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 ❤️

Helping Leaders Adopt Gen AI with Clarity | AI Evangelist | AI x Transformation | Ex-Big 4 | Perplexity Fellow | 14K+ Community Builder

Note: Everything I share here reflects my personal views, not those of my employer or its affiliated brands.

AI isn’t optional anymore. It’s the new age for how leaders think, work, and build.

I help business and non-technical leaders cut through the noise, understand what AI can actually do for them, and turn it into something real: faster workflows, stronger teams, and smarter decision-making.

My work sits at the intersection of AI strategy, hands-on implementation, and workforce reinvention. I guide leaders through the shift from traditional processes to AI-infused ways of working, whether that means automating routine tasks, redesigning roles, or exploring the new wave of agentic AI systems that can take on more complex work.

Over the years, I’ve partnered with executives across industries to modernize operations, simplify tech stacks, and build solutions that are practical, scalable, and aligned with real business goals, not hype.

What I focus on today:
• Helping leaders adopt Gen AI with clarity
• Infusing AI into teams and processes so people work smarter
• Exploring agentic AI to unlock new capabilities
• Turning early ideas into measurable business impact

My goal is simple: make AI useful, understandable, and actionable for anyone, regardless of their technical background, and help organizations thrive as the world shifts around them.

Prem N.

Google is building one of the most diverse and powerful AI ecosystems — covering everything from image generation and UI design to personal research assistants and AI-driven development tools.

Here's the breakdown of the key products shaping Google’s next wave of AI innovation and how each fits into the broader ecosystem.

Here’s what each tool brings to the table:

1. Gemini Gems:
Custom AI assistants fine-tuned for personal goals, workflows, and business needs.

2. NotebookLM:
An AI research assistant that organizes, summarizes, and queries your personal documents.

3. Google Pomelli:
AI-powered marketing tool that creates brand-consistent social media content for businesses.

4. Google Stitch:
A design-focused AI that converts text prompts into exportable UI layouts.

5. Google AI Studio:
A web-based IDE for prototyping apps using Google’s generative models — built for developers.

6. Google Opal:
A no-code tool to build and share lightweight AI-powered micro-apps instantly.

7. Google Whisk:
An experimental AI from Labs for image remixing and creative visual transformations.

8. Google Imagen 3:
Ultra-realistic text-to-image generation with exceptional typography and fine-detail rendering.

9. Nano Banana:
Google DeepMind’s model for high-quality image creation and editing.

10. Google Lumiere:
An advanced video generation model creating realistic, coherent footage directly from text input.

Google’s AI ecosystem isn’t just a collection of tools, it’s a full stack of creativity, productivity, research, and development capabilities. As these systems integrate more deeply, they will redefine how businesses build, design, create, and automate in the AI-first world.

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Context engineering is becoming one of the most important skillsets in the AI era — because the quality of an AI system’s output depends entirely on the quality of context it receives.

This framework breaks down the six pillars that shape how AI understands, reasons, retrieves, and responds with accuracy and relevance.

Here’s what each component contributes:

🔹 Prompt Techniques
- Tree of Thoughts (ToT): Helps the model explore multiple reasoning paths and choose the optimal answer.
- ReAct Prompting: Blends reasoning (“think”) with action (“act”), allowing the model to use tools, gather data, and refine responses iteratively.

🔹 Memory
- Short-Term Memory: The model’s immediate context window — everything currently “in view” that shapes its next step.
- Long-Term Memory (ReAct-based): External vector storage that allows AI to remember past interactions, facts, and patterns over time.

🔹 Retrieval
Retrieval pipelines break queries into chunks, embed them, fetch relevant knowledge from vector stores, and feed enriched context back into the LLM for more accurate generation.

🔹 Query Augmentation
LLMs rewrite vague or incomplete queries into precise, structured prompts — enabling better problem-solving and more accurate output.

🔹 Agents
AI agents reason step-by-step, use tools adaptively, access memory, decompose problems, and switch strategies dynamically when one approach fails.

🔹 Tools
External tools expand an AI system’s capabilities — enabling database queries, API calls, file operations, search, and multi-step workflows through structured integrations.

Context engineering is the hidden layer that transforms AI from a simple text generator into a reliable reasoning engine. When these six components work together — prompting, memory, retrieval, augmentation, agents, and tools — AI systems become dramatically smarter, more accurate, and far more capable of solving real-world problems.

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𝐀𝐠𝐞𝐧𝐭-𝐭𝐨-𝐀𝐠𝐞𝐧𝐭 (𝐀𝟐𝐀) 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐌𝐨𝐝𝐞𝐥 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 (𝐌𝐂𝐏) 𝐚𝐫𝐞 𝐪𝐮𝐢𝐜𝐤𝐥𝐲 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐭𝐡𝐞 𝐛𝐚𝐜𝐤𝐛𝐨𝐧𝐞 𝐨𝐟 𝐡𝐨𝐰 𝐦𝐨𝐝𝐞𝐫𝐧 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐞 — 𝐚𝐧𝐝 𝐭𝐡𝐢𝐬 𝐝𝐢𝐚𝐠𝐫𝐚𝐦 𝐬𝐡𝐨𝐰𝐬 𝐞𝐱𝐚𝐜𝐭𝐥𝐲 𝐡𝐨𝐰 𝐭𝐡𝐞 𝐭𝐰𝐨 𝐰𝐨𝐫𝐤 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫.
Instead of one AI model trying to do everything, A2A and MCP allow multiple specialized agents to coordinate, share context, and execute tasks using the right tools at the right time.

𝐇𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐛𝐫𝐨𝐤𝐞𝐧 𝐝𝐨𝐰𝐧:

𝟏. 𝐓𝐡𝐞 𝐔𝐬𝐞𝐫 𝐓𝐚𝐥𝐤𝐬 𝐭𝐨 𝐚 𝐑𝐨𝐮𝐭𝐞𝐫 𝐀𝐠𝐞𝐧𝐭
The user interacts with a single Client/Router Agent, which interprets the request and decides which specialized agent should handle it.

𝟐. 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐀𝐠𝐞𝐧𝐭𝐬 (𝐀𝟐𝐀) 𝐓𝐚𝐤𝐞 𝐎𝐯𝐞𝐫
The router forwards the task to the right expert agent — each designed for a specific job.
These A2A agents communicate seamlessly, passing context and coordinating actions without the user managing anything manually.

𝟑. 𝐀𝐠𝐞𝐧𝐭𝐬 𝐔𝐬𝐞 𝐓𝐨𝐨𝐥𝐬 𝐯𝐢𝐚 𝐌𝐂𝐏
Each specialized agent connects to the appropriate MCP server, gaining access to structured tools, APIs, and capabilities such as:
- Tool X, Tool Y (from API Hub Z)
- Tool X, Tool Y (from MCP Server X)
- Tool 1, Tool 2 (from MCP Server Y)
MCP standardizes how tools are discovered, connected, and executed — making it easy for agents to find the right capability instantly.

𝟒. 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 𝐋𝐢𝐤𝐞 𝐜𝐫𝐞𝐰𝐀𝐈, 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧, 𝐚𝐧𝐝 𝐎𝐭𝐡𝐞𝐫𝐬 𝐏𝐨𝐰𝐞𝐫 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐀𝐠𝐞𝐧𝐭𝐬
- crewAI → Specialized Agent A
- LangChain → Specialized Agent B
- Other frameworks → Specialized Agent C
This lets teams mix and match tools, agent frameworks, and servers freely.

The Big Picture
- A2A handles intelligent collaboration between agents.
- MCP handles standardized access to tools and APIs.

Together, they create a powerful AI ecosystem where:
- The user makes one request
- The router assigns tasks
- Specialized agents solve problems
- MCP connects them to the tools needed
- The system delivers accurate, multi-step results

This is the future of agent orchestration - modular, interoperable, and tool-aware AI systems that work together like a digital team.

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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);}Armand Ruiz's LinkedIn Performance

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