News from the AI & ML world
DeeperML - #aiagents
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@shellypalmer.com
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References:
Lyzr AI
AI agents are rapidly transforming workflows and development environments, with new tools and platforms emerging to simplify their creation and deployment. Lyzr Agent Studio, integrated with Amazon's Nova models, allows enterprises to build custom AI agents tailored for specific tasks. These agents can be optimized for speed, accuracy, and cost, and deployed securely within the AWS ecosystem. The use of these AI agents are designed to automate tasks, enhance productivity, and provide personalized experiences, streamlining operations across various industries.
Google's Android 16 is built for "agentic AI experiences" throughout the platform, providing developers with tools like Agent Mode and Journeys. These features enable AI agents to perform complex, multi-step tasks and test applications using natural language. The platform also offers improvements like Notification Intelligence and Enhanced Photo Integration, allowing agents to interact with other applications and access photos contextually. This provides a foundation for automation across apps, making the phone a more intelligent coordinator. Phoenix.new has launched remote agent-powered Dev Environments for Elixir, enabling large language models to control Elixir development environments. This development, along with the ongoing efforts to create custom AI agents, highlight the growing interest in AI's potential to automate tasks and enhance productivity. As AI agents become more sophisticated, they will likely play an increasingly important role in various aspects of work and daily life. Flo Crivello, CEO of AI agent platform Lindy, provides a candid deep dive into the current state of AI agents, cutting through hype to reveal what's actually working in production versus what remains challenging. Recommended read:
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Kuldeep Jha@Verdict
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Databricks has unveiled Agent Bricks, a new tool designed to streamline the development and deployment of enterprise AI agents. Built on Databricks' Mosaic AI platform, Agent Bricks automates the optimization and evaluation of these agents, addressing the common challenges that prevent many AI projects from reaching production. The tool utilizes large language models (LLMs) as "judges" to assess the reliability of task-specific agents, eliminating manual processes that are often slow, inconsistent, and difficult to scale. Jonathan Frankle, chief AI scientist of Databricks Inc., described Agent Bricks as a generalization of the best practices and techniques observed across various verticals, reflecting how Databricks believes agents should be built.
Agent Bricks originated from the need of Databricks' customers to effectively evaluate their AI agents. Ensuring reliability involves defining clear criteria and practices for comparing agent performance. According to Frankle, AI's inherent unpredictability makes LLM judges crucial for determining when an agent is functioning correctly. This requires ensuring that the LLM judge understands the intended purpose and measurement criteria, essentially aligning the LLM's judgment with that of a human judge. The goal is to create a scaled reinforcement learning system where judges can train an agent to behave as developers intend, reducing the reliance on manually labeled data. Databricks' new features aim to simplify AI development by using AI to build agents and the pipelines that feed them. Fueled by user feedback, these features include a framework for automating agent building and a no-code interface for creating pipelines for applications. Kevin Petrie, an analyst at BARC U.S., noted that these announcements help Databricks users apply AI and GenAI applications to their proprietary data sets, thereby gaining a competitive advantage. Agent Bricks is currently in beta testing and helps users avoid the trap of "vibe coding" by forcing rigorous testing and evaluation until the model is extremely reliable. Recommended read:
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Kuldeep Jha@Verdict
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Databricks has unveiled Agent Bricks, a no-code AI agent builder designed to streamline the development and deployment of enterprise AI agents. Built on Databricks’ Mosaic AI platform, Agent Bricks aims to address the challenge of AI agents failing to reach production due to slow, inconsistent, and difficult-to-scale manual evaluation processes. The platform allows users to request task-specific agents and then automatically generates a series of large language model (LLM) "judges" to assess the agent's reliability. This automation is intended to optimize and evaluate enterprise AI agents, reducing reliance on manual vibe tracking and improving confidence in production-ready deployments.
Agent Bricks incorporates research-backed innovations, including Test-time Adaptive Optimization (TAO), which enables AI tuning without labeled data. Additionally, the platform generates domain-specific synthetic data, creates task-aware benchmarks, and optimizes the balance between quality and cost without manual intervention. Jonathan Frankle, Chief AI Scientist of Databricks Inc., emphasized that Agent Bricks embodies the best engineering practices, styles, and techniques observed in successful agent development, reflecting Databricks' philosophical approach to building agents that are reliable and effective. The development of Agent Bricks was driven by customer needs to evaluate their agents effectively. Frankle explained that AI's unpredictable nature necessitates LLM judges to evaluate agent performance against defined criteria and practices. Databricks has essentially created scaled reinforcement learning, where judges can train an agent to behave as desired by developers, reducing the reliance on labeled data. Hanlin Tang, Databricks’ Chief Technology Officer of Neural Networks, noted that Agent Bricks aims to give users the confidence to take their AI agents into production. Recommended read:
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@thenewstack.io
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thenewstack.io
, venturebeat.com
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Emerging tools are revolutionizing AI agent development and management. Databricks recently launched Agent Bricks, a no-code AI agent builder, simplifying the creation process. Complementing this, Google's Gemini Agent Network Protocol offers a framework for intelligent collaboration among AI agents, enabling dynamic communication and task distribution. These advancements signify a move toward more accessible and collaborative AI agent ecosystems.
Vanta has introduced an AI agent designed to automate security compliance workflows, promising to save enterprises significant time on policy management and audit preparation. This agent proactively identifies compliance issues, suggests fixes, and takes action while keeping humans in control. By minimizing human error and automating repetitive tasks, Vanta's AI agent allows security teams to focus on higher-value work, addressing the increasing time organizations spend on compliance, particularly as security risks escalate. The Vanta AI Agent addresses critical areas that typically consume hundreds of hours of manual work. It automates policy onboarding by scanning documents, extracting key details, and mapping policies to relevant compliance controls. This eliminates bottlenecks associated with manual control mapping and generates policy change summaries, streamlining annual reviews. The use of Gemini models by Google in the Gemini Agent Network Protocol enables automated task distribution, collaborative problem-solving, and enriched dialogue management, making it ideal for complex data analysis and information validation. Recommended read:
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@learn.aisingapore.org
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AI agents are rapidly transitioning from simple assistants to active participants in enterprise operations. This shift promises to revolutionize workflows and unlock new efficiencies. However, this move towards greater autonomy also introduces significant security concerns, as these agents increasingly handle sensitive data and interact with critical systems. Companies are now grappling with the need to balance the potential benefits of AI agents with the imperative of safeguarding their digital assets.
The Model Context Protocol (MCP) is emerging as a key standard to address these challenges, aiming to provide a secure and scalable framework for deploying AI agents within enterprises. Additionally, the concept of "agentic security" is gaining traction, with companies like Impart Security developing AI-driven solutions to defend against sophisticated cyberattacks. These solutions leverage AI to proactively identify and respond to threats in real-time, offering a more dynamic and adaptive approach to security compared to traditional methods. The complexity of modern digital environments, driven by APIs and microservices, necessitates these advanced security measures. Despite the enthusiasm for AI agents, a recent survey indicates that many organizations are struggling to keep pace with the security implications. A significant percentage of IT professionals express concerns about the growing security risks associated with AI agents, with visibility into agent data access remaining a primary challenge. Many companies lack clear policies for governing AI agent behavior, leading to instances of unauthorized system access and data breaches. This highlights the urgent need for comprehensive security strategies and robust monitoring mechanisms to ensure the safe and responsible deployment of AI agents in the enterprise. Recommended read:
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Jesus Rodriguez@TheSequence
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References:
CustomGPT
, TheSequence
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Advancements in AI agent development are rapidly transforming how organizations access data and automate tasks. Custom AI agents are emerging as a powerful tool, offering domain-specific responses and actions that make interactions more intuitive and effective. These agents are purpose-built, leveraging domain-specific fine-tuning to align with unique operational needs, unlike general AI models that serve broad purposes. Companies are finding that these custom agents handle niche queries and complex workflows with greater precision, leading to significant improvements in efficiency and accuracy.
Custom AI agents enable organizations to access data and automate tasks with tailored responses, making interactions intuitive and effective. Building these agents involves a series of steps, from gathering relevant domain data and defining precise objectives to selecting or fine-tuning a foundation model and designing conversational flows. As you build your agent, you’ll iterate on design, test performance, and refine responses so it meets requirements and adapts to evolving needs. Techniques like semantic indexing and entity recognition ensure the agent understands relationships between concepts, improving its ability to retrieve and process information. Partnering is also allowing companies to Orchestrate large-scale agent training. Reasoning agents are among the most sought-after LLM use cases, automating complex tasks across domains. With Lambda’s 1-Click Clusters and dstack’s orchestration, teams spend less time on setup and more on building. Self-improving agents can rewrite their own code to enhance performance. Built atop frozen foundation models, these agents alternate between self-modification and evaluation, benchmarking candidate agents on real-world coding tasks. Recommended read:
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Justin Westcott,@AI News | VentureBeat
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AI News | VentureBeat
, AI Accelerator Institute
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AI agents are poised to revolutionize how we interact with the internet, moving beyond passive assistants to active participants authorized to act on our behalf. This shift necessitates a redesign of the web, transforming it from a human-centric interface to a machine-native environment optimized for speed, efficiency, and transactional capabilities. The current internet, designed for human eyes and fingers, is proving inefficient for AI, which requires structured data, clear intent, and exposed capabilities to navigate, decide, and transact effectively. This evolution will lead to a web where APIs become the new storefronts, prioritizing verifiable sources and trust over traditional user experience elements.
The development and deployment of AI agents face significant challenges, particularly in ensuring reliability and consistency within defined business processes. Existing agentic frameworks often fall short due to a lack of state, leading to unpredictable behavior and poor adherence to workflows. A recent survey highlighted that only 25% of AI initiatives are live in production, with hallucinations and prompt management being major obstacles. This indicates a need for robust evaluation processes and automated testing pipelines to address these issues, as traditional software QA methods may not fully apply to AI applications. The survey indicated that without robust evaluation, AI agents may not reach production or may not be sustainable long term. An alternative approach, known as process calling, aims to create reliable, process-aware, and easily debuggable conversational agents. This method addresses the limitations of tool calling by incorporating state tracking and structured workflows. Companies achieving success with LLMs are prioritizing robust evaluation and moving beyond simple tool-based interactions. As AI agents become more prevalent, the internet will likely bifurcate into two webs: one designed for humans and another designed for machines. This machine-native web will feature faster protocols, cleaner metadata, and a focus on verifiable sources, ultimately reshaping the architecture of the internet to accommodate AI's unique requirements. Recommended read:
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Alexey Shabanov@TestingCatalog
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Analytics Vidhya
, TestingCatalog
AI agents are rapidly transforming how work gets done by automating and streamlining a variety of workflows. These intelligent systems are designed to handle tasks ranging from managing schedules, emails, and notes, as exemplified by Genspark's new AI Secretary feature, to providing personalized customer engagement in the automotive retail sector, demonstrated by Impel's use of fine-tuned LLMs. The core advantage of agentic AI lies in its capacity for autonomous decision-making and enhanced customer experiences powered by AI-driven solutions. Impel, for instance, optimizes automotive retail customer connections through personalized experiences at every touchpoint, utilizing Sales AI to provide instant responses and maintain engagement during the car-buying journey.
The development of agentic AI extends to the realm of IoT, where these agents are poised to enable autonomous, goal-driven decision-making. This is particularly relevant in smart homes, cities, and industrial systems, where AI agents can proactively address network issues, strengthen security, and improve overall productivity. Agentic AI marks a structural shift from traditional AI, transitioning from task-specific and supervised models to autonomous agents capable of real-time decisions and adaptation. These agents possess memory, autonomy, task awareness, learning, and reasoning abilities, allowing them to operate with minimal human intervention. However, the effectiveness of AI agents hinges on accurate monitoring strategies and their ability to navigate complex tasks. To ensure reliability in real-world scenarios, benchmarks like WebChoreArena are being developed to challenge agents with memory-intensive and reasoning-intensive scenarios. Building robust conversational AI agents also requires overcoming limitations in existing frameworks. The Rasa platform offers an alternative approach through process calling, enabling the creation of reliable, process-aware, and easily debuggable conversational agents. This method addresses issues such as loss of conversational context and poor adherence to business processes, ensuring that AI agents can consistently guide users through predetermined workflows. Recommended read:
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@Salesforce
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References:
Salesforce
The modern workplace is undergoing a significant transformation with the integration of AI agents into daily operations. Organizations are increasingly adopting autonomous AI systems capable of automating entire workflows across various industries. This shift marks the beginning of the "agentic AI era," where intelligent systems can perform complex tasks, make decisions, and interact with systems with minimal human oversight. This evolution requires organizational leaders to strategically plan for AI agent integration and implementation to maximize efficiency and productivity.
This new collaborative workforce sees humans and AI agents working together, fundamentally changing roles, workflows, and strategies. The contact center is a prime example, moving away from a "bot vs. human" approach to a "bot with human" model. In this landscape, human agents become orchestrators of complex customer journeys, while AI agents act as autonomous copilots, taking initiative on routine tasks and deferring to humans when emotional intelligence or nuanced understanding is required. This collaborative approach aims to improve speed, operational throughput, and decision quality. Companies such as Cisco are building the infrastructure to support this AI-driven future, where potentially billions of AI agents will work together globally and continuously. This includes developing systems where AI agents can independently handle tasks such as identity verification, multi-step backend actions, and even proactive customer engagement. However, successful integration requires careful consideration of data integration, system compatibility, governance, compliance, and change management to ensure AI agents operate within predefined boundaries and judgment frameworks. Recommended read:
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@orases.com
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AI agents are rapidly transforming industries by automating tasks and enhancing decision-making, moving beyond simple automation to intelligent autonomy. These agents are being implemented across various sectors, promising significant improvements in efficiency and productivity. A strategic roadmap is essential for successful AI agent implementation, aligning technology with workflows and business objectives to ensure that these systems have a real impact on operations and decision-making. Without a clear structure, companies risk wasting investments on generic tools and isolated pilot projects.
The impact of AI agents is particularly evident in customer experience (CX), with companies increasingly integrating AI agents into their technology interactions. Cisco's recent Agentic AI Report highlights the transformative impact of these autonomous agents, which can retain memory, reason about tasks, and autonomously select actions to optimize outcomes with minimal human intervention. Cisco's data anticipates that enterprises expect 56% of their interactions with technology partners will be managed by AI agents within the next 12 months, increasing to 68% over three years. This accelerated adoption necessitates that vendors rapidly develop and deploy scalable, robust agentic AI solutions. Thomson Reuters is also leveraging this trend with agentic AI capabilities in its CoCounsel assistant, enabling autonomous, multi-step task execution in tax and accounting workflows. Early results show that processes like tax jurisdiction reviews have been drastically reduced from half a week to under an hour. The company plans to extend agentic AI to legal, risk, and compliance domains, connecting firm knowledge, codes, and internal documents into one workspace where AI handles complete workflows, rather than individual queries. This integration allows professionals to focus on higher-level tasks, ensuring that human expertise guides judgment and validates outputs. Recommended read:
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@www.marktechpost.com
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The development of AI agents capable of performing human tasks on computers is gaining momentum, with a particular focus on multi-agent communication systems. Several research labs and companies are actively exploring this area, aiming to build agents that can effectively coordinate and collaborate. A key aspect of this research involves establishing robust communication protocols that enable seamless interaction between multiple AI agents. Recent articles highlight the progress being made in constructing code using these multi-agent communication systems, paving the way for more sophisticated and autonomous AI applications.
Mistral AI recently released its Agents API, providing public access through La Plateforme for developers to create autonomous agents. This API allows agents to plan tasks, utilize external tools, and maintain long-term context. The interface comes equipped with connectors for Python execution, web search, Flux 1.1 image generation, and a document library. The Agents API supports the mistral-medium-latest and mistral-large-latest models, allowing agents to delegate subtasks to each other via the Model Context Protocol, creating coordinated workflows across multiple services. A tutorial was recently released which provides a coding guide to building scalable multi-agent communication systems using the Agent Communication Protocol (ACP). This guide implements ACP by building a flexible messaging system in Python, leveraging Google's Gemini API for natural language processing. The tutorial details the installation and configuration of the google-generativeai library, introduces core abstractions, message types, performatives, and the ACPMessage data class for standardizing inter-agent communication. Through ACPAgent and ACPMessageBroker classes, the guide demonstrates how to create, send, route, and process structured messages among multiple autonomous agents, also showing how to implement querying, requesting actions, broadcasting information, maintaining conversation threads, acknowledgments, and error handling. Recommended read:
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Priyansh Khodiyar@CustomGPT
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CustomGPT
, hackernoon.com
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The Model Context Protocol (MCP) is gaining momentum as a key framework for standardizing interactions between AI agents and various applications. Developed initially by Anthropic, MCP aims to provide a universal method for AI models to connect with external tools, data sources, and systems, similar to how USB-C streamlines connections for devices. Microsoft is actively embracing this protocol, introducing MCP servers for its Dynamics 365 platform. Furthermore, companies are integrating MCP into their APIs, indicating a widespread movement towards its adoption.
The core challenge MCP addresses is the current fragmented and inconsistent nature of AI integrations. Without a standardized protocol, developers often resort to custom code and brittle integrations, leading to systems that are difficult to maintain and scale. MCP standardizes how context is defined, passed, and validated, ensuring that AI agents receive the correct information in the right format, regardless of the data source. This standardization promises to alleviate the "It Works on My Machine… Sometimes" syndrome, where AI applications function inconsistently across different environments. MCP's adoption is expected to pave the way for more autonomous enterprises and smarter systems. Microsoft envisions a future where AI agents proactively identify problems, suggest solutions, and maintain context across conversations, thereby transforming workflows across diverse fields such as marketing and software engineering. The evolution of identity standards, particularly OAuth, is crucial to secure agent access across connected systems, ensuring a robust and reliable ecosystem for AI agent interactions. This collaborative effort to build standards will empower the next generation of AI agents to operate effectively and securely. Recommended read:
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Coral Garnick@Madrona
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References:
AIwire
, insideAI News
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Microsoft is intensifying its efforts in AI agent technology, signaling a significant shift towards the "autonomous enterprise." At Microsoft Build 2025, the company highlighted its focus on agentic AI, including the expansion of Azure AI Foundry to support complex workflows and multi-agent systems. This platform now processes billions of enterprise queries daily, serving over 70,000 customers, and is being enhanced with Grok models from xAI, giving developers a wider selection of AI tools. CVP Charles Lamanna emphasizes that AI agents are fundamentally reshaping teams, tools, and workflows, predicting a future where "there will now be ‘an agent for that’" instead of just an app.
The introduction of the Model Context Protocol (MCP) servers for Microsoft Dynamics 365 ERP and CRM is aimed at enhancing AI agent interoperability. These servers break down data and application silos, allowing agents to work across processes, thereby enabling new autonomous scenarios for improved business functionality and productivity. Microsoft is also pushing for multi-agent workflows and on-device AI through Foundry Local, supporting open protocols like Agent-to-Agent (A2A) and MCP to streamline AI system communication. These protocols are crucial for creating an 'open agentic web', allowing diverse AI systems to work together efficiently. To ensure practical implementation for developers, Microsoft has introduced AgentOps tools within Azure AI Foundry. These tools provide real-time insights into agent performance, covering aspects such as efficiency, cost, and safety, which are often overlooked during deployment. NVIDIA, through its AI-Q Blueprint, is also addressing the challenge of integrating multiple vendor agent systems by promoting interoperability among agents, tools, and data sources. As Bartley Richardson from NVIDIA noted, enterprises must acknowledge the reality of multi-vendor environments and strive to seamlessly mesh different agent systems to drive efficiency across industries, even if the agentic systems don’t get it right 100% of the time. Recommended read:
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Alex Simons@Microsoft Security Blog
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Microsoft Security Blog
, www.microsoft.com
Microsoft is aggressively pursuing advancements in AI agent technology, with a focus on secure access and collaborative capabilities. The company's efforts are highlighted by the development of Magentic-UI, an open-source research prototype designed as a human-centered web agent. This agent aims to facilitate real-time collaboration on complex, web-based tasks. Microsoft envisions that within the next two years, AI agents will evolve from simply responding to requests to proactively identifying problems, suggesting solutions, and maintaining context across conversations.
The key to this evolution lies in adapting identity standards, specifically OAuth, to ensure secure agent access to data and systems. Microsoft is building a robust agent ecosystem, including sophisticated elements like MCP servers for Dynamics 365, to enhance AI interaction across various platforms. Magentic-UI, built on the Magentic-One architecture and powered by AutoGen, allows users to directly modify the agent's plan and provide feedback, ensuring transparency and control. It is integrated with Azure AI Foundry models and agents. Magentic-UI is engineered to support intricate, multi-step workflows through human-AI collaboration, showcasing potential advancements in agentic user experience (AUX). It can perform tasks that involve browsing the web, executing code, and understanding files. Microsoft believes the next generation of AI agents will augment and amplify organizational capabilities, enabling autonomous tasks such as creating marketing campaign plans and developing new software features with minimal human interaction. Recommended read:
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@www.marktechpost.com
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References:
AI News | VentureBeat
, www.marktechpost.com
AI agents are rapidly transforming software engineering workflows, offering increased efficiency and accessibility. Mistral AI has launched its Agents API, a platform designed to enable developers to integrate autonomous, generative AI capabilities into existing applications. This API allows for the creation of AI agents capable of performing tasks such as running Python code securely, generating images, and performing retrieval-augmented generation (RAG). These agents can access real-time information from the web and utilize user-provided document libraries, significantly enhancing their ability to provide accurate and up-to-date responses.
Designed to complement Mistral’s existing Chat Completion API, the Agents API focuses on agentic orchestration, built-in connectors, and persistent memory. The API is equipped with several built-in connectors, including Code Execution, Image Generation, Document Library, and Web Search. This flexibility allows for the coordination of multiple AI agents to tackle complex tasks, surpassing the limitations of traditional language models by enabling them to perform real-world tasks and maintain conversational context over time. The rise of AI agents is also changing the economics of software engineering. The emergence of "cheap SWE agents" is enabling teams with more millions in ARR than employees. Tools like GitHub Copilot, Claude Code, and OpenAI Codex are democratizing the field, making software development accessible to individuals without a technical background. These agents are also improving developer productivity and code quality. Recommended read:
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Alexey Shabanov@TestingCatalog
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Mistral AI is expanding its AI capabilities with the introduction of a new Agents feature within Le Chat, offering users intuitive customization, advanced controls, and faster performance. This redesigned Agents feature replaces the earlier Agent Builder interface and integrates closely with the main chat experience. It allows users to create and customize autonomous agents with functionalities similar to OpenAI's GPT Builder but with its unique design choices and system integrations.
Mistral AI has also launched its Agents API, a framework designed to empower developers to build AI agents capable of executing various tasks. These tasks include running Python code in a secure sandbox, generating images using the FLUX model, and performing retrieval-augmented generation (RAG). The Agents API provides a cohesive environment for large language models to interact with multiple tools and data sources, fostering efficient and versatile AI agent creation. The features that are converging across major LLM API vendors are code execution (Python in a sandbox), web search (using Brave), document library (hosted RAG), and image generation (FLUX for Mistral). The rate of MCP support is also similar across the major vendors with OpenAI adding it May 21st, Anthropic launched theirs May 22nd and now Mistral has launched theirs on May 27th. For professionals like Lead AI Engineers or Senior AI Engineers, the Mistral Agents API represents a powerful addition to their AI toolkit. Recommended read:
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Ken Yeung@Ken Yeung
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Microsoft is making significant strides in AI innovation, with a focus on both experimental and practical applications. One notable project unveiled at Build 2025 is Project Amelie, an experimental AI agent designed to autonomously build machine learning pipelines from a single prompt. Powered by Microsoft Research's RD agent, Amelie aims to automate and optimize research and development processes in machine learning, potentially eliminating manual setup work typically handled by data scientists. Early testing has shown promising results, with Project Amelie outperforming current state-of-the-art benchmarks on MLE-Bench.
Microsoft is also applying AI to solve real-world problems in healthcare and weather forecasting. They have unveiled an AI-powered orchestration system available through the Azure AI Foundry Agent Catalog to streamline cancer care planning, which brings together specialized AI agents to assist clinicians with analyzing multimodal medical data from imaging and genomics to clinical notes and pathology. This system aims to automate parts of the tumor board process, making personalized treatment plans more accessible. In weather forecasting, Microsoft's latest AI model, Aurora, is able to provide detailed and accurate 10-day forecasts in seconds. In addition to these innovations, Microsoft is advancing its Windows AI strategy with native support for Model Context Protocol (MCP) on Windows 11 and the introduction of Windows AI Foundry. The MCP integration will bring Anthropic's protocol to Windows 11, enabling AI agents to connect with native apps, system services, and external tools. With its Windows AI Foundry, developers can fine-tune and run AI models directly on Windows PCs. These efforts aim to build a secure agentic future on Windows, fostering the development of AI agents within the Windows ecosystem. Recommended read:
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@www.marktechpost.com
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References:
, AI News | VentureBeat
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Advancements in AI are rapidly shifting towards multi-agent systems, where specialized AI agents collaborate to perform complex tasks. These agents, envisioned as a team of expert colleagues, are designed to analyze data, interact with customers, and manage logistics, among other functions. The challenge lies in orchestrating these independent agents to work together seamlessly, ensuring they can coordinate interactions, manage shared knowledge, and handle potential failures effectively. Solid architectural blueprints are crucial for building reliable and scalable multi-agent systems, emphasizing the need for patterns designed for reliability and scale from the outset.
LangGraph Platform is emerging as a key tool for deploying these complex, long-running, and stateful AI agents. It addresses challenges such as maintaining open connections for extended processing times, preventing timeouts, and recovering from exceptions. The platform supports launching agent runs in the background, provides polling and streaming endpoints to monitor run status, and implements strategies to minimize exceptions. Features like heartbeat signals, configurable retries, and multiple streaming modes are crucial for reliable agent operation, providing end-users with intermediate output to demonstrate progress during lengthy processes. A new paradigm called Group Think is being explored to further enhance the efficiency of multi-agent reasoning. This approach allows multiple reasoning agents within a single LLM to operate concurrently, observing each other's partial outputs at the token level. By enabling real-time mutual adaptation among agents mid-generation, Group Think reduces duplication and speeds up collaborative LLM inference. This contrasts with traditional sequential or independently parallel sampling techniques, which often introduce delays and limit the practicality of deploying multi-agent LLMs in time-sensitive or computationally constrained environments. Recommended read:
References :
Sean Endicott@windowscentral.com
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Microsoft is aggressively pursuing the integration of AI agents across its ecosystem, as highlighted at Build 2025. The company is embedding AI deeper into Windows 11, utilizing the Model Context Protocol (MCP) to facilitate secure interaction between AI agents and both applications and system tools. This move transforms Windows into an "agentic" platform where AI can automate tasks without direct human intervention. The MCP acts as a standardized communication layer, enabling diverse AI agents and applications to seamlessly share information and perform actions. Microsoft is also pushing AI to the edge with tools unveiled at Build 2025, and are creating smarter faster experiences across devices.
Microsoft is also enhancing its Microsoft 365 Copilot with "Model Tuning," allowing businesses to train the AI assistant on internal data, creating domain-specific expertise. This feature enables the creation of AI agents customized for specialized tasks, such as legal document creation or drafting arguments, using an organization’s unique knowledge base. It’s designed to secure data within the platform, ensuring that internal information isn't used to train broader foundation models. The feature is rolling out in June through the Microsoft Copilot Tuning Program, available to customers with at least 5,000 M365 Copilot licenses. Adding to its AI advancements, Microsoft is exploring AI's role in various applications, like integrating Copilot into Notepad for AI-assisted writing and developing AI models like Aurora for accurate weather forecasting. However, a potential security concern arose when a private Teams message inadvertently revealed that "Microsoft is WAY ahead of Google with AI security" during a Build 2025 protest. The leaked message was within a Microsoft Teams message where Walmart are expanding their use of AI. The company is also developing NLWeb, an open-source protocol designed to AI-enable the web by transforming websites into AI-powered conversational interfaces. Recommended read:
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