝐞𝐝 𝐦𝐨𝐫𝐞 𝐛𝐲 𝐬𝐢𝐧𝐠𝐥𝐞 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐨𝐫 𝐧𝐞𝐭𝐰𝐨𝐫𝐤𝐬 𝐨𝐟 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐧𝐠 𝐦𝐮𝐥𝐭𝐢-𝐚𝐠𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦𝐬?

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𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐃𝐨 𝐧𝐨𝐭 𝐍𝐞𝐞𝐝 𝐁𝐞𝐭𝐭𝐞𝐫 𝐌𝐨𝐝𝐞𝐥𝐬. 𝐓𝐡𝐞𝐲 𝐍𝐞𝐞𝐝 𝐁𝐞𝐭𝐭𝐞𝐫 𝐌𝐞𝐦𝐨𝐫𝐲.

If you want to build reliable AI systems, you must understand how agent memory actually works.

𝟏. 𝐒𝐡𝐨𝐫𝐭 𝐓𝐞𝐫𝐦 𝐌𝐞𝐦𝐨𝐫𝐲
Acts like the AI scratchpad. Stores temporary details such as user preferences, parameters, intermediate results, and tool outputs while solving a task.
Helps the agent reason step by step, recall sub-tasks, and avoid overloading the context window.

𝟐. 𝐋𝐨𝐧𝐠 𝐓𝐞𝐫𝐦 𝐌𝐞𝐦𝐨𝐫𝐲
Stores persistent knowledge across sessions.
Includes episodic memory for past interactions, semantic memory for domain knowledge, and procedural memory for learned workflows.
Allows the agent to adapt, personalize, and get smarter over time.

𝟑. 𝐓𝐚𝐬𝐤 𝐒𝐭𝐚𝐭𝐞 𝐒𝐭𝐨𝐫𝐚𝐠𝐞
The agent stores evolving task details so the reasoning chain stays organized and uncluttered.

𝟒. 𝐓𝐚𝐬𝐤 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐑𝐞𝐜𝐚𝐥𝐥
When continuing a multi step task, the agent pulls previous details back into working memory to keep reasoning consistent.

𝟓. 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥
The agent fetches relevant knowledge such as travel data, domain rules, or previous decisions to guide the next action.

𝟔. 𝐌𝐞𝐦𝐨𝐫𝐲 𝐒𝐭𝐨𝐫𝐚𝐠𝐞
Once the task is complete, the agent stores valuable patterns, workflows, and successful outcomes for future use.

𝟕. 𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐌𝐞𝐦𝐨𝐫𝐲
A temporary buffer holding parameters, intermediate outputs, candidate actions, and next steps.
This is where reasoning, planning, and tool calling actively happen.

𝟖. 𝐋𝐨𝐧𝐠 𝐓𝐞𝐫𝐦 𝐌𝐞𝐦𝐨𝐫𝐲 𝐓𝐲𝐩𝐞𝐬
Episodic memory: remembers past choices and interactions
Semantic memory: stores domain knowledge
Procedural memory: saves workflows and routines so the agent can reuse them

Agents that combine working memory with long term memory become more reliable, more personalized, and far more capable across real world tasks.

𝐖𝐡𝐢𝐜𝐡 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐚𝐠𝐞𝐧𝐭 𝐦𝐞𝐦𝐨𝐫𝐲 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐰𝐢𝐥𝐥 𝐦𝐚𝐤𝐞 𝐭𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐢𝐦𝐩𝐚𝐜𝐭 𝐨𝐧 𝐟𝐮𝐭𝐮𝐫𝐞 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬?

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#AgentMemory #AIAgents #AIArchitecture #AgenticAI #LLM

𝗔 𝗴𝗼𝗼𝗱 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁 𝗿𝗲𝗲𝗹 𝗿𝗲𝘃𝗲𝗮𝗹𝘀 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝗼𝗳 𝗮 𝗴𝗿𝗲𝗮𝘁 𝗲𝗽𝗶𝘀𝗼𝗱𝗲
A single clip can surface the themes that matter most.

In this highlight reel, Nicholas Renotte moves from creativity to risk to modularity to the essence of agentic AI.

Here is the insight:
→ Agentic systems only work when the cost, risk, and architecture actually make sense.

Mini quote from the clip: “agents are LLMs plus tools”

We touch everything from modular design to error margins.
Nick reminds us that certain industries cannot afford even slight discrepancies.

▪️ Build modularly so you can swap components as the ecosystem evolves.

He also points out a core tension.
If everything changes daily, how do you keep up without burning out?

The answer sits in his closing message.
Creativity still belongs to the human.
Use AI. But keep thinking for yourself.

👉 𝗟𝗶𝗻𝗸 𝘁𝗼 𝗳𝘂𝗹𝗹 𝗲𝗽𝗶𝘀𝗼𝗱𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀.
🎧 𝗟𝗶𝘀𝘁𝗲𝗻 𝗼𝗻 𝗬𝗼𝗨𝗧𝗨𝗯𝗲, 𝗦𝗽𝗼𝘁𝗶𝗳𝘆, 𝗮𝗻𝗱 𝗔𝗽𝗽𝗹𝗲 𝗣𝗼𝗱𝗰𝗮𝘀𝘁𝘀.

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