AI Breakthroughs & Security Insights

Welcome to another edition of the NexaQuanta newsletter, where we bring you the latest insights into cutting-edge AI breakthroughs and enterprise technologies.

This week, we’re exploring transformative innovations from industry leaders like IBM, Meta, and Google.

Stay ahead of the curve with updates on AI advancements that are shaping the future of computing, cybersecurity, and multi-agent systems.

In this edition, we explore IBM’s BeeAI platform, a revolutionary tool that facilitates seamless multi-agent AI collaboration, and the powerful capabilities of IBM z17, an AI-powered mainframe designed for real-time decision-making and enhanced security.

We also highlight IBM’s tutorial on building private AI research agents using Granite 3.2 and Meta’s release of the Llama 4 series, which sets a new standard in open multimodal AI. Finally, we delve into Google’s Sec-Gemini v1, an experimental AI model built to enhance cybersecurity defenses, offering a promising solution for today’s complex security challenges.

Welcome to This Week’s NexaQuanta AI & Tech Digest

IBM’s BeeAI Platform Makes Multi-Agent AI Collaboration Easier Than Ever

IBM Research has unveiled a new version of BeeAI, an experimental platform designed to make it easier for developers to run and connect multiple AI agents, regardless of which framework or programming language they’re built in.

A New Era of Multi-Agent Systems

Inspired by the idea that “two heads are better than one,” BeeAI allows developers to combine several open-source AI agents to work independently or as a team. This shift opens the door to building smarter, more collaborative systems.

One of the most significant improvements is the introduction of ACP (Agent Communication Protocol)—a universal way for agents to talk to each other. This protocol solves a considerable challenge: inconsistent interfaces between agents. ACP allows different agents to exchange information smoothly and work together across platforms.

“ACP will act like a universal connector,” said Kate Blair, Director of Product Incubation at IBM Research. “It provides a standardized way for agents to interact with each other and external systems.”

Developer-Focused Redesign

Originally aimed at business users, BeeAI has been redesigned to focus on developers. Its updated goal is to make finding, connecting, and managing agents simple, no matter what tools or languages they use.

With just two commands in the command line or a click in the UI, developers can try out popular agents like:

Aider – a coding assistant that edits code collaboratively
GPT-Researcher – an agent that collects research and citations
A podcast agent – turns research into structured podcast scripts for AI-based text-to-speech systems.

Built on Open Standards

BeeAI’s protocol is based on Anthropic’s Model Communication Protocol (MCP), which sets rules for how agents connect to tools and data.

With ACP, IBM has taken it further, enabling agent discovery, collaboration, and task-sharing. Although ACP is still in the pre-alpha stage, IBM is actively engaging the open-source community to build it into a universal standard.

At the AI Dev 25 conference in San Francisco—hosted by Andrew Ng—IBM’s Kate Blair and Ismael Faro, a distinguished engineer at IBM Research, presented BeeAI’s latest features. Faro invited developers to help shape the future of ACP.

For more details, feel free to visit this link.

Meet IBM z17: AI-Powered Mainframe for a Smarter Future

IBM has introduced IBM z17, a next-generation mainframe built with AI. From processing trillions of operations per second to protecting against cyber threats, z17 is designed to push the limits of enterprise computing performance, security, and innovation.

Built with AI in Every Layer

Powered by the IBM Telum II processor, IBM z17 brings AI capabilities directly into hardware, software, and system operations. This full-stack AI approach enables real-time decision-making close to where the data resides—for analyzing medical images, reducing loan risks, or preventing retail fraud.

AI That Works in Real Time

With the ability to process up to 24 trillion operations per second, IBM z17 uses large language models (LLMs) to apply AI predictions directly to mainframe transactions. This real-time intelligence can fuel growth, boost accuracy, and drive meaningful industry outcomes.

Security at the Core

With advanced AI-powered security tools, IBM z17 helps enterprises stay ahead of threats and is part of the world’s first series of quantum-safe systems. Businesses can simplify compliance, protect critical data, and accelerate their path toward post-quantum security—all in one platform.

Smarter Operations Through Automation

With built-in AI insights and automation, z17 empowers IT teams to streamline operations, reduce complexity, and increase productivity. The platform enhances observability and reporting, helping businesses avoid disruptions and stay resilient.

Built for the Hybrid Cloud

IBM z17 is designed to perform in hybrid cloud environments, combining security, speed, and flexibility. Whether you’re handling critical workloads or planning for long-term growth, z17 offers the tools to modernize with confidence.

More of Everything: Benefits at a Glance

More Precision – Real-time AI-powered insights using secure transactional data
More Security – AI-driven protection against evolving threats
More Productivity – Standardized operations through automation and AI tools
More Resiliency – Advanced observability and self-healing features

To learn more, you can explore the product by visiting this link.

IBM Demonstrates How to Build a Private AI Research Agent for Image Analysis Using Granite 3.2

IBM has released a powerful new tutorial showing how to build an AI research agent to analyze images and generate in-depth, structured insights. Using Granite 3.2 Vision and Language Models, developers can now create private, cost-effective research agents on their local machines.

Combining Vision, Language & Reasoning

This solution uses the Granite 3.2 Vision Model with the 8B Granite Language Model, which is known for its strong reasoning abilities. Together, they form an intelligent agent that can examine images, understand context, and generate detailed research outputs based on visual elements and textual instructions.

Built for Local, Secure Use

Everything runs locally, powered by Ollama, Open WebUI, and the Granite model—ensuring user privacy and lower costs. The agent is also built on CrewAI, a framework that supports asynchronous, multi-agent collaboration for handling complex research tasks efficiently.

Smarter Research with RAG

The system uses Retrieval-Augmented Generation (RAG) to enhance the quality of results. It pulls relevant data from the web and user documents in real time, grounding its insights in reliable sources. This helps the AI generate responses that are both contextually accurate and up to date.

What Can the Image Research Agent Do?

From breaking down business dashboards to explaining complex scientific visuals, this agent turns images into structured knowledge. Use cases include:

Architecture Diagrams – Identify components and system structure
Business Dashboards – Interpret KPIs and business metrics
Historical Photos & Artwork – Analyze styles, events, and references
Scientific Charts – Understand datasets, lab results, or research figures

Agent Collaboration in Action

Unlike previous tutorials on sequential task completion, this tutorial shows how AI agents can work in parallel. The primary agent breaks an image into researchable topics and assigns each to a separate sub-agent. Once complete, their findings are merged into a final, easy-to-understand report.

Open-Source & Ready to Use

IBM has shared the complete open-source code in the [IBM Granite Community GitHub repository], making it easy for developers to get started. The setup requires a few steps and uses popular tools like OpenWebUI and Ollama to ensure smooth, local operation.

This tutorial opens new possibilities for industries that rely on visuals—like education, healthcare, and engineering—by enabling faster, more thoughtful decision-making through image-based research.

Click here to read more details.

Meta Launches Llama 4 Series: A New Benchmark in Open Multimodal AI

A New Benchmark in Open Multimodal AI

Meta has unveiled its most advanced open-weight AI models with the Llama 4 series. The release includes robust new offerings—Llama 4 Scout and Llama 4 Maverick—designed to deliver cutting-edge multimodal performance at scale. These models mark a significant leap in accessible, open-source AI for enterprise and developer communities.

Two Flagship Models: Scout and Maverick

At the heart of the Llama 4 series are two high-performing models built on Mixture-of-Experts (MoE) architecture:

Llama 4 Scout (17B parameters): Optimized for efficiency, Scout uses 16 experts and fits within a single NVIDIA H100 GPU, making it ideal for developers looking to build advanced apps without high infrastructure costs.
Llama 4 Maverick (17B parameters, 128 experts): Engineered for high performance, Maverick outperforms leading models like GPT-4o and Gemini 2.0 Flash on key benchmarks including coding, reasoning, and long-context understanding.

Both models are distilled from a larger, unreleased model (Llama 4 Behemoth) and demonstrate state-of-the-art accuracy with significant improvements in cost-effectiveness.

The Power Behind the Models: Llama 4 Behemoth

Though still in training, Llama 4 Behemoth (288B parameters) plays a crucial role in this release. Through a distillation process, it serves as the foundation for Scout and Maverick.

Behemoth has already outperformed major competitors in early benchmarks like GPT-4.5, Claude 3.7 Sonnet, and Gemini 2.0 Pro, particularly in STEM-related tasks and long-context performance.

Meta is expected to release more technical details at LlamaCon on April 29, where Behemoth may take center stage.

True Multimodality, Redefined

A standout feature of the Llama 4 series is its native multimodal architecture. Unlike many models that bolt on vision capabilities after pre-training, Llama 4 uses early fusion—blending text and visual data from the start. This results in superior visual reasoning, image captioning, and cross-modal understanding.

Scout, in particular, can process up to 10 million tokens, a massive context window that allows the model to handle books, documents, and complex datasets easily.

Smarter Post-Training, Smarter AI

Meta has introduced a refined post-training strategy combining:

Lightweight supervised fine-tuning
Online reinforcement learning (RL)
Direct preference optimization (DPO)

Instead of traditional methods, Meta filtered out “easy” examples to focus on harder, high-impact prompts—boosting model robustness, especially in reasoning, multilingual interactions, and real-world image understanding.

Open Access and Deployment Options

In line with its open-source approach, Meta has made Llama 4 Scout and Maverick freely available on:

llama.com
Hugging Face
Meta AI products like WhatsApp, Messenger, Instagram Direct, and the Meta.ai website

Developers and enterprises can immediately begin building multimodal applications with strong foundational capabilities.

Check more details by visiting this page.

Google Introduces Sec-Gemini v1

A Powerful New AI Model to Strengthen Cybersecurity Defenses

On April 4, Google’s Sec-Gemini team introduced Sec-Gemini v1, an experimental AI model explicitly engineered for cybersecurity operations.

This cutting-edge model is designed to shift the cybersecurity balance in favor of defenders by dramatically enhancing threat detection and analysis workflows.

Addressing the Defender’s Dilemma

Cybersecurity experts have long faced an asymmetric challenge: defenders must secure everything, while attackers need only one vulnerability to succeed. Google aims to address this imbalance with Sec-Gemini v1 by force-multiplying cybersecurity professionals’ efforts through AI.

Unlike traditional tools, Sec-Gemini v1 combines Gemini’s state-of-the-art reasoning capabilities with real-time cybersecurity intelligence to enhance SecOps workflows. From root cause analysis to threat and vulnerability assessments, this model delivers speed and depth.

Built on Gemini, Powered by Google Threat Intelligence

Sec-Gemini v1’s advantage lies in its deep integration with Google Threat Intelligence (GTI), the Open Source Vulnerability (OSV) database, and other proprietary sources. This enables it to outperform other models across essential cybersecurity benchmarks:

CTI-MCQ (Threat Intelligence benchmark): Outperforms peers by 11%, demonstrating superior threat actor recognition and contextual awareness.
CTI-RCM (Root Cause Mapping): This model outperforms other models by 10.5%. It accurately identifies root causes of vulnerability and classifies them using the CWE taxonomy.

Real-World Performance: Understanding Complex Threats

Sec-Gemini v1’s capabilities shine in practical scenarios. When queried about Salt Typhoon, a sophisticated threat actor, the model correctly identifies the entity and provides a detailed description—something not all models can do.

It also links vulnerabilities with active threat actors by merging OSV data and Mandiant threat intelligence. This contextual mapping helps analysts evaluate threats and vulnerabilities holistically, reducing response time and improving strategic decision-making.

Free Access for the Cybersecurity Community

To foster innovation and collective security, Google is making Sec-Gemini v1 available to select organizations, researchers, and NGOs. This move underscores Google’s belief in open collaboration to strengthen global defenses against cyber threats.