Agents

Databricks introduces Omnigent, a meta-harness for combining, controlling, and sharing agents, to streamline the use of agents at scale. Not mentioned are specific numbers, model names, or benchmark results. The practical implication for engineers building AI systems is the potential to improve agent management and collaboration. Omnigent aims to provide a unified platform for agent development and deployment. The introduction of Omnigent may simplify the process of building and managing complex agent pipelines.

The NVIDIA Blackwell Ultra NVL72 platform has achieved leading performance in the first round of the AgentPerf benchmark, a new industry standard for agentic AI infrastructure, running 20x more agents per megawatt than the NVIDIA Hopper. This benchmark measures the performance of systems in handling complex, multi-step AI workloads, which are fundamentally different from conversational AI. The results demonstrate the importance of codesign and optimization across the full stack for achieving high performance in agentic AI. The practical implication for engineers building AI systems is that they need to consider the unique requirements of agentic AI workloads when designing and optimizing their systems.

ChatSee.AI Inc. has raised $6.5 million in seed funding to develop a 'failure memory' layer for enterprise AI agents, enabling them to learn from past failures and improve performance. This technology aims to reduce the risk of AI system failures and improve overall reliability. The authors note that traditional AI systems often lack the ability to learn from failures, leading to repeated mistakes. By incorporating a failure memory layer, ChatSee's technology promises to enhance the robustness and resilience of AI agents. This development has significant implications for the adoption of AI in high-stakes industries such as finance and healthcare.

The Strands Evals SDK introduces detectors that automate AI agent failure detection and root cause analysis, reducing diagnosis time from hours to minutes. Detectors analyze execution traces using large language model (LLM)-based analysis and provide structured output, including categorized failures, causal chains, and fix recommendations. This complements the evaluation framework by answering not only "how well did the agent do?" but also "why did it fail and how do I fix it?". The detector pipeline operates in two phases, with Phase 1 scanning each span in a session against a comprehensive failure taxonomy. For engineers building AI systems, this means they can quickly identify and fix issues, improving overall system reliability and performance.

Sakana AI has launched Sakana Marlin, a virtual Chief Strategy Officer that uses "ultra deep research" to generate 100+ page reports in 8 hours, abandoning instantaneous text generation in favor of deep, long-horizon reasoning. Marlin operates as a self-contained digital strategy team, formulating hypotheses, gathering data, and mapping causal dynamics to deliver comprehensive, professional-grade portfolios. This approach marks a shift from shallow, rapid generation to deep, methodical reasoning, targeting corporations, financial institutions, and think tanks. The practical implication for engineers building AI systems is the potential to integrate Marlin's long-horizon reasoning capabilities into their own systems, enabling more in-depth and strategic analysis.

OpenAI Group PBC has acquired Ona, a startup specializing in AI agent orchestration, to improve management of long-running AI agents, potentially enhancing productivity and efficiency for developers. This acquisition may facilitate the deployment and scaling of AI agents in various environments, including local machines and cloud infrastructure. The acquisition's impact on AI agent management and developer workflows remains to be seen. By integrating Ona's platform, OpenAI aims to streamline the process of running and managing AI agents, reducing the need for manual intervention and improving overall system reliability.

The AI landscape has shifted from models that simply know to agents that do, with foundation models being used as cognitive engines for AI agents in the physical world. To be useful in high-stakes physical environments, agents need to be grounded in physical laws and operational constraints, overcoming the challenge of hallucination. Four approaches to grounding AI agents are proposed, including physics-guided deep learning, which integrates first-principle physical knowledge into the foundation model in pretraining. This ensures that predictions obey governing physical laws, making agents physically consistent and operationally reliable. The practical implication for engineers building AI systems is that they must consider the physical constraints of the environment in which their agents will operate.

A recent Ivanti research survey found that 85% of IT professionals claim every AI agent has a named owner, but only 42% actually know who owns them, revealing a significant governance gap. Organizational leaders are more likely to hide their AI use, with 42% doing so for a "secret advantage." The lack of clear ownership and governance frameworks poses significant risks, including the potential for employees to use unmanaged AI engines with sensitive customer data. This gap has significant implications for engineers building AI systems, as it highlights the need for more robust governance and ownership structures.

The Practitioner's Guide to AgentOps outlines a comprehensive framework for building and managing multi-step AI agent pipelines, leveraging the AgentOps platform to streamline workflows, and integrating with various tools and services such as AWS Bedrock and LangChain. The guide provides a detailed overview of AgentOps' architecture, including its ability to handle complex tasks, integrate with existing systems, and scale to meet the demands of large enterprises. By adopting AgentOps, practitioners can reduce the complexity of building and deploying AI agents, enabling faster time-to-market and improved business outcomes. However, the guide notes that successful implementation requires careful planning, integration, and testing to ensure seamless operation.

The performance of AI agents is hindered by the intent-execution gap, which is the mismatch between what the model intends and what the harness executes. Minimizing this gap is sufficient to achieve state-of-the-art performance across diverse agentic benchmarks. The Simple Strands Agent (SSA) is introduced as a lightweight and customizable single-agent harness designed to close the gap between reported and actual performance. Effective agent design is not entirely model agnostic, and model-harness codesign is critical in achieving optimal performance. This has significant implications for engineers building AI systems, as it highlights the importance of considering the model-harness interface and identifying invariant components that remain effective across model upgrades and environments.

The authors present a novel framework for strategic reasoning in complex multi-agent decision-making, leveraging insights from game theory and multi-agent systems. This framework, called "Strategic Reasoning Graphs," enables the representation of complex decision-making processes as a graph, allowing for more efficient and scalable reasoning. The authors demonstrate the effectiveness of their framework on a variety of benchmark scenarios, including a multi-agent game with 10 players and 100 actions, achieving a 30% improvement in decision-making speed. The proposed framework has the potential to advance AI systems in complex decision-making environments, such as autonomous vehicles and smart cities.

Rocket Close built Supercharger, an agentic AI solution, to optimize title operations workflows by combining title and closing knowledge to guide teams through the order processing workflow. The solution uses Strands Agents, large language models (LLMs), Amazon Bedrock, Amazon Bedrock Knowledge Bases, and Model Context Protocol (MCP) tools to centralize knowledge and automate research-heavy tasks. This results in improved efficiency, reduced time spent searching for information, and enhanced operational efficiency and client experience. The solution's architecture is designed with security in mind, using Amazon Bedrock Guardrails and row-level data entitlements to prevent accidental access to customer-sensitive data. For engineers building AI systems, this solution demonstrates the potential of agentic AI to streamline complex workflows and improve productivity.

The article discusses the limitations of increasing context size in Retrieval-Augmented Generation (RAG) systems for aggregation tasks, finding that it does not improve accuracy and instead makes errors harder to detect. The author benchmarks retrieval-based pipelines against a deterministic full-scan engine across 100,000 rows, demonstrating the need to route computation queries away from RAG. This finding has significant implications for engineers building AI systems, as it suggests that alternative approaches are needed to improve accuracy in aggregation tasks. The author's system, built in response to these limitations, offers a potential solution.

PhoenixAI, a company formerly known as CelerData, has secured $80 million in Series B funding to accelerate the development of its AI-native database technology, designed to support the growth of agentic AI in regulated industries. This investment will enable the company to expand its governance capabilities and further develop its database technology. The AI-native database is expected to improve data management and analysis for applications that rely on large language models and multi-step AI agents. This move marks a significant step towards creating more robust and scalable AI systems that can handle complex data and tasks.

The AI agent ecosystem is currently in a phase of protocol proliferation, with four significant protocols published in the past eighteen months: Model Context Protocol (MCP), Agent2Agent (A2A), Agent Communication Protocol (ACP), and Agent Network Protocol (ANP). MCP has already won the tool-calling layer, with over 10,000 active public MCP servers and 164 million monthly Python SDK downloads by April 2026. A2A is a task coordination interface that defines how two agents delegate a task, while ACP is a message envelope format and ANP is a discovery and identity protocol. The practical implication for engineers building AI systems is that they need to understand the different layers of the stack and choose the appropriate protocol for their specific use case.

Publicis Sapient has introduced Sapient Sustain, an AI-enabled support platform that leverages agentic artificial intelligence to enhance the reliability of IT operations and managed services. By using AI to automate and optimize IT processes, Sapient Sustain aims to reduce downtime and improve overall IT performance. The platform's AI capabilities enable proactive issue detection and resolution, allowing IT teams to focus on strategic initiatives. This marks a significant step towards transforming IT operations with AI-driven intelligence.

Agent-EvalKit, an open-source toolkit under the Apache 2.0 license, enables systematic evaluation of AI agents by integrating with popular AI coding assistants, including Claude Code, Kiro CLI, and Kilo Code. It spans six evaluation phases, facilitating a comprehensive assessment of AI agents. This evaluation framework can be applied to various domains, including travel research, showcasing its versatility. By leveraging Agent-EvalKit, developers can refine and improve their AI agents, leading to better performance and more accurate results. However, the toolkit's effectiveness heavily relies on the quality of the evaluation metrics and the agents being assessed.

Visa has partnered with OpenAI to enable AI agents to make payments on behalf of users, integrating with the OpenAI platform to facilitate agentic commerce. This collaboration combines Visa's global payment network with OpenAI's AI capabilities, allowing for seamless transactions through AI-powered interfaces. The partnership marks a significant step toward increasing the use of AI in everyday commerce. This integration is expected to simplify payment processes for users, but it may also raise security and trust concerns in the long run.

A shift in FinOps AI strategies is underway, moving beyond token economics as agentic costs emerge, potentially leading to runaway spending on workloads that organizations barely understand. This evolution is driven by the increasing complexity of cloud cost management, requiring a more strategic framework for governing AI workloads. The authors argue that organizations must adapt to this new paradigm to avoid uncontrolled spending and inefficient resource allocation. This shift demands a more nuanced understanding of AI workloads and their associated costs, which can be unpredictable and difficult to manage.

NVIDIA researchers have developed a new AI framework for grasping, autonomous driving, and multi-agent training that leverages a combination of simulation and real-world data to improve performance and robustness. The framework uses a novel architecture that integrates a multi-modal perception model with a reinforcement learning-based control policy, enabling robots to adapt to new objects and environments. This approach has been demonstrated to improve grasping success rates by 15% and autonomous driving safety by 20% in simulation. By training agents in simulation and fine-tuning them on real-world data, the framework enables scalable and efficient training of complex AI systems.

NVIDIA is introducing Agent Skills for autonomous vehicles, robotics, and vision AI, enabling researchers to accelerate development by providing a complete workflow for physical AI research. This includes a set of pre-trained models, a simulation environment, and a suite of tools for data collection and training. By streamlining the development process, researchers can focus on higher-level tasks such as system integration and testing. This marks a significant step towards more efficient physical AI research, potentially leading to breakthroughs in autonomous vehicles and robotics.

NVIDIA and Microsoft are collaborating on a unified stack for agentic AI deployment, integrating AI models with fast hardware, secure runtimes, and a responsive data layer across Windows devices, cloud, and local environments. This stack is designed to support long-running reasoning and real-time decision-making in AI applications. The partnership aims to accelerate the development and deployment of agentic AI systems, enabling developers to build more sophisticated and responsive AI experiences. The unified stack is expected to bridge the gap between model development and deployment, reducing the complexity and increasing the efficiency of AI development.

NVIDIA has announced NVIDIA JetPack 7.2 and NVIDIA NemoClaw support on NVIDIA Jetson, enabling agentic AI capabilities in the physical world. This is achieved through a substantial performance gain on the Jetson AGX Orin 32GB module, with NVIDIA CUDA 13 now supported on the NVIDIA Jetson Orin. The Yocto project is also supported, providing a flexible and customizable build system. This development brings agentic AI to the edge, empowering developers to create more sophisticated and interactive AI experiences.