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Build AI Agents Worth Keeping: The Canvas Framework
Enterprise AI agent initiatives frequently stall after pilot phases due to technology-first thinking, governance gaps, infrastructure complexity, and poor alignment with business needs. To bridge this divide, development teams are shifting away from data-first pipelines toward a product-first methodology structured as product, agent, data, and model. The Canvas framework provides a phased workflow moving from quick proof-of-concept validation to model orchestration and operational hardening. In Phase 4, developers focus on API management, external provider orchestration, cost optimization, and evaluation pipelines. Phase 5 adds necessary governance, compliance, user experience, and security layers required to transform working agent prototypes into sustainable production deployments.
Powering Partner Gateway metrics with Apache Pinot
Partner Gateway needed low-latency analytics for API traffic and response-time metrics across intervals from one hour to 30-day retrospectives, with a particular dataset potentially reaching approximately 6.8 billion rows in a 30-day period. Apache Pinot was selected as a distributed real-time OLAP datastore, using high-throughput ingestion and columnar, indexed, partitioned data for millisecond analytical queries. Initial aggregation queries on datasets over 150GB timed out for 20- to 30-day periods, exceeding 10 seconds and threatening a 300-millisecond SLA. The implementation routes metrics through Kafka and Apache Flink, then ingests Schema Registry-compatible messages into Pinot, while partitioning by metric name and adding aggregation-focused optimizations. For one metric, query latency fell from 5,072 to 35 milliseconds; Star-tree indexing improved speed but raised 30-day storage from 192GB to 373GB, with the additional AWS EBS cost described as minor.
Smol2Operator: Post-Training GUI Agents for Computer Use
Lightweight vision-language models often lack the precise visual grounding and action execution capabilities required for graphical user interface automation. To resolve this deficiency, the authors constructed a post-training pipeline that evolves base models into agentic GUI coders through supervised fine-tuning. The process standardizes disparate mobile and desktop dataset action spaces into a unified functional format utilizing normalized coordinates relative to image size. Training is structured into two sequential phases that first establish basic visual perception and subsequently introduce explicit reasoning steps with masked assistant loss. On the ScreenSpot-v2 perception benchmark, fine-tuning SmolVLM2-2.2B-Instruct increased element localization accuracy to 61.71 percent, while a compact 460-million-parameter model achieved approximately 58 percent accuracy.
Simplify AI-Driven Data Connectivity With MongoDB and MCP Toolbox
Organizations building generative AI applications require simplified, standardized connections to an expanding ecosystem of enterprise databases. MCP Toolbox for Databases, an open-source Model Context Protocol server based on Anthropic's standardized protocol, now supports MongoDB Atlas to streamline connections to generative AI systems. This new integration allows gen AI agents to execute standard CRUD operations alongside advanced transformations and computations using the MongoDB aggregation framework. Through flexible configuration files, developers can configure MongoDB Atlas as both a data source and a sink by defining parameters like connection URIs, target datasets, filters, projections, and toolsets. By linking flexible document data with cross-database query capabilities, enterprises can unify results and automate multi-source AI workflows.
SyGra: The One-Stop Framework for Building Data for LLMs and SLMs
Training and aligning large language models and small language models requires datasets tailored for complex reasoning, preference tuning, multi-turn questioning, and format conversion. To eliminate the need for bespoke data preparation scripts, SyGra provides a low-code and no-code Python framework for automated dataset creation, transformation, and alignment. The library integrates with diverse inference backends, including vLLM, Hugging Face TGI, Triton, and Ollama, allowing teams to focus on prompt engineering rather than pipeline infrastructure. It supports data workflows ranging from converting PDFs and knowledge bases into question-answering pairs to filtering low-quality samples and generating Direct Preference Optimization datasets. These plug-and-play workflows reduce manual curation effort while accelerating model fine-tuning and retrieval-augmented generation pipelines.
Introducing the Okta Open Source MCP Server
The Okta Open Source MCP Server connects large language model agents to Okta’s Admin Management APIs for identity and access management workflows. It translates natural-language requests into structured API calls, supporting user and group management, group membership and application operations, system-log retrieval, and additional application and policy actions. The server uses Okta’s official SDK and supports interactive Device Authorization Grant authentication or browserless Private Key JWT, with scoped API access and credentials supplied through environment variables. The guide explains setup with Python 3.9 or later and uv, Okta application configuration, and VS Code’s MCP client settings, then shows examples including user administration and audit-report generation. Its stated purpose is to automate organization-management tasks while respecting Okta’s security model.
Gaia2 and ARE: Empowering the community to study agents
Existing AI agent evaluation environments are often tightly coupled to specific tasks and fail to model real-world challenges such as API failures, spontaneous events, and asynchronous conditions. To address this limitation, the Gaia2 benchmark and the Meta Agents Research Environments (ARE) framework introduce interactive read-and-write evaluation. Gaia2 incorporates 1,000 human-created scenarios spanning multi-step execution, cross-source search, ambiguity handling, adaptability, temporal reasoning, agent collaboration, and noise tolerance. Using a simulated smartphone interface equipped with 101 tools, evaluations showed that GPT-5 with high reasoning scored highest overall, while Kimi K2 was the leading open-source model. The results demonstrated that instruction following and search do not reliably predict performance on closer-to-real-world tasks.
MongoDB Community Edition to Atlas: A Migration Masterclass With BharatPE
BharatPE, an Indian fintech processing over ₹12,000 crore monthly across 450 cities, operated a 45-terabyte self-hosted MongoDB Community Edition deployment. Managing three sharded three-node clusters resulted in uneven data distribution, high maintenance costs, scaling friction, and disaster recovery gaps under strict regulatory requirements. To modernize infrastructure, the database operations team partnered with MongoDB to execute a structured five-stage migration to MongoDB Atlas. The initiative utilized mongosync for secure data transition alongside environment mirroring and automated integrity validation scripts. Transitioning to MongoDB Atlas yielded a 40 percent improvement in query response times and provided automated failover backed by a 99.995 percent uptime SLA.
Scaleway on Hugging Face Inference Providers 🔥
Scaleway is integrated as a supported serverless Inference Provider on the Hugging Face Hub, expanding model deployment options across Hub model pages and official client SDKs for JavaScript and Python. Operating out of European data centers located in Paris, France, Scaleway Generative APIs host open-weight models including gpt-oss, Qwen3, DeepSeek R1, and Gemma 3 with structured outputs, function calling, multimodal processing, and sub-200ms first-token response times. Developers can route inference requests using their Hugging Face tokens or supply direct Scaleway API keys. Billing for routed requests charges standard provider rates starting at €0.20 per million tokens without markups, while direct requests bill to Scaleway accounts. Hugging Face PRO subscribers receive two dollars of monthly inference credits applicable across providers.
Introducing Notion 3.0
Notion 3.0 introduces Notion AI Agents that can create documents, build databases, search across tools, and execute multi-step workflows inside a workspace. Agents can perform up to 20 minutes of autonomous work across hundreds of pages, including researching information, synthesising findings, creating structured databases, and notifying users when tasks finish. Users can personalise an Agent through a Notion page containing instructions and context, including task triage, response formatting, and reference information; templates can add specialised skills. The launch also includes database row permissions, new AI connectors, and additional MCP integrations, while Custom Agents are planned to run on schedules or triggers and share workflows with teams.
Navigating the AI storm: How to harness the power of AI without being crushed by it
Support leaders are being asked to preserve customer experience and operational performance while becoming AI experts and leading high-lift, cross-functional implementations. Before AI, support was often treated as a cost center; now executives may demand aggressive AI metrics without understanding the resources, risks, and work still required. The proposed response is to harness the change by learning AI deeply, testing multiple vendors through trials, choosing a durable partner, and defining benchmarks and phased implementation plans. Support leaders should also explain why deflecting 50% of inquiries does not justify cutting staffing by 50%, given coverage, service-level redundancy, growth, attrition, and work beyond direct inquiries. The article ultimately argues that visible ownership, concise project management, and weekly or bi-weekly executive updates can reposition support as a value driver rather than a cost center.
Modernizing Core Insurance Systems: Breaking the Batch Bottleneck
Migrating core insurance platforms from legacy relational databases with PL/SQL to Java and MongoDB Atlas frequently degrades batch processing performance. Like-for-like migrations can cause batch jobs to run 25 to 30 times slower due to high network round-trips, inefficient per-record operations, and under-utilized bulk capabilities. To resolve these bottlenecks, an optimization framework was developed that leverages MongoDB native bulkWrite operations, intelligent reference data prefetching, and parallel execution. An architecture combining a Spring Boot controller and a dedicated executor framework partitions workloads across threads or event processors. This framework recovered lost performance, completing previously failing batch jobs within service-level agreements and achieving 10 to 15 times better execution speeds than legacy systems in certain workloads.
MongoDB.local NYC 2025：定义 AI 时代的理想数据库
The page associated with the title “MongoDB.local NYC 2025: Defining the Ideal Database for the AI Era” is rendered as a MongoDB Resources page rather than an event article. Its metadata describes a collection of webinars, video presentations, white papers, datasheets, and other resources, with controls for content type and language. The visible resource content includes “MongoDB Search Fundamentals: Powering Modern Search Experiences,” which presents MongoDB Search as an integrated full-text search capability and covers lexical search, search indexes, autocomplete, fuzzy queries, scoring, filters, and facets. The provided text does not state details about the NYC event or establish a conclusion about an ideal database for AI.
MongoDB.local NYC 2025: Defining the Ideal Database for the AI Era
At MongoDB.local NYC, MongoDB introduced new database capabilities, AI integrations, and modernization tools designed for agentic and generative AI workloads. The release of MongoDB 8.2 arrives alongside public previews of Search and Vector Search for both MongoDB Community Edition and Enterprise Server. MongoDB also highlighted Voyage AI embedding models and rerankers to improve retrieval accuracy across raw data, metadata, and embeddings. To address the costs and constraints of legacy infrastructure, the new MongoDB Application Modernization Platform combines AI-assisted tooling and specialized expertise to migrate legacy systems. According to MongoDB, early migrations using this platform run two to three times faster, while code rewriting tasks accelerate by an order of magnitude.
MongoDB.local NYC 2025: Definiendo la base de datos ideal para la era de la IA
At MongoDB.local NYC 2025, MongoDB introduced new capabilities and product updates tailored for artificial intelligence workloads. MongoDB 8.2 was unveiled alongside public previews of full-text and vector search for both MongoDB Community Edition and Enterprise Server. The company also detailed its integration with Voyage AI embedding and reranking models to deliver precision retrieval across raw data, metadata, and vectors. To address legacy infrastructure migration hurdles, MongoDB launched the Application Modernization Platform, which combines AI-assisted tooling and specialized workflows to accelerate legacy application refactoring two to three times faster. These architectural updates aim to position the JSON document model as the persistent memory and context store required by autonomous, agentic AI workflows.
MongoDB.local NYC 2025 : définir la base de données idéale à l'ère de l'IA
At the MongoDB.local NYC 2025 event, MongoDB unveiled several platform updates aimed at modernizing enterprise database architectures and supporting emerging agentic artificial intelligence workloads. The newly released MongoDB 8.2 version provides enhanced performance alongside expanded developer features across the broader document database ecosystem. The company also integrated Voyage AI embedding and reranking models to improve context retrieval precision and efficiency when building production-grade AI systems. Furthermore, dedicated Search and Vector Search capabilities have reached general availability across both MongoDB Community Edition and Enterprise Server environments. To streamline infrastructure transitions, the newly introduced Application Modernization Platform leverages AI-powered tooling and specialized techniques to migrate legacy database systems two to three times faster.
MongoDB.local NYC 2025: Definindo o Banco de Dados Ideal para a Era da IA
At MongoDB.local NYC, new product capabilities were unveiled to support modern enterprise workloads and artificial intelligence applications. The release of MongoDB 8.2 delivers improved performance alongside embedding and reranking models from Voyage AI for AI application development. Additionally, search and vector search capabilities have entered public preview for both MongoDB Community Edition and Enterprise Server across customer runtime environments. To address the high operational costs of legacy database systems, MongoDB introduced the Application Modernization Platform to accelerate migration and automated code rewriting using AI-driven tooling. These capabilities position the JSON-based document database as a foundational data layer providing persistent state, contextual retrieval, and memory for agentic AI systems.
MongoDB.local NYC 2025: AI 시대를 위한 이상적인 데이터베이스 정의
MongoDB presented multiple updates at MongoDB.local NYC 2025 aimed at supporting modern AI workloads and enterprise modernization. The event detailed MongoDB 8.2 alongside Voyage AI embedding models and rerankers designed to enhance accuracy and efficiency in AI applications. Search and vector search capabilities have expanded into public preview for MongoDB Community Edition and Enterprise Server. Furthermore, the company launched the MongoDB Application Modernization Platform, which combines AI-driven tools and expert workflows to migrate legacy systems to MongoDB two to three times faster. MongoDB positions its JSON document model as a foundation providing memory, facts, and state continuity for agentic AI applications.
MongoDB.local NYC 2025: Definition der idealen Datenbank für das KI-Zeitalter
MongoDB unveiled several core database updates aimed at supporting artificial intelligence workloads during MongoDB.local NYC 2025. The release of MongoDB 8.2 introduces performance enhancements alongside Voyage AI embedding models and rerankers for AI applications. Furthermore, Search and Vector Search are now available in public preview for both MongoDB Community Edition and MongoDB Enterprise Server. To address the costs and operational friction of legacy systems, MongoDB launched the Application Modernization Platform (AMP), which combines AI-powered tools with specialized talent to accelerate database migrations. These capabilities position the JSON-based document model to supply the persistent state, memory, and retrieval mechanisms required by emerging agentic AI architectures.
MongoDB.local NYC 2025: Definire il database ideale per l'era dell'AI
MongoDB announced several product releases and platform updates aimed at supporting artificial intelligence and agentic workflows at MongoDB.local NYC. The release of MongoDB 8.2 arrives alongside integrations with Voyage AI embedding and reranker models designed to enhance data retrieval precision. In addition, MongoDB launched Search and Vector Search in public preview for both Community Edition and Enterprise Server deployments, bringing vector capabilities to self-managed environments. To assist organizations transitioning away from rigid legacy database systems, MongoDB also introduced the Application Modernization Platform, which combines AI-driven tooling and specialized migration expertise. Early benchmarks from the modernization platform demonstrate legacy migrations running two to three times faster while accelerating code rewriting tasks by an order of magnitude.
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