Artificial Intelligence

Multi-agents AI: what exactly is it? (Part 1)

But first, why do we need it
Most AIs today still fall into one of two categories:

1- Over-reliant on a single large model → prone to mistakes, loops, and unpredictable behavior.
2- Predefined workflows → more reliable but rigid and hard to scale.

Neither truly enables AI to handle real tasks independently.

Multi-agent AI takes a different approach. Instead of one AI doing everything, multiple specialized agents work together dynamically to complete tasks efficiently.

One might gather information, another analyzes it, and another takes action—they communicate, adjust plans, and track progress, just like a well-coordinated team.

Here’s how it happens/ tech breakdown:

1️⃣ Role Assignment & Task Delegation
At the core of any multi-agent system, there’s usually an Orchestrator Agent (or Coordinator).

This agent is responsible for: Breaking down the task; Deciding which agents are needed; Delegating work based on agent capabilities

2️⃣ Communication & Information Sharing
Agents exchange data through APIs, message passing, or shared memory.

This allows them to:
- Share insights in real time
- Adjust workflows dynamically based on new information

3️⃣ Reflection & Self-Correction
Unlike single-agent AI, multi-agent systems track progress and self-correct using:

- Task Ledgers (tracking what’s been done vs. what’s left)
- Feedback Loops (agents double-check their work)
- Dynamic Replanning (if an approach fails, agents adjust strategy)

4️⃣ Multi-LLM & Specialized AI Models
Instead of using one large LLM for everything, multi-agent AI systems combine:

- A generalist LLM for reasoning and orchestration
- Small fine-tuned models for specialized tasks

5️⃣ Execution & Continuous Learning
Once agents complete a task, multi-agent systems don’t just stop—they learn from each execution to improve performance.

And this isn’t theoretical, it’s already happening. A few examples:

🚗 Tesla’s Full Self-Driving (vision, path planning, and decision-making agents working together)
💰 Goldman Sachs AI Trading (market analysis, risk management, and execution agents)
🔬 Recursion AI in drug discovery (analyzing biological data, predicting drug interactions, and optimizing trials)

Guide to Building an AI Agent

1️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗟𝗟𝗠
Not all LLMs are equal. Pick one that:
- Excels in reasoning benchmarks
- Supports chain-of-thought (CoT) prompting
- Delivers consistent responses

📌 Tip: Experiment with models & fine-tune prompts to enhance reasoning.

2️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗼𝗴𝗶𝗰
Your agent needs a strategy:
- Tool Use: Call tools when needed; otherwise, respond directly.
- Basic Reflection: Generate, critique, and refine responses.
- ReAct: Plan, execute, observe, and iterate.
- Plan-then-Execute: Outline all steps first, then execute.

📌 Choosing the right approach improves reasoning & reliability.

3️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗖𝗼𝗿𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀
Set operational rules:
- How to handle unclear queries? (Ask clarifying questions)
- When to use external tools?
- Formatting rules? (Markdown, JSON, etc.)
- Interaction style?

📌 Clear system prompts shape agent behavior.

4️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆
LLMs forget past interactions. Memory strategies:
- Sliding Window: Retain recent turns, discard old ones.
- Summarized Memory: Condense key points for recall.
- Long-Term Memory: Store user preferences for personalization.

📌 Example: A financial AI recalls risk tolerance from past chats.

5️⃣ 𝗘𝗾𝘂𝗶𝗽 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗧𝗼𝗼𝗹𝘀 & 𝗔𝗣𝗜𝘀
Extend capabilities with external tools:
- Name: Clear, intuitive (e.g., "StockPriceRetriever")
- Description: What does it do?
- Schemas: Define input/output formats
- Error Handling: How to manage failures?

📌 Example: A support AI retrieves order details via CRM API.

6️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗥𝗼𝗹𝗲 & 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀
Narrowly defined agents perform better. Clarify:
- Mission: (e.g., "I analyze datasets for insights.")
- Key Tasks: (Summarizing, visualizing, analyzing)
- Limitations: ("I don’t offer legal advice.")

📌 Example: A financial AI focuses on finance, not general knowledge.

7️⃣ 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗥𝗮𝘄 𝗟𝗟𝗠 𝗢𝘂𝘁𝗽𝘂𝘁𝘀
Post-process responses for structure & accuracy:
- Convert AI output to structured formats (JSON, tables)
- Validate correctness before user delivery
- Ensure correct tool execution

📌 Example: A financial AI converts extracted data into JSON.

8️⃣ 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱)
For complex workflows:
- Info Sharing: What context is passed between agents?
- Error Handling: What if one agent fails?
- State Management: How to pause/resume tasks?

📌 Example:
1️⃣ One agent fetches data
2️⃣ Another summarizes
3️⃣ A third generates a report

Master the fundamentals, experiment, and refine and.. now go build something amazing! (Written by : Armand Ruiz)

𝗗𝗲𝗲𝗽𝘀𝗲𝗲𝗸 𝗶𝘀 𝗱𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝗻𝗴 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝗮𝗻𝗱 𝗰𝗼𝘀𝘁 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀𝗅 🤯
𝗖𝗼𝗺𝗽𝗶𝗹𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗣𝗼𝘀𝘁𝘀 𝗼𝗻 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸𝗅

𝗥𝗲𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝗳𝗿𝗼𝗺 𝗴𝗹𝗼𝗯𝗮𝗹 𝗔𝗜 𝗘𝘅𝗽𝗲𝗿𝘁𝘀-
Yann LeCun - To people who see the performance of DeepSeek and think: "China is surpassing the US in AI."
You are reading this wrong.
The correct reading is: "Open source models are surpassing proprietary ones."

Andrew Ng "Today's "DeepSeek selloff" in the stock market -- attributed to DeepSeek V3/R1 disrupting the tech ecosystem -- is another sign that the application layer is a great place to be. The foundation model layer being hyper-competitive is great for people building applications."

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗱𝗲𝗲𝗽𝘀𝗲𝗲𝗸? 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗬𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄-
DeepSeek is a Chinese AI startup that has rapidly emerged as a disruptive force in the global artificial intelligence landscape. Founded in July 2023 by Liang Wenfeng (also transliterated as Li Wenf), a Zhejiang University graduate and hedge fund manager, the company developed an open-source large language model (LLM) that rivals leading U.S. models like OpenAI's GPT-4 at a fraction of the cost135. - from Perplexity

𝗞𝗲𝘆 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀:
𝗖𝗼𝘀𝘁 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: The Development and training cost of deepseek is under 6 Million USD, where as OpenAI and Gemini takes tens of millions of dollars.

𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Independent benchmark tests show DeepSeek models outperforming ChatGPT-4 in mathematics, programming, and reasoning tasks.

𝗪𝗵𝘆 𝘁𝗵𝗲 𝗨𝗦 𝗦𝘁𝗼𝗰𝗸 𝗠𝗮𝗿𝗸𝗲𝘁𝘀 𝗮𝗿𝗲 𝗰𝗿𝗮𝘀𝗵𝗶𝗻𝗴:
The DeepSeek was developed using open source models - Llama from Meta, by coming up with new ideas and built them on top of other people's work despite significant restrictions on Hardware from USA. They developed these models with lower end GPU's demonstrating that unlimited hardware is not the solution. By proving high-performance models can be built cheaply and openly, it pressures Western firms to justify their massive investments while offering developing nations an accessible AI alternative.

𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸 𝗘𝘅𝗽𝗮𝗻𝗱𝘀 𝗔𝗜 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝘄𝗶𝘁𝗵 𝗝𝗮𝗻𝘂𝘀 𝗣𝗿𝗼-7𝗕 - Chinese AI firm DeepSeek releases new open-source multimodal model Janus Pro-7B on Hugging Face, claiming performance matching specialized models like DALL-E 3. Link: https://seekingalpha.com/news/4398945-deepseek-releases-open-source-ai-multimodal-model-janus-pro-7b

𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸 𝗗𝗶𝘀𝗿𝘂𝗽𝘁𝘀 𝗔𝗜 𝗠𝗮𝗿𝗸𝗲𝘁 𝘄𝗶𝘁𝗵 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗠𝗼𝗱𝗲𝗹 - DeepSeek's R1 model demonstrates efficient AI development, matching top performers while using fewer resources and lower costs. Link: https://venturebeat.com/ai/deepseek-r1s-bold-bet-on-reinforcement-learning-how-it-outpaced-openai-at-3-of-the-cost/

In 1994, people told me programming was for nerds and that I should become a doctor or a lawyer instead.

10 years later, they told me that someone from India would take my job for $5/hour.

Then, no code was going to doom my career.

In 2021, Codex, then Copilot, then ChatGPT, then Devin, then OpenAI o1...

People keep yelling that "Programming is Dead," and yet the demand for good Software Engineers has never been higher.

Stop listening to midwit people. Learn to build good software, and you'll be okay.

Prediction on AI in 2025 🚀

1. Major Acquisitions: Anthropic (Amazon), Mistral (Meta), and Cohere (Google).

2. Autonomous AI Agents: Self-directed AI tools capable of managing complex workflows, from customer service to project management, with minimal human oversight.

3. Generative AI 2.0: Advances in generative models will produce even more realistic images, videos, and text, powering creative industries and personalized content.

4. Industry Shifts: Smaller AI firms may fold; only OpenAI and XAI likely to remain independent.

5. AI Integration: Deeper embedding in businesses, consumer electronics, and robotics.

6. Rest like SSI (from Ilya) etc will simply fold like Inflection AI etc.

7. Only OpenAI and XAI may remain as independent companies.

8. Mainstream Adoption of AI Agents.

9. Proliferation of Specialized Large Language Models.

I struggled with Data Science interviews until...

I followed this roadmap:

𝗣𝘆𝘁𝗵𝗼𝗻
👉🏼 Master the basics: syntax, loops, functions, and data structures (lists, dictionaries, sets, tuples)
👉🏼 Learn Pandas & NumPy for data manipulation
👉🏼 Matplotlib & Seaborn for data visualization

𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆
👉🏼 Descriptive statistics: mean, median, mode, standard deviation
👉🏼 Probability theory: distributions, Bayes' theorem, conditional probability
👉🏼 Hypothesis testing & A/B testing

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
👉🏼 Supervised vs. unsupervised learning
👉🏼 Key algorithms: Linear & Logistic Regression, Decision Trees, Random Forest, KNN, SVM
👉🏼 Model evaluation metrics: accuracy, precision, recall, F1 score, ROC-AUC
👉🏼 Cross-validation & hyperparameter tuning

𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
👉🏼 Neural Networks & their architecture
👉🏼 Working with Keras & TensorFlow/PyTorch
👉🏼 CNNs for image data and RNNs for sequence data

𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
👉🏼 Handling missing data, outliers, and data scaling
👉🏼 Feature selection techniques (e.g., correlation, mutual information)

𝗡𝗟𝗣 (𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴)
👉🏼 Tokenization, stemming, lemmatization
👉🏼 Bag-of-Words, TF-IDF
👉🏼 Sentiment analysis & topic modeling

𝗖𝗹𝗼𝘂𝗱 𝗮𝗻𝗱 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮
👉🏼 Understanding cloud services (AWS, GCP, Azure) for data storage & computing
👉🏼 Working with distributed data using Spark
👉🏼 SQL for querying large datasets

Don’t get overwhelmed by the breadth of topics. Start small—master one concept, then move to the next. 📈

You’ve got this! 💪🏼

🚀 The Reality of Artificial Intelligence in the Real World 🌍

When people hear about Artificial Intelligence, their minds often jump to flashy concepts like LLMs, transformers, or advanced AI agents. But here’s the kicker: 90% of real-world ML solutions revolve around tabular data! 📊

Yes, you heard that right. The bread and butter of Ai and machine learning in industries like healthcare, finance, logistics, and e-commerce is structured, tabular data. These datasets drive critical decisions, from predicting customer churn to optimizing supply chains.

📌 What You should Focus in Tabular Data?

1️⃣ Feature Engineering: Mastering this art can make or break a model. Understanding your data and creating meaningful features can give you an edge over even the fanciest models. 🛠️
2️⃣ Tree-Based Models: Algorithms like XGBoost, LightGBM, and Random Forest dominate here. They’re powerful, interpretable, and remarkably efficient for tabular datasets. 🌳🔥
3️⃣ Job-Ready Skills: Companies prioritize practical solutions over buzzwords. Learning to solve real-world problems with tabular data makes you a sought-after professional. 💼✨

💡 Takeaway: Before chasing the latest ML trends, invest time in understanding and building solutions for tabular data. It’s not just foundational—it’s the key to unlocking countless opportunities in the industry.

🌟 Remember, the simplest solutions often have the greatest impact. Don't overlook the power of tabular data in shaping the AI-driven world we live in!

💡 While OpenAI launched ChatGPT Pro for 💲200/month, Meta quietly dropped Llama 3.3—a 70B open-source model. 🔥

👨‍💻 Mark Zuckerberg is actually pushing open-source tech like no other big AI player. The Llama 3.3 model runs locally on developer workstations, offers 🚀 class-leading performance, and costs a fraction of what competitors charge for inference.

🌟 Meanwhile, Google released a new model too: Gemini-Exp-1206, now ranked 🥇 in the Chatbot Arena for creative writing, coding, and tackling hard prompts.

🖥 Microsoft also launched Copilot Vision in Edge, an AI that can 👀 see and interact with the browser in real-time using your 🗣 voice. It helps you navigate, analyze web content, and multitask more easily while browsing.

Oh, and X/Twitter’s Grok is now free too—for 10 messages every 2 hours and 4 images/day. 🎉

🤖 This industry is still finding its footing. The competition is fierce, but it’s exciting to watch 🌟 innovation unfold daily.

⚡️ OpenAI, for instance, is rumored to roll out more updates to Pro users leading up to Christmas. 🎄 Maybe that 💲200 will make sense soon.

Another crazy week in AI 🤯

1.OpenAI’s Sora video model was allegedly leaked.

2.Alibaba launches QwQ-32 model, it beats OpenAI's o1

3.Runway Expand Runner H AI agent

4.With Hume, now control a computer with voice.

5.Claude 2 major feature release Styles and MCP

6.Qwen realsed reasoning models.

7.RenderNet’s new Video Anyone feature lets you create consistent character videos.

8.OpenAI has expanded the list of apps it can work with.

OpenAI and others seek new paths to smarter AI as current methods hit limitations

As the AI world pushes boundaries, companies like OpenAI are hitting unexpected roadblocks with large language models (LLMs). The days of "bigger is better" seem to be waning, and a new chapter in AI innovation is unfolding.

Here’s what’s happening:

1️⃣ Scaling Challenges: The mantra of scaling up pre-training—using vast datasets and computing power—is reaching its limits. Even pioneers like Ilya Sutskever acknowledge the plateau.

2️⃣ Resource Race: Training cutting-edge models is increasingly complex, demanding millions of dollars, massive energy, and specialized chips. Power shortages and data scarcity are slowing progress.

3️⃣ A New Approach: AI labs are exploring human-like thinking for algorithms, focusing on quality over sheer scale. This shift could redefine the AI arms race.

The next breakthrough isn’t just about size—it’s about Smart, sustainable innovation.

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