News from the AI & ML world
DeeperML - #aiagents
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Lyzr Team@Lyzr AI
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The rise of Agentic AI is transforming enterprise workflows, as companies increasingly deploy AI agents to automate tasks and take actions across various business systems. Dust AI, a two-year-old artificial intelligence platform, exemplifies this trend, achieving $6 million in annual revenue by enabling enterprises to build AI agents capable of completing entire business workflows. This marks a six-fold increase from the previous year, indicating a significant shift in enterprise AI adoption away from basic chatbots towards more sophisticated, action-oriented systems. These agents leverage tools and APIs to streamline processes, highlighting the move towards practical AI applications that directly impact business operations.
Companies like Diliko are addressing the challenges of integrating AI, particularly for mid-sized organizations with limited resources. Diliko's platform focuses on automating data integration, organization, and governance through agentic AI, aiming to reduce manual maintenance and re-engineering efforts. This allows teams to focus on leveraging data for decision-making rather than grappling with infrastructure complexities. The Model Context Protocol (MCP) is a new standard developed by Dust AI that enables this level of automation, allowing AI agents to take concrete actions across business applications such as creating GitHub issues, scheduling calendar meetings, updating customer records, and even pushing code reviews, all while maintaining enterprise-grade security. Agentic AI is also making significant inroads into risk and compliance, as showcased by Lyzr, whose modular AI agents are deployed to automate regulatory and risk-related workflows. These agents facilitate real-time monitoring, policy mapping, anomaly detection, fraud identification, and regulatory reporting, offering scalable precision and continuous assurance. For example, a Data Ingestion Agent extracts insights from various sources, which are then processed by a Policy Mapping Agent to classify inputs against enterprise policies. This automation reduces manual errors, lowers compliance costs, and accelerates audits, demonstrating the potential of AI to transform traditionally labor-intensive areas. Recommended read:
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@shellypalmer.com
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Lyzr AI
, AI Accelerator Institute
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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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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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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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References:
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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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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