Armand Ruiz

Director Data Science & AI Elite

Leading the Product Management org for the AI Portfolio of products focused on Generative AI and Traditional ML. Products include: watsonx.ai and watsonx.gov

Vice President of AI Platform

I am building the world's largest team of 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 by

- Recruiting hundreds of new engineers around the globe
- Reskilling our existing talent
- Offering them top-quality training in new-generation AI

Responsible for Pre-Sales Generative AI Pilots with the new IBM watsonx.ai Platform.

Director watsonx team - Client Engineering

Director of the group IBM Data Science Elite team. We prove the value of IBM Watson solutions by co-creating pilots with our customers. I managed a team of 90+ data scientists that worked on 100s of complex AI implementations.

Director Data Science

I manage a rockstar team of Product Managers leading the product portfolio of IBM Watson AI products.

Product Director - Product Management, Watson Studio and Machine Learning

Leading new Data Science products for IBM

Lead Product Manager - IBM Data Science and Watson Machine Learning

Bring Open Source data science tech to IBM stack

Product Manager for Data Science Open Source Extensibility 

In charge of Data Science projects in EMEA

Data Science Advocate - IBM Data Science & Machine Learning

Support sales in Data Science opportunities

Technical Seller Data Science & Smarter Cities

Internship to bring new innovations to IBM

Intern - Technical Software Engineer, Extreme Blue

Vodafone

Optimize 3G network coverage using Data Science

Data scientist - Radio Optimization Engineering

Federació Catalana de Tennis

Tennis Coach

VP of Product - AI Platform @IBM

As the Director of AI Engineering at IBM, I lead the watsonx team, a global group of over 1,000 AI Engineers who work on cutting-edge Generative AI solutions for various industries. With more than 12 years of experience in AI, I have a passion for democratizing practical AI skills and empowering professionals to leverage AI to transform their business.

Previously, I managed IBM's Data Science and AI Elite, delivering complex AI implementations for hundreds of clients. 

I also led the product management of IBM Watson Studio and Watson Machine Learning, an AI platform used by hundreds of customers and SaaS apps powered by AI. I hold a Master of Science in Telecommunications from Universitat Politècnica de Catalunya and speak three languages: Spanish, Catalan, French, and English.

Connect with me!

✉️ JOIN MY NEWSLETTER: Every Saturday morning, you'll get 1 actionable tip to launch and grow your AI strategies in business in less than 4 minutes. → GO HERE: https://www.nocode.ai/

📧 Email: armand@nocode.ai
X: @armand_ruiz


"It is kind of fun to do the impossible"
All opinions are my own and don't represent my employer.

Called it. 🎯 The Model Context Protocol (MCP) is officially moving to the Linux Foundation under the new Agentic AI Foundation.

I’ve been saying for a while that open governance is the only viable path forward for AI infrastructure. Today’s news proves it.

This brings Anthropic, OpenAI, Microsoft, Google, and others under one neutral banner. 

10,000+ active servers. 
97M+ SDK downloads. 
Zero vendor lock-in.

If you want an ecosystem to flourish, you have to open it up. The era of Agentic AI just got a serious upgrade.

Read the news here: https://lnkd.in/g7vy5-x2

At NeurIPS2025, Google presented Titans, a new architecture that combines the speed of RNNs with the performance of Transformers. 

The trade-off between infinite context and computational cost is the biggest hurdle in AI right now.

While Transformers are accurate but expensive to scale, and linear models are fast but lose detail, Titans delivers the best of both worlds by introducing Test-Time Memorization.

The Core Innovation: Instead of a static state, Titans uses a deep neural memory module that actively updates while running, utilizing a "Surprise Metric":
- Low Surprise: Routine data is processed efficiently without clogging memory.
- High Surprise: Anomalies and key details are instantly integrated into long-term memory.

The Results:
- Scales effectively to 2M+ token context windows.
- Outperforms larger models (including GPT-4) on extreme long-context reasoning benchmarks.
- Generalizes beyond text to genomics and time-series.

This is a move from passive processing to active, efficient learning.

Jensen just dropped a masterclass on the future of AI. 

It was great to hear from him in a long-format podcast conversation. 

Three key insights he shared:

1/ Human language is the new syntax: The "tech divide" is closing. You won't need C++ to code the future; you just need to speak.

2/ The "30-Day" Mindset: Nvidia operates like they are always 30 days from bankruptcy. This paranoia fuels their innovation and resilience.

3/ Safety is an engineering problem: Forget the sci-fi movie plots. AI safety is just like cybersecurity—as capabilities rise, so do the guardrails.

Pic taken during my epic commute

No alternative text description for this image

Current mood: "Joining a new company is akin to an organ transplant - and you're the new organ. If you're not thoughtful in adapting to the new situation, you could end up being attacked by the organizational immune system and rejected."

Learning fast, exploring the culture, and pushing myself to bring out the best work I’ve ever done. 

Loving the challenge. 🤩

As a leader, your job is to run a tight ship.

That doesn’t mean control. It means precision.

Clear goals. 
Clear roles. 
Clear standards.

In fast-moving teams, especially in AI, ambiguity is expensive and confusion kills momentum.  Alignment compounds it.

Gemini 3 is here and it isn’t just another model release. It’s Google firing on every AI cylinder: infra, research, product, and distribution.

A few things stand out:

1.	Full-stack advantage, finally realized.
Gemini 3 is the first model shipped into Google Search on day one. That’s not just marketing ; it’s how you put frontier AI in front of billions.

2.	The agent era is no longer theoretical.
With Deep Think mode, long context, multimodal understanding, and real-time planning, Gemini 3 moves beyond chat. It’s not a chatbot. It’s a problem-solver.

3.	AI is getting better at “reading the room.”
It reflects a core shift: models are starting to grasp intent and nuance and not just data.

Benchmarks back it up: topping LMArena, solving “Humanity’s Last Exam”, setting a new bar in math and science tasks. But that’s the sideshow.

More info here: https://lnkd.in/gWeXGwhC

I cannot wait to try it out

JSESSIONID

li_at

Talks about #ai, #nocode, #deeplearning, #machinelearning, and #artificialintelligence

building AI systems

𝗔𝗜 𝗪𝗼𝗿𝗹𝗱 𝗠𝗼𝗱𝗲𝗹𝘀 𝟭𝟬𝟭

Current AI is often described as a "statistical correlation machine." It predicts the next word or pixel based on patterns it has seen before. World Models represent a shift toward causal understanding—giving machines a way to simulate the future.

What is a World Model? Think of it as an internal flight simulator. Instead of just recognizing an environment, the AI builds a mental model of how that environment works. It uses this model to test hypotheses ("If I drop this glass, it will break") without needing to actually perform the action.

The "𝗟𝗮𝘂𝗻𝗱𝗿𝘆" Example Imagine training a robot to fold clothes.

- 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝗪𝗼𝗿𝗹𝗱 𝗠𝗼𝗱𝗲𝗹: The robot must physically interact with the clothes millions of times, learning through endless trial and error.

- 𝗪𝗶𝘁𝗵 𝗮 𝗪𝗼𝗿𝗹𝗱 𝗠𝗼𝗱𝗲𝗹: The robot simulates the physics of the fabric in its head. It plans the fold internally, predicts the outcome, and only moves its arm when it is confident. This results in safer, faster, and more generalizable learning.

𝗛𝗼𝘄 𝘁𝗵𝗶𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝘀 𝗳𝗿𝗼𝗺 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗟𝗟𝗠𝘀: While Large Language Models (LLMs) are masters of syntax and semantics, they often struggle with physical causality. World Models differ because:

𝟭. 𝗧𝗵𝗲𝘆 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗿𝗲𝗰𝗮𝗹𝗹: They don't just retrieve information; they run mental experiments.

𝟮. 𝗧𝗵𝗲𝘆 𝗹𝗲𝗮𝗿𝗻 𝗹𝗮𝘄𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗿𝘂𝗹𝗲𝘀: They infer the physics of their environment from data rather than being hand-coded.

𝟯. 𝗧𝗵𝗲𝘆 𝗮𝗿𝗲 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲: They predict "what happens next" in a state-space, not just a token-space.

𝗧𝗵𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽: The next frontier is the convergence of these technologies. By combining the reasoning of LLMs with the grounding of World Models, we move closer to Embodied Intelligence—software that can read, watch, plan, and act in the physical world.

Scaling laws in AI are nowhere near finished. The latest Gemini 3 results show it clearly.

Pre-training: Many believed progress had plateaued. Yet the jump from 2.5 to 3.0 is one of the biggest leaps we have seen. When you improve data, architecture, and compute together, you keep breaking ceilings. No walls in sight.

Post-training: This might be the real frontier. We are still early in how we align, steer, and specialize models. The innovation curve here is just beginning, and the gains on 3.0 prove it.

Good insights from Deepmind engineers this week.

Better models are not coming slowly. They are coming faster.

Plan for continuous capability jumps, not incremental upgrades. The disruption curve is steepening.

it is very cool to work everyday with people in person. i think  product innovation and magic really happens with real human interaction

AI is making 10X engineers 1000X, and 1X engineers 0.1X 

stealing this quote from Shadi Copty 🙃

Fortusight, Founder | AI advisor | Ex-Dir of Llama @Meta & Ex-VP of Watson @IBM

AI Evals Explainer

Generated with Nano Banana Pro

It’s insane how many hours I’ve seen senior technical leaders burn on architecture charts. 

All that time spent polishing pixels... now you just describe it in plain English and boom and nano banana AI handles the rest.

By the way, this is a solid, modern and agile AI stack. To build at x10 speed you need to have the right building blocks.

Fortusight's AI stack

A lot of people ask me this question, I'll start with the absolute core:
1. I use Agno for multi-agent framework, they're fast, elegant and robust.
2. Exa for web grounding, I consider Exa a bigger booster than LLM choice for my use case.
3. FMP for premium, clean, reliable financial data turned into tailored tools.

For LLMs, I use O3 for multi-agent orchestration and planning, GPT 4.1 for digesting web content and supporting my A1Facts flow; Flash2.5 for structured extraction and Gemini 3 pro for synthesizing investment thesis.

I constantly progress the choices of LLMs prioritizing quality over price.

For tools, I use Cursor with Opus 4.5 (recently replacing Gemini 2.5 pro); and Figma make - which I absolutely love.

For internal ops agents, (1) managing coverage requests (2) digesting user feedback (3) analyzing product analytics (4) writing my weekly newsletter -- all built with Agno and Gemini 3 pro.

Less exciting but just as critical :-) Streamlit for front end [being replaced with react], and Firebase for database/auth/analytics -- both choices have been really critical for prototyping rapidly with my users.

The "secret sauce" is secret (lol), but I will say solving factuality in evolving market dynamics is a significant effort, and using expert-system techniques and simulations augments LLMs where they are critically weak.

Happy to share more if you have any questions -- and obviously here's the link if you want to try it out (https://lnkd.in/g7eR86dr)

Introducing SAM 3D: Powerful 3D Reconstruction for Physical World Images

and it’s not your typical 3D reconstruction tool.

It does what previous models couldn’t:
- Reconstruct real-world objects and scenes from a single image.
- Handle occlusion, indirect views, and cluttered backgrounds.
- Estimate human pose and shape with surprising accuracy.

Why does this matter?
Because for the first time, we’re seeing 3D perception at the scale, quality, and accessibility of today’s 2D models.

The architecture behind SAM 3D borrows from LLMs: pre-training on synthetic data, followed by post-training on real-world images using a human-in-the-loop ranking engine. The result is a feedback loop that continuously improves both the data and the model.

The implications stretch far beyond creative media. Robotics. E-commerce. Sports medicine. Interactive avatars. You name it.

And the best part? 

It's fast. 
Real-time fast.

Meta’s already using SAM 3D to power a “View in Room” feature in Facebook Marketplace; turning static listings into immersive experiences.

The gap between virtual and physical is shrinking. SAM 3D is a serious leap forward.

Learn more:
- 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁 𝗯𝗹𝗼𝗴: https://lnkd.in/g8w6dvAB
- 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗦𝗔𝗠 𝟯𝗗 𝗢𝗯𝗷𝗲𝗰𝘁𝘀 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/gd_HQE9c
- 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗦𝗔𝗠 𝟯𝗗 𝗕𝗼𝗱𝘆 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/gp3p9jaf
- 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗦𝗔𝗠 𝟯𝗗 𝗢𝗯𝗷𝗲𝗰𝘁𝘀: https://lnkd.in/gfwmcKGK
- 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗦𝗔𝗠 𝟯𝗗 𝗕𝗼𝗱𝘆: https://lnkd.in/g9CPHEXJ
- 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗣𝗹𝗮𝘆𝗴𝗿𝗼𝘂𝗻𝗱: https://lnkd.in/guSDTnU3

A sharp, modern AI tech stack for building a serious startup.

Fortusight from Shadi Copty lays out a blueprint that actually reflects where the industry is going:

multi-agent architectures, real-time grounding, premium data, and a diversified LLM strategy tuned to specific jobs rather than brand loyalty.

1/ Agno for agent frameworks.
2/ Exa for grounding.
3/  FMP for clean financial data.
4/ A portfolio of models (O3, GPT-4.1, Flash2.5, Gemini 3) each picked for its superpower.

Cursor + Opus 4.5, Figma Make, Firebase, and Streamlit/React for fast product loops.

And a layer of expert systems + simulation to fix factuality where LLMs keep failing.

This is the kind of stack that lets a small team punch far above its weight.

🚀

Open article: Financial Analysis App by fortusight.streamlit.app

Exciting project for you all to check! I tried an earlier prototype and it was very impressive

Startup life, here I come :-)

Thrilled to share with you my new adventure, Fortusight - a project I've labored on nights and weekends for the last 2 years.

It's a tool for the serious investor that wants to deeply understand a company before buying and holding for 5-10 years.

It uses AI Agents to read through filings, earning calls, analyst reports, news, press-releases - forms an opinion on a company's competitive position, management's friendliness to shareholders, and creates bull/base/bear scenarios for valuation.

To quote a friendly hedge fund manager "I used to have 5 people doing all of this" - each company is analyzed for 192 different perspectives, with A1Facts to ensure factuality for high stakes decisions.

Here's how you try it: https://lnkd.in/g7eR86dr

Let's go 🚀 🚀 🚀

🚨 Stop scrolling. The math on this is barely real.

Get a $799/mo institutional AI research platform for just $50/mo.

The catch: Only 10 spots open. 

RIAs & Fund Managers—this is a complete no-brainer. 👇

https://fortusight.ai/

I need your help 🙏

I'm looking for design partners for my baby startup that just got pre-seed funded.

If you know a wealth fund manager, a small RIA, a family office that wants to reduce their equity research costs from ~$75K to less than what they pay for starbucks coffee - share this link with them: https://fortusight.ai  

For 10 design partners, I'm giving away Fortusight for $50/month instead of $800/month for a year in exchange for feedback to help guide the product's development.

The beta with our users has been incredibly successful, with recurring usage of our equity deep dives.

This will propel the company forward, and one day I will find ways to repay your kindness - many thanks ❤️

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.

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?

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#AgenticAI #AIAgents #AIArchitecture #LLMOps #AIEngineering

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

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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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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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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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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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?

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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

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

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘁𝗵𝗲 𝗴𝗼𝗮𝗹 𝘄𝗮𝘀𝗻’𝘁 𝘁𝗼 𝗿𝗲𝗮𝗰𝗵 𝘁𝗵𝗲 𝘁𝗼𝗽, 𝗯𝘂𝘁 𝘁𝗼 𝗹𝗶𝘃𝗲 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗳𝘂𝗹𝗹𝗲𝘀𝘁 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹?

Somewhere along the way, many of us learned to measure success by 𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻𝘀, 𝘁𝗶𝘁𝗹𝗲𝘀, 𝗮𝗻𝗱 𝗹𝗮𝗱𝗱𝗲𝗿𝘀.

But the real work begins when we stop chasing the next step and start 𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝗻 𝘄𝗵𝗼 𝘄𝗲 𝗰𝗮𝗻 𝗯𝗲𝗰𝗼𝗺𝗲.

This year I realized that for myself.

I stopped worrying about the next step upward.

I started giving myself permission to 𝗳𝗼𝗹𝗹𝗼𝘄 𝘁𝗵𝗲 𝗶𝗱𝗲𝗮𝘀 𝗮𝗻𝗱 𝗶𝗻𝘀𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻𝘀 that called to me.

Sometimes they felt random.

But somehow they all worked together and 𝗽𝘂𝗹𝗹𝗲𝗱 𝗺𝗲 𝘁𝗼𝘄𝗮𝗿𝗱 𝘁𝗵𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝗜 𝘄𝗮𝘀 𝗺𝗲𝗮𝗻𝘁 𝘁𝗼 𝗴𝗿𝗼𝘄 𝗶𝗻𝘁𝗼.

That one shift 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴.

It pushed me to grow in ways I did not expect.

It forced me to 𝘀𝘁𝗲𝗽 𝗶𝗻𝘁𝗼 𝗺𝗼𝗿𝗲 𝗼𝗳 𝗺𝘆 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹.

Growth looks different when you 𝗰𝗵𝗼𝗼𝘀𝗲 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳.

Potential expands when you take risks on your own path.

Fulfillment shows up when you 𝗹𝗲𝘁 𝘆𝗼𝘂𝗿 𝗹𝗶𝗳𝗲 𝗯𝗲 𝗯𝗶𝗴𝗴𝗲𝗿 𝘁𝗵𝗮𝗻 𝘁𝗵𝗲 𝗹𝗮𝗯𝗲𝗹 the world gives you.

Maybe the real win is 𝗰𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗼𝗳 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝘁𝗵𝗮𝘁 𝗳𝗲𝗲𝗹𝘀 𝘁𝗿𝘂𝗲 𝘁𝗼 𝘆𝗼𝘂.

What did you realize about success this year?

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I am excited to share that I’ve joined Planet Wild as a Nature Investor! 🌿

Planet Wild is a global community of nature enthusiasts stepping up to protect the planet's biodiversity. 🌍 Exactly my type of thing!

The natural world is vanishing at an unbelievable rate. Every day, we are losing 135 plant and animal species. Every. Single. Day. That’s 50,000 species a year!

Sooner or later, this will lead to ecosystem collapse! Unless we do something about it.

So, how is the Planet Wild community making a difference?

Every month, the community crowdfunds global rewilding projects at the front lines to:

🦁 Bring back endangered species 
🌊 Revive ocean and aquatic habitats
🌳 Restore entire landscapes

Everything is documented in videos to showcase what our support really achieves on the ground. 

Here is just one example of how the community finally cracked the code on ocean plastic: 
https://lnkd.in/g2FqxjbD

This is one of the most important things in the world right now, so join me in becoming a Planet Wild Nature Investor! The bigger our community becomes, the greater our impact will be.

#planetwild #biodiversity

🔥 𝗧𝗵𝗶𝘀 𝗺𝗶𝗴𝗵𝘁 𝗯𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗵𝗼𝗻𝗲𝘀𝘁 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗼𝗻 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗵𝗲𝗮𝗿 𝘁𝗵𝗶𝘀 𝘆𝗲𝗮𝗿.

On this episode of the Generative AI Revolution Podcast, Nicholas Renotte, 𝗛𝗲𝗮𝗱 𝗼𝗳 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗔𝗱𝘃𝗼𝗰𝗮𝗰𝘆 𝗮𝘁 𝗜𝗕𝗠, shared how he thinks about agentic systems, the cost realities, the safety gaps, and the patterns that make them work in the enterprise. Now available on 𝗬𝗼𝘂𝗧𝘂𝗯𝗲, 𝗦𝗽𝗼𝘁𝗶𝗳𝘆, and 𝗔𝗽𝗽𝗹𝗲 𝗣𝗼𝗱𝗰𝗮𝘀𝘁𝘀.

𝗔 𝗾𝘂𝗼𝘁𝗲 𝘁𝗵𝗮𝘁 𝘀𝘁𝘂𝗰𝗸 𝘄𝗶𝘁𝗵 𝗺𝗲:
“𝗬𝗼𝘂 𝗮𝗿𝗲 𝘀𝘁𝗶𝗹𝗹 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗲 𝗯𝗲𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝘂𝗻𝗶𝘃𝗲𝗿𝘀𝗲.
𝗬𝗼𝘂𝗿 𝗶𝗱𝗲𝗮𝘀 𝘀𝘁𝗶𝗹𝗹 𝗼𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺 𝗺𝗼𝘀𝘁 𝗟𝗟𝗠𝘀.
𝗗𝗼 𝗻𝗼𝘁 𝗹𝗲𝘁 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗵𝗶𝗻𝗸 𝗳𝗼𝗿 𝘆𝗼𝘂.
𝗛𝘂𝗺𝗮𝗻 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 𝗶𝘀 𝘀𝘁𝗶𝗹𝗹 𝘁𝗵𝗲 𝘀𝘁𝗿𝗼𝗻𝗴𝗲𝘀𝘁 𝗳𝗼𝗿𝗺 𝗼𝗳 𝗶𝗻𝘁𝗲𝗹𝗶𝗴𝗲𝗻𝗰𝗲.”

This conversation is part of a series built on real, unscripted discussions with the people shaping the future of AI.

𝗪𝗵𝗮𝘁 𝘄𝗲 𝗰𝗼𝘃𝗲𝗿𝗲𝗱:
→ “Agents are just LLMs plus tools.”
→ Why cost remains the biggest blocker for enterprise agent adoption.
→ The safety gaps Nicholas sees daily and why oversight still matters.
→ Why tables, policies, and edge cases break LLM reasoning.
→ How Nicholas approaches modular architecture to avoid brittle systems.
→ When frameworks like LangChain, LlamaIndex, Autogen, LangGraph, Small Agents, MCP, or watsonx actually make sense.
→ Why traceability is non-negotiable for enterprise AI.
→ Nicholas’ personal routines: deep work, discipline, curiosity, and staying grounded amid rapid change.

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀:
→ Agentic AI breaks in ways traditional systems never did.
→ The teams who understand governance, cost, and architecture will win.
→ Curiosity and clear thinking matter far more than keeping up with every model release.

Nicholas also shared the long path that led him to IBM. Not luck. Not shortcuts. Consistency and relentless learning. This conversation surprised me, challenged me, and expanded how I think about modern AI. It will do the same for you.

Full breakdown plus YouTube and Spotify links are in the article.

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View newsletter: Agentic AI: News & Use Cases

Featuring: Nicholas Renotte, Head of AI Developer Advocacy, IBM From the moment I met Nicholas Renotte, I knew this episode would be special. Nicholas

Open article: Generative AI Revolution Podcast | Episode 4 | Agentic AI Explained by Jothi Moorthy • 3 min read

Generative AI Revolution Podcast | Episode 4 | Agentic AI Explained

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

Microsoft has released a free AI Agents course for beginners. In just 14 lessons, you will go from building your first agent to deploying production-ready AI agents.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐥𝐞𝐚𝐫𝐧, 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩:
1. AI Agents for Beginners (Full Course) → https://lnkd.in/gxCXp4yH
2. What are AI Agents → https://lnkd.in/gxCXp4yH
3. Which AI Agent Framework to Use → https://lnkd.in/gBdGunhS
4. How to Design Good AI Agents → https://lnkd.in/g3k3mfpv
5. What Is the Agent Tool Use Design Pattern? → https://lnkd.in/gF3dSRb5
6. What Is agentic RAG? → https://lnkd.in/geZmpmpq
7. How to build effective AI agents → https://lnkd.in/gJJaxYaf
8. What Is the AI Agent Planning Design Pattern? → https://lnkd.in/gR_zd6-p
9. How to use a multi-AI agent system → https://lnkd.in/gS_BjiTm
10. How can AI agents improve? (Metacognition) → https://lnkd.in/gGmRKF3X
11. How to deploy AI agents into production → https://lnkd.in/gWBPBsCU
12. Using Agentic Protocols (MCP, A2A, and NLWeb) → https://lnkd.in/gq3x54Gb
13. Context engineering for AI agents → https://lnkd.in/gkQURFhN
14. AI Agent Memory: Building Self-Improving Agents → https://lnkd.in/gnknd2hY

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