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.

FREE MCP Course by Microsoft for Beginners

1. Full Course (Lessons 1-11) MCP for Beginners
https://lnkd.in/gCKchGmi

2. Introduction to Model Context Protocol (MCP)
https://lnkd.in/gFCBwCie

3. MCP core concepts
https://lnkd.in/gvFUM2J5

4. MCP security best practices
https://lnkd.in/gKb3Ujhx

5. Build your first MCP server
https://lnkd.in/g2mB7_Pp

6. How to build, test & deploy MCP apps with real tools and workflows
https://lnkd.in/gfUBP8Fk

7. Advanced MCP: Secure, scalable, and multi-modal AI agents
https://lnkd.in/gQmcPnn5


8. How to contribute to MCP: Tools, docs, code & more
https://lnkd.in/gwhV378G

9. Lessons from MCP early adopters
https://lnkd.in/gRtRVQq6

10. MCP development best practices
https://lnkd.in/gw7QByYt

11. MCP in action: Real-world case studies
https://lnkd.in/gtwfnDTw

12. Build AI agents in VS Code: 4 hands-on labs with MCP + AI Toolkit
https://lnkd.in/gQHcc6mM

♻️ Repost this to help your network get started 
➕ Follow Jothi Moorthy for more

Jothi Moorthy

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?

♻️ Repost this to help your network get started
➕ Follow Jothi Moorthy for more

#AgenticAI #AIAgents #AIArchitecture #LLMOps #AIEngineering

𝐘𝐨𝐮 𝐡𝐚𝐯𝐞 𝐠𝐨𝐭 𝐚𝐧 𝐋𝐋𝐌. 𝐘𝐨𝐮 𝐡𝐚𝐯𝐞 𝐠𝐨𝐭 𝐀𝐏𝐈𝐬 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚. 𝐁𝐮𝐭 𝐡𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐦𝐚𝐤𝐞 𝐭𝐡𝐞𝐦 𝐚𝐥𝐥 𝐰𝐨𝐫𝐤 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫? 𝐄𝐧𝐭𝐞𝐫 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧.

LangChain is a powerful tool for creating interactive AI applications by connecting large language models (LLMs) with external data, tools, and APIs. It enables you to create chatbots, agents, and automated systems with context-awareness and dynamic decision-making.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐰𝐡𝐲 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧 𝐢𝐬 𝐬𝐨 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞:

* Simplifies Prompt Management: It helps you handle complex prompts and makes chaining multiple LLM calls easy.
* Contextual Memory: LangChain provides memory capabilities that ensure AI systems remain contextually aware, improving interaction over time.
* Open-Source and Versatile: It works with both OpenAI and other open-source models, offering flexibility for your needs.
* Integrates with Everything: From APIs to databases, LangChain integrates seamlessly with external tools and data, enabling robust functionality in your AI systems.
* Supports RAG: LangChain retrieval-augmented generation (RAG) makes it powerful for accessing and using external knowledge dynamically.

𝐂𝐨𝐫𝐞 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬:

1. Models: Use LLMs to generate responses.
2. Prompt Templates: Format and manage prompts with ease.
3. Chains: Link multiple tasks together for more complex workflows.
4. Memory: Store and retrieve context, ensuring long-term interaction.

𝐇𝐨𝐰 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧 𝐖𝐨𝐫𝐤𝐬:

1. User Input → Used to create prompts dynamically.
2. Prompt Templates → Fill placeholders with user data for effective formatting.
3. LLM → Process the prompts and generate responses.
4. Custom Data Retrieval → Use external sources for contextual answers (via RAG).

𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐄𝐱𝐚𝐦𝐩𝐥𝐞:

* A user asks about the electric scooter market. LangChain connects with a local database and an SEO tool to generate context-rich content in real-time.

LangChain is perfect for developers aiming to integrate multiple AI models into seamless workflows. It is also the go-to choice for anyone looking to optimize the decision-making and memory capabilities of AI agents.

𝐖𝐡𝐢𝐜𝐡 𝐀𝐈 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐥𝐥 𝐲𝐨𝐮 𝐛𝐮𝐢𝐥𝐝 𝐰𝐢𝐭𝐡 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧?

♻️ Repost this to help your network get started
➕ Follow Jothi Moorthy for more

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

CS Manager – Agentic AI Architect @ IBM | #30 Favikon Top Creator 🔥 | 255K+ Followers Across Platforms | Keynote Speaker | Board Member | Podcast Host | WITC Magazine Publisher | IBM ATE Top 0.05% | Multiple Patents

Niraj Prasad Bhatta

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?

♻️ Repost this to help your network get started
➕ Follow Jothi for more

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?

♻️ Repost this to help your network get started
➕ Follow Jothi Moorthy for more

#NeuralNetworks #DeepLearning #AIEngineering #AgenticAI

𝟭𝟰 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿𝘀 𝗣𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝗻 𝟮𝟬𝟮𝟱
If your AI can’t take action, it’s just a chatbot.
These servers give LLMs the ability to interact with real systems. File access, API calls, memory, containers, payments — this is what brings agent workflows to life.

I mapped 14 verified MCP servers across core categories. Each one is tested and linked in the carousel.

𝐅𝐢𝐥𝐞𝐬 & 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞
• Filesystem MCP
• Google Drive MCP
• Notion MCP
𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧
• Slack MCP
𝐒𝐞𝐚𝐫𝐜𝐡 & 𝗪𝐞𝐛
• Brave Search MCP
• Perplexity MCP
• Fetch MCP
𝐌𝐞𝐦𝐨𝐫𝐲 & 𝗧𝐢𝐦𝐞
• Memory MCP
• Time MCP
𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 & 𝐂𝐚𝐜𝐡𝐢𝐧𝐠
• PostgreSQL MCP
• Redis MCP
𝐃𝐞𝐯𝐎𝐩𝐬 & 𝐈𝐧𝐟𝐫𝐚
• GitHub MCP
• Docker MCP
𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 & 𝐅𝐢𝐧𝐚𝐧𝐜𝐞
• Stripe MCP

Each slide covers what the server does, how it connects, and where to find it.

𝗥𝗲𝗽𝗼𝘀𝘁 𝘁𝗼 𝘀𝗵𝗮𝗿𝗲 𝘄𝗶𝘁𝗵 𝗼𝘁𝗵𝗲𝗿 𝗯𝘂𝗶𝗹𝗱𝗲𝗿𝘀 ☘️

14 MCP Servers Powering Agentic AI in 2025

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?

♻️ Repost this to help your network get started
➕ Follow for more

#AIAgents #AgentDesign #AgenticAI #AIEngineering

𝐈𝐧 𝐫𝐞𝐚𝐥𝐢𝐭𝐲, 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞, 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐑𝐞𝐚𝐝𝐲 𝐑𝐀𝐆 𝐦𝐞𝐚𝐧𝐬 𝐝𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐚𝐧 𝐞𝐧𝐭𝐢𝐫𝐞 𝐬𝐲𝐬𝐭𝐞𝐦 𝐚𝐫𝐨𝐮𝐧𝐝 𝐢𝐭 𝐨𝐧𝐞 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧 𝐡𝐚𝐧𝐝𝐥𝐞 𝐒𝐜𝐚𝐥𝐞, 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲, 𝐋𝐚𝐭𝐞𝐧𝐜𝐲, 𝐚𝐧𝐝 𝐀𝐝𝐚𝐩𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲.

𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 𝐭𝐡𝐚𝐭 𝐦𝐚𝐤𝐞 𝐭𝐡𝐚𝐭 𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞:

𝟏. 𝐃𝐚𝐭𝐚 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 & 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠
The quality of what you retrieve depends on the quality of what you store.

* ETL pipelines (Airbyte, Meltano) to automate data flow
* Document loaders (LangChain, Unstructured) to handle diverse formats
* Cleaning and chunking to structure text for efficient retrieval

𝟐. 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬 & 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬
This is the foundation of how your AI “understands” meaning.

* Embedding models (OpenAI, Cohere) convert text into vector space
* Vector databases (ChromaDB, Weaviate, Pinecone, Qdrant) enable semantic search at scale

𝟑. 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 & 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬
Retrieval is more than just “fetching data.”

* Hybrid retrieval: combine dense and sparse methods for better recall
* Context selection: techniques like Top-k or Max Marginal Relevance ensure relevance
* Knowledge graphs (Neo4j) and RDF stores add structure to relationships

𝟒. 𝐐𝐮𝐞𝐫𝐲 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 & 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫
Once retrieved, information must be processed and synthesized effectively.

* Guardrails to ensure structured, safe outputs
* RAG fusion to merge multiple context sources into a single, coherent answer
* Prompt engineering for precision and control

𝟓. 𝐌𝐞𝐦𝐨𝐫𝐲 & 𝐂𝐚𝐜𝐡𝐢𝐧𝐠
Performance scales only if memory does.

* Short-term caching (Redis, Zep) for quick access
* Long-term storage (Weaviate) for persistent knowledge
* Semantic caching (GPTCache) to avoid redundant computation

𝟔. 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠, 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 & 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲
What gets measured gets improved.

* Track metrics like retrieval accuracy, latency, and hallucination rates
* Use tools like Langfuse, Helicone, and TruLens to debug and iterate

𝟕. 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 & 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧
None of this matters if you cannot ship it.

* Containerization (Docker), orchestration (Kubernetes), and hosting (Vercel) ensure scalability and reliability.

Here is the takeaway: RAG is evolving from a model feature into an AI system design principle.
And those who master its components will be the ones building the most powerful agentic systems of the next decade.

Which of these components are you focusing on right now?

♻️ Repost this to help your network build better AI systems
➕ Follow Jothi Moorthy for more

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.

♻️ Repost this to help your network get started
➕ Follow Jothi Moorthy for more

#VectorDatabases #RAG #AIagents #LLMEngineering

𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐀𝐈 𝐌𝐨𝐝𝐞𝐥 𝐠𝐢𝐯𝐞 𝐲𝐨𝐮 𝐆𝐨𝐥𝐝 𝐨𝐧𝐞 𝐝𝐚𝐲 𝐚𝐧𝐝 𝐆𝐚𝐫𝐛𝐚𝐠𝐞 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭? 𝐈𝐭'𝐬 𝐧𝐨𝐭 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥—𝐢𝐭'𝐬 𝐭𝐡𝐞 𝐜𝐨𝐧𝐭𝐞𝐱𝐭.
Context Engineering is not just a technique; it's the backbone of how we make AI systems truly intelligent. 
Instead of changing the model itself, we focus on adjusting what the AI knows before it answers. 
By shaping the context that surrounds the model’s input, we make AI more relevant, accurate, and aligned with the user's intent.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:
* AI doesn't "understand" the world, it reacts to the context it’s given.
* Better context leads to better reasoning, fewer hallucinations, and more accurate outputs.
* It enables AI to personalize its responses and avoid mistakes, ensuring the AI stays consistent with your specific needs.

𝐓𝐡𝐞 𝐥𝐚𝐲𝐞𝐫𝐬 𝐭𝐡𝐚𝐭 𝐦𝐚𝐤𝐞 𝐮𝐩 𝐜𝐨𝐧𝐭𝐞𝐱𝐭:
1. Instructions: What does the AI need to do?
2. Knowledge: What information does the AI need to know?
3. Tools: What additional tools or resources does the AI need to access to complete the task?
4. RAG (Retrieval Augmented Generation): Adding knowledge for better-informed answers.
5. Prompt Engineering & Structured Outputs: Creating better prompts for clearer results.

𝐇𝐨𝐰 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬:
1. User Query: AI starts by receiving a query from the user.
2. Context Collector: AI collects all relevant data and rules.
3. Context Builder: It creates a well-structured prompt for the task.
4. Model Generates Output: The AI generates an answer.
5. Feedback Loop: Updates context for the next time the query comes in.

Effective Context Engineering makes AI not just a responder, but a true problem-solving partner, ensuring that it acts with a deep understanding of the situation.

What context do you think would be the most challenging to design for an AI system?

♻️ Repost this to help your network get started
➕ Follow Jothi for more

𝐈𝐟 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐦𝐚𝐬𝐭𝐞𝐫 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠, 
start with these 20 algorithms they are the foundation of everything from recommendation systems to GPT models  

AI is not magic. It is math, logic, and pattern recognition wrapped in these core algorithms that power nearly every intelligent system we use today.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐥𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞:

1. Linear Regression
Predicts continuous outcomes like house prices or sales forecasts.

2. Logistic Regression
Used for classification predicting yes/no outcomes (like churn prediction).

3. Naïve Bayes
Probabilistic model for text and spam detection, assuming features act independently.

4. K-Nearest Neighbors (KNN)
Finds the closest data points to make predictions great for recommendations.

5. Decision Trees
Creates rule-based branches for predictions transparent and interpretable.

6. Random Forests
Combines multiple decision trees to improve accuracy and reduce overfitting.

7. Support Vector Machines (SVM)
Finds the best boundary to separate data classes used in image classification.

8. K-Means Clustering
Groups data into clusters perfect for segmentation and pattern discovery.

9. Principal Component Analysis (PCA)
Reduces dimensions for visualization and compression without losing much information.

10. Gradient Boosting (XGBoost, LightGBM, CatBoost)
Ensemble methods that build models iteratively to correct previous errors.

11. Feedforward Neural Networks (Perceptrons)
The foundation of deep learning great for regression and classification.

12. Backpropagation
The learning engine behind neural networks adjusts weights to minimize errors.

13. Convolutional Neural Networks (CNNs)
Specialized for visual tasks from detecting tumors to identifying objects in photos.

14. Recurrent Neural Networks (RNNs, LSTMs, GRUs)
Handle sequences used in text, speech, and time-series prediction.

15. Transformers (BERT, GPT, Attention Mechanisms)
The backbone of modern AI understanding context, relationships, and long dependencies.

16. Gradient Descent (SGD, Adam, RMSProp)
The optimization workhorse used in nearly every deep learning model.

17. Expectation-Maximization (EM Algorithm)
Estimates hidden variables used in clustering and probabilistic models.

18. Genetic Algorithms / Evolutionary Strategies
Mimic natural selection evolving solutions for optimization problems.

19. A* Search Algorithm
Finds the shortest path using heuristics used in navigation and pathfinding.

20. Reinforcement Learning (Q-Learning, DQN, Policy Gradients)
Teaches AI through trial and reward the logic behind AlphaGo and autonomous systems.

If you understand these, you can decode any AI system no matter how complex.

𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐢𝐬 𝐦𝐨𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐭𝐨𝐝𝐚𝐲 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐭𝐨 𝐦𝐚𝐬𝐭𝐞𝐫 𝐟𝐢𝐫𝐬𝐭?

♻️ Repost this to help your network get started
➕ Follow Jothi Moorthy for more

#AIEngineering #MachineLearning

𝗜 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗿𝗲𝗮𝗱 Building Agentic AI Systems by Anjanava Biswas and Wrick Talukdar.

It’s a timely look at what it takes to design AI systems that move beyond prompts to plan, reason, and act.

𝗪𝗵𝗮𝘁 𝗜 𝗹𝗶𝗸𝗲𝗱 𝗺𝗼𝘀𝘁 𝗶𝘀 𝗵𝗼𝘄 𝗴𝗿𝗼𝘂𝗻𝗱𝗲𝗱 𝗶𝘁 𝗶𝘀 𝗶𝗻 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲.
Instead of chasing buzzwords, it focuses on how to actually build agentic architectures that work in the real world.

𝗧𝗵𝗲 𝘀𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗻 𝗿𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗶𝗻𝘁𝗿𝗼𝘀𝗽𝗲𝗰𝘁𝗶𝗼𝗻 really caught my attention.
It shows how agents can step back, review their own reasoning, and adjust before small errors turn into big problems.

𝗔𝗻𝗼𝘁𝗵𝗲𝗿 𝗸𝗲𝘆 𝗶𝗱𝗲𝗮 𝗶𝘀 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝘁𝗼𝗼𝗹 𝘂𝘀𝗲 𝗮𝗻𝗱 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴.
The authors walk through how agents can link multiple tools or APIs together to handle complex goals more efficiently.

𝗜 𝗮𝗹𝘀𝗼 𝗳𝗼𝘂𝗻𝗱 𝘁𝗵𝗲 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗼𝗿, 𝗪𝗼𝗿𝗸𝗲𝗿, 𝗮𝗻𝗱 𝗗𝗲𝗹𝗲𝗴𝗮𝘁𝗼𝗿 𝗺𝗼𝗱𝗲𝗹 refreshingly clear.
It gives a simple way to organise collaboration across agents without overcomplicating the system.

𝗘𝘁𝗵𝗶𝗰𝘀 𝗮𝗻𝗱 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗿𝗲𝗻’𝘁 𝗮𝗻 𝗮𝗳𝘁𝗲𝗿𝘁𝗵𝗼𝘂𝗴𝗵𝘁.
There’s a consistent emphasis on trust, transparency, and keeping human alignment at the centre of autonomous design.

If you’re working on agent-based systems, this book gives you a solid foundation for building scalable, responsible AI.

Definitely one I’ll be referencing again. 🔥

Appreciate the Packt team and Sonia Chauhan for sending me a copy of this book, a thoughtful addition to the growing conversation around agentic AI.

𝐈𝐟 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐬𝐭𝐚𝐲 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐧 𝐀𝐈, 𝐭𝐡𝐞𝐬𝐞 𝟏𝟓 𝐆𝐞𝐧𝐀𝐈 𝐭𝐞𝐫𝐦𝐬 𝐚𝐫𝐞 𝐧𝐨𝐧-𝐧𝐞𝐠𝐨𝐭𝐢𝐚𝐛𝐥𝐞  

AI is evolving faster than ever and if you can not speak its language, you will struggle to keep up. Whether you are a developer, data scientist, or product strategist, these are the core concepts defining the next generation of AI systems.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐜𝐫𝐚𝐬𝐡 𝐜𝐨𝐮𝐫𝐬𝐞:

1. Generative AI
AI that creates new content text, images, music, or code by learning from existing data.

2. LLM (Large Language Model)
A deep learning model trained on massive text data to generate human-like language and reasoning.

3. Foundation Model
A large pre-trained model that serves as the base for fine-tuning specialized AI systems.

4. Fine-Tuning
Adapting a pre-trained model for a specific domain or dataset for improved accuracy.

5. Prompt Engineering
Crafting precise instructions (prompts) to guide AI toward producing the most relevant and high-quality responses.

6. Zero-Shot Learning
When AI performs a task without being trained on examples of that exact task.

7. Few-Shot Learning
Training AI with only a handful of examples crucial for fast adaptation.

8. Hallucination
When AI generates confident-sounding but false information a key risk in LLMs.

9. Context Window
The maximum amount of text/data an AI model can “remember” during a single interaction.

10. Token
The smallest unit of text (word or subword) that an AI model reads or generates.

11. Embedding
A numerical representation of data that captures meaning and relationships between concepts.

12. Diffusion Model
A generative method that creates realistic images by transforming noise into coherent visuals.

13. GAN (Generative Adversarial Network)
Two neural networks compete one generates, the other critiques to produce ultra-realistic synthetic data.

14. RAG (Retrieval-Augmented Generation)
Combines retrieval and generation to give LLMs access to external, up-to-date knowledge.

15. Multimodal AI
AI that understands and creates across multiple formats text, image, audio, and video simultaneously.

If you understand these 15, you do not just *use* GenAI you can *architect* it.

𝐖𝐡𝐢𝐜𝐡 𝐨𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐭𝐞𝐫𝐦𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐰𝐢𝐥𝐥 𝐬𝐡𝐚𝐩𝐞 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝐛𝐢𝐠 𝐰𝐚𝐯𝐞 𝐨𝐟 𝐀𝐈 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬?

♻️ Repost this to help your network get started
➕ Follow Jothi Moorthy for more

#GenerativeAI #LLM #AIEngineering

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?

♻️ Repost this to help your network get started
➕ Follow Jothi Moorthy for more

𝐈𝐦𝐚𝐠𝐢𝐧𝐞 𝐚𝐬𝐤𝐢𝐧𝐠 𝐨𝐧𝐞 𝐩𝐞𝐫𝐬𝐨𝐧 𝐭𝐨: 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐚 𝐭𝐨𝐩𝐢𝐜, 𝐰𝐫𝐢𝐭𝐞 𝐚 𝐫𝐞𝐩𝐨𝐫𝐭, 𝐟𝐚𝐜𝐭-𝐜𝐡𝐞𝐜𝐤 𝐢𝐭, 𝐬𝐜𝐡𝐞𝐝𝐮𝐥𝐞 𝐚 𝐦𝐞𝐞𝐭𝐢𝐧𝐠, 𝐬𝐞𝐧𝐝 𝐞𝐦𝐚𝐢𝐥𝐬, 𝐚𝐧𝐝 𝐮𝐩𝐝𝐚𝐭𝐞 𝐲𝐨𝐮𝐫 𝐂𝐑𝐌-𝐚𝐥𝐥 𝐚𝐭 𝐨𝐧𝐜𝐞.  

They did break. 

Single AI agents are breaking too. That is why we are building teams.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐚 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐨𝐟 𝐡𝐨𝐰 𝐦𝐮𝐥𝐭𝐢-𝐚𝐠𝐞𝐧𝐭 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐨𝐫𝐤:

𝟏. 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐢𝐧 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬
Different agents interact through structured communication patterns depending on the problem type.

* Parallel: Multiple agents process data simultaneously.
* Sequential: One agent’s output becomes the next agent’s input.
* Loop: Agents iteratively refine results until they meet the goal.
* Router: Directs input to the most capable agent dynamically.
* Aggregator: Collects insights from multiple agents into a unified response.
* Network / Hierarchical: Combines specialized agents with a coordinating leader for complex workflows.

𝟐. 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬
Here is how different configurations power modern AI ecosystems:

* Hierarchical: One agent orchestrates multiple sub-agents (e.g., search + summarization + Slack automation).
* Human-in-the-Loop: AI collaborates with humans for validation or escalation.
* Shared Tools: Multiple agents access common tools like vector search or APIs.
* Sequential: Tasks flow through multiple specialized agents from search to generation to delivery.
* Database with Tools: Agents use structured databases and APIs to transform or query data dynamically.
* Memory Conversion: Agents with memory components store, recall, and adapt responses over time.

𝟑. 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬
Multi-agent architectures allow complex reasoning, collaboration, and task decomposition something single LLMs can not do effectively.
They are the foundation of next-gen AI systems that act autonomously across tools, APIs, and real-time data streams.

We are moving from “AI models” to “AI ecosystems.”

𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐦𝐮𝐥𝐭𝐢-𝐚𝐠𝐞𝐧𝐭 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐰𝐢𝐥𝐥 𝐝𝐨𝐦𝐢𝐧𝐚𝐭𝐞 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐢𝐧 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝟐 𝐲𝐞𝐚𝐫𝐬?

♻️ Repost this to help your network get started
➕ Follow Jothi Moorthy for more

#MultiAgentSystems #AIAgents #AgenticAI

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?

♻️ Repost this to help your network get started
➕ Follow Jothi Moorthy for more

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?

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?

♻️ Repost this to help your network get started 
➕ Follow Jothi Moorthy for more

Large Language Models might generate fluent text, but they struggle with one critical limitation: 

They do not know what they don’t know.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐰𝐡𝐲 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐢𝐬 𝐭𝐡𝐞 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐩𝐢𝐞𝐜𝐞 𝐭𝐡𝐚𝐭 𝐭𝐮𝐫𝐧𝐬 𝐋𝐋𝐌𝐬 𝐟𝐫𝐨𝐦 𝐠𝐮𝐞𝐬𝐬𝐞𝐫𝐬 𝐢𝐧𝐭𝐨 𝐠𝐫𝐨𝐮𝐧𝐝𝐞𝐝 𝐭𝐡𝐢𝐧𝐤𝐞𝐫𝐬:

𝟏. 𝐓𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥
LLMs rely on pre-trained knowledge that eventually goes stale.
Ask them about recent events, and they’ll respond with:
“I don’t have any information regarding this topic.”
They simply can not access new, external data on their own.

𝟐. 𝐇𝐨𝐰 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐅𝐢𝐱𝐞𝐬 𝐈𝐭 (𝐑𝐀𝐆)
Retrieval-Augmented Generation (RAG) changes the game.
It lets LLMs pull relevant, up-to-date information from indexed documents, vector databases, or enterprise data sources before generating answers.

𝟑. 𝐓𝐡𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬

* The user submits a query (e.g., “Why did OpenAI’s CEO get rehired so quickly?”)
* The system embeds and indexes existing documents into vector form
* The retriever fetches the most relevant chunks
* The LLM combines context from those chunks with the query
* The model then generates a grounded, factual, and contextualized response

𝟒. 𝐓𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞
Without RAG: The model gives generic or outdated replies.
With RAG: It delivers reasoned, context-aware answers rooted in real-world facts.

RAG does not make models smarter it makes them relevant.

So the real question is not how powerful your model is, but how well it retrieves and uses knowledge.

♻️ Repost this to help your network get started
➕ Follow Jothi Moorthy for more

#RAG #LLMEngineering #AIArchitecture

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?

♻️ Repost this to help your network get started
➕ Follow for more

taplio

Jothi Moorthy's Linkedin Analytics

Jothi Moorthy

Performance ScoreJothi Moorthy's LinkedIn Performance

Jothi Moorthy's Best Posts (last 30 days)

Ready to grow your LinkedIn presence?

taplio

.css-6ytdur{background-image:linear-gradient(284deg, rgba(39, 43, 72, 1) 0%, rgba(33, 47, 183, 1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Jothi Moorthy's Linkedin Analytics

Jothi Moorthy

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);}Jothi Moorthy's LinkedIn Performance

Jothi Moorthy's Best Posts .css-1emoo84{font-size:var(--chakra-fontSizes-md);font-weight:400;color:var(--chakra-colors-gray-600);}(last 30 days)

Ready to grow your LinkedIn presence?

Jothi Moorthy's Linkedin Analytics

Performance ScoreJothi Moorthy's LinkedIn Performance

Jothi Moorthy's Best Posts (last 30 days)