What is today’s beat?

In today’s releases we have some hot news!
🧨 Llama Index launched Agentic Document Worflows
🧨 Cohere North - agents for enterprise with high security constraints.
🧨 Microsoft Phi-4 is open source, and available for all

Today’s tutorial takes you through 5 steps to understand AI agents from a developer’s perspective, and finishes with a simple implementation in python using OpenAI GPT-4o.

In the community, we look at a few useful tools for your development team, upcoming events, and a last call to submit to Reddot’s 2025 design award submission.

Your FREE newsletter
 share
to show support

🎯 RELEASES 🎯

Llama Index
Launch: Agentic Document Workflows

Synopsis

LlamaIndex has introduced Agentic Document Workflows, a significant improvement as it maintains state across reasoning steps. This release expands the capabilities of LlamaIndex, a platform renowned for its role in integrating and managing unstructured data for retrieval-augmented generation (RAG) pipelines.

Core Observations

1. Introduction to LlamaIndex and Agentic Document Workflows:

   • includes pre-built agents, and workflow guideline templates to get you started quicker

   • standard features like tooling out of the box

2. Integrations:

   • Llamaparse integrates with popular data storage solutions (e.g., AWS S3, Google Drive) and collaborative platforms (e.g., Notion, Slack).

3. Ecosystem and Enhanced Value for Users:

   • the framework is packaged as a python pip or javascript npm

Broader Context

LlamaIndex addresses a critical gap in AI applications: the ability to make complex, context-aware decisions in real time across common platforms like Google Drive and S3. The Agentic feature adds value and ease to its existing RAG pipelines. It’s pretty easy to get going, and the docs are great.

Cohere
North - A secure AI workspace to get more done

Synopsis

Cohere, a leader in language model development for enterprise, has launched "North," a platform designed to adhere to strict security regulations needed for large scale enterprise AI - like government, education or Department of Defense. It is a direct competitor to Microsoft Copilot and Google Vertex AI Agent Builder.

Core Observations

1. Introduction to Cohere and North:

   • Cohere specializes in developing advanced large language models (LLMs) for text processing, retrieval-augmented generation, and various AI-driven applications.

   • The North platform provides a secure AI environment, enabling enterprises to deploy, customize, and manage LLMs without compromising data privacy.

2. Performance Metrics and Features:

   • North integrates Cohere's Command R7B model, the smallest and fastest in the R series, offering a 128K context window.

   • optimized for high throughput and low latency, and tuned real-time AI applications such as chatbots and code assistants.

3. Ecosystem and Partner Integrations:

   • Cohere's ecosystem includes partnerships with major cloud providers, ensuring deployment flexibility across cloud, hybrid, and on-premises infrastructures. (Google, Oracle, Microsoft, MongoDB and the list goes on)

Broader Context

Cohere’s North platform is targeting large scale enterprise AI as a platform and software as a service. By focusing on data privacy and flexible deployment, North offers a practical solution for businesses to integrate AI into their processes without risking sensitive information or the difficulty of implementing it.

Apply to the early access program here

Microsoft
Phi-4 - Open Source & Outperforms OpenAI-o1

Synopsis

Microsoft has released the Phi-4 model as an open-source AI on Hugging Face with an MIT license. It showcases significant advancements in small-model efficiency and mathematical reasoning and used the OpenAI’s Simple Eval benchmark to set its score. This YouTube short is how most feel about Microsoft’s contribution to AI, but after many online requests Microsoft has shown a change of heart.

Core Observations

1. Phi-4 Model Overview:

   • Phi-4 is a 14-billion parameter, dense decoder-only Transformer model developed by Microsoft Research. It was trained on 9.8 trillion tokens, utilizing 1,920 H100-80G GPUs over 21 days.

2. Performance and Efficiency:

   • Phi-4 achieves superior mathematical reasoning capabilities, outperforming larger models like Google's Gemini Pro on benchmarks such as the November 2024 AMC 10/12 tests.

3. Integration with Hugging Face:

   • Phi-4 is fully open-source and available on Hugging Face under the MIT License, providing researchers and developers with access to its weights and architecture for experimentation and deployment.

4. Open-Sourcing for Customization:

   • The open-source nature of Phi-4 allows users to fine-tune the model for specific tasks, promoting innovation and adaptability across various applications.

5. Complementing Microsoft’s Ecosystem:

   • Phi-4 integrates effectively with Microsoft's Azure platform and other tools, reinforcing its utility in enterprise AI applications requiring cost-efficient, high-performing solutions.

Broader Context

Microsoft's Phi-4 model is a big release for the open source community. By open-sourcing the model, Microsoft actually helps us - wow! Good job Microsoft!

download the model here on HuggingFace

Trending

• xAI Grok now available on Apple Store

• VLC demos real-time AI subtitles with video (CES 2025)

• Alibaba programming assistant has new updates - available as free vscode and jetbrains extensions 

We are 100% free!

And with your support, we create more FREE content!

Please share us with a friend!

⚙️ BUILDERS BYTES ⚙️

What will you learn today?

Today we look at the topic of AI agents, and some of the key areas a developer needs to consider when creating and designing. View this resource here for a quick 5 minute read, or just read below for a quick overview.

Key Takeaways

1. Introduction to LLM Agents
   LLM agents, or Large Language Model agents, are AI systems that integrate advanced language processing with components like planning and memory to perform complex tasks autonomously. For example, GitHub Copilot uses an LLM agent to provide real-time coding suggestions, enhancing developer productivity by reducing the time spent searching for solutions.

   The importance of LLM agents lies in their ability to automate intricate processes, improve efficiency, and adapt to various applications across industries.
   

2. The LLM Agent Framework: This framework consists of several key components:

   • Agent Core: The central decision-making unit that defines the agent's goals, tool usage, planning modules, memory integration, and potentially its persona.

   • Memory Module: Stores internal logs and user interactions, including short-term memory (current thought processes) and long-term memory (historical records over time).

   • Tools: Predefined workflows that enable the agent to perform tasks, such as Retrieval-Augmented Generation (RAG) pipelines, code interpreters, and APIs for internet searches.

   • Planning Module: Employs techniques like task decomposition and reflection to manage complex problem-solving.

3. Multi-Agent Systems (MAS): LLM-based multi-agent systems involve multiple autonomous agents collaborating to tackle complex tasks. These systems can be categorized into:

   • Cooperative Interaction: Agents work together, sharing information to enhance efficiency and decision-making. This includes disordered cooperation (free expression without a set sequence) and ordered cooperation (following specific rules or sequences).

   • Adversarial Interaction: Incorporates game theory concepts where agents engage in debate or argumentation, leading to dynamic strategy adjustments and improved outcomes

4. Real World LLM Agents: An example is BabyAGI, a task-driven autonomous agent designed to perform various tasks across different domains. It utilizes technologies like OpenAI's GPT-4, Pinecone's vector search platform, and the LangChain framework.

5. Evaluating LLM Agents: Assessment involves measuring performance on real-world tasks, considering factors like task completion, efficiency, and adaptability. Benchmarks such as TheAgentCompany provide extensible evaluation frameworks for AI agents interacting with the world similarly to digital workers.

6. Build Your Own Agent:
   Check out a simple implementation of OpenAI GPT4o here.

⭐️ ⭐️⭐️⭐️⭐️
Like these tutorials?
👉️ Star out repo to show support
⭐️⭐️⭐️⭐️⭐️⭐️

Do you have a product in AI and would like to contribute?
👉️ email us: [email protected] 

Is there something you’d like to see in this section?
👉️ share your feedback

Trending

• ModernBert Embed - trained on new ModernBert for embeddings

• bRAG tutorial - use GraphDB and vector store to enhance your LLM

• Langchain STORM tutorial - based on the paper, create a websearch assistant

🤩 COMMUNITY 🤩

TOOLS

• Lecca - Ai Agents and Automation (email, docs, and many more tools)

• Agentic Labs - Software Documentation you can talk to

• Vapi - voice AI for developers

EVENTS

• Langchain Interrupt - announces their first Agentic AI conference in San Fransisco in May

• HumanX Las Vegas March 10-13

• Nvidia GTC 2025, March 17–21, 2025 - the #1 AI conference in the world

• Reddot - product design challenge has call for last submissions by January 17

THANK YOU

Found something cool?
Want something different?

Our Mission at AlphaWise

AlphaWise strives to cultivate a vibrant and informed community of AI enthusiasts, developers, and researchers. Our goal is to share valuable insights into AI, academic research, and software that brings it to life. We focus on bringing you the most relevant content, from groundbreaking research and technical articles to expert opinions to curated community resources. 

Looking to connect with us?

We actively seek to get involved in community with events, talks, and activities. Email us at [email protected] 

Looking to promote your company, product, service, or event?