Posts tagged with data
Why it matters: Engineers face increasing data fragmentation across SaaS silos. This post details how to build a unified context engine using knowledge graphs, multimodal processing, and prompt optimization (DSPy) to enable effective RAG and agentic workflows over proprietary enterprise data.
- •Dropbox Dash functions as a universal context engine, integrating disparate SaaS applications and proprietary content into a unified searchable index.
- •The system utilizes custom crawlers to navigate complex API rate limits, diverse authentication schemes, and granular permission systems (ACLs).
- •Content enrichment involves normalizing files into markdown and using multimodal models for scene extraction in video and transcription in audio.
- •Knowledge graphs are employed to map relationships between entities across platforms, providing deeper context for agentic queries.
- •The engineering team leverages DSPy for programmatic prompt optimization and 'LLM as a judge' frameworks for automated evaluation.
- •The architecture explores the Model Context Protocol (MCP) to standardize how LLMs interact with external data sources and tools.
Why it matters: The GitHub Innovation Graph provides a rare, large-scale dataset on open-source activity. It validates the global impact of developer contributions and offers data-driven insights into how software collaboration influences economic policy, AI development, and geopolitical trends.
- •GitHub released its second full year of data for the Innovation Graph, providing aggregated statistics on global public software development activity.
- •The update includes refreshed bar chart races for global metrics such as git pushes, repositories, developers, and organizations.
- •Academic researchers are utilizing the dataset to study global collaboration networks, software economic complexity, and digital production in emerging markets.
- •The data has been integrated into major global reports, including the Stanford AI Index and the WIPO Global Innovation Index, to track AI and innovation trends.
- •Future goals focus on improving data accessibility and expanding metrics to better support researchers and policy makers in the open-source ecosystem.
Why it matters: Translating natural language to complex DSLs reduces friction for subject matter experts interacting with massive, federated datasets. This approach bridges the gap between intuitive human intent and rigid technical schemas, improving productivity across hundreds of enterprise applications.
- •Netflix is evolving its Graph Search platform to support natural language queries using Large Language Models (LLMs).
- •The system translates ambiguous user input into a structured Filter Domain Specific Language (DSL) for federated GraphQL data.
- •Accuracy is maintained by ensuring syntactic, semantic, and pragmatic correctness through schema validation and controlled vocabularies.
- •The architecture utilizes Retrieval-Augmented Generation (RAG) to provide domain-specific data processing without replacing existing UIs.
- •Pre-processing and context engineering are critical to prevent LLM hallucinations and ensure fields match the underlying index.
Why it matters: This article details the architectural shift from fragmented point solutions to a unified AI stack. It provides a blueprint for solving data consistency and metadata scaling challenges, essential for engineers building reliable, real-time agentic systems at enterprise scale.
- •Salesforce unified its data, agent, and application layers into the Agentforce 360 stack to ensure consistent context and reasoning across all surfaces.
- •The platform uses Data 360 as a universal semantic model, harmonizing signals from streaming, batch, and zero-copy sources into a single plane of glass.
- •Engineers addressed metadata scaling by treating metadata as data, enabling efficient indexing and retrieval for massive entity volumes.
- •A harmonization metamodel defines mappings and transformations to generate canonical customer profiles from heterogeneous data sources.
- •The architecture centralizes freshness and ingest control to maintain identical answers across different AI agents and applications.
- •Real-time event correlation is optimized to update unified context immediately while balancing storage costs for large-scale personalization.
Why it matters: Azure Storage is shifting from passive storage to an active, AI-optimized platform. Engineers must understand these scale and performance improvements to architect systems capable of handling the high-concurrency, high-throughput demands of autonomous agents and LLM lifecycles.
- •Azure Storage is evolving into a unified platform supporting the full AI lifecycle, from frontier model training to large-scale inferencing and agentic applications.
- •Blob scaled accounts now support millions of objects across hundreds of scale units, enabling massive datasets for training and tuning.
- •Azure Managed Lustre (AMLFS) has expanded to support 25 PiB namespaces and 512 GBps throughput to maximize GPU utilization in high-performance computing.
- •Deep integration with frameworks like Microsoft Foundry, Ray, and LangChain facilitates seamless data grounding and low-latency context serving for RAG architectures.
- •Elastic SAN and Azure Container Storage (ACStor) are being optimized for 'agentic scale' to handle the high concurrency and query volume of autonomous agents.
- •New storage tiers and performance updates, such as Premium SSD v2 and Cold/Archive tiers for Azure Files, focus on reducing TCO for mission-critical workloads.
Why it matters: Cross-agent memory allows AI tools to learn codebase conventions autonomously, reducing manual context-setting. Its just-in-time verification ensures agents don't act on stale data, significantly improving the reliability of AI-generated code and reviews in complex, evolving repositories.
- •GitHub Copilot is evolving into a multi-agent ecosystem where agents share a cumulative knowledge base across the development lifecycle.
- •The system uses cross-agent memory to learn codebase conventions and patterns without requiring explicit user instructions for every session.
- •To solve the problem of stale data, GitHub implemented 'just-in-time verification' rather than expensive offline curation services.
- •Memories are stored with specific code citations, which agents verify via real-time read operations to ensure relevance to the current branch.
- •Memory creation is handled as a tool call, allowing agents to autonomously document facts like API synchronization requirements or logging patterns.
- •The feature is currently in public preview and is fully opt-in for Copilot coding agent, CLI, and code review users.
Why it matters: Engineers must balance speed-to-market with customizability. This ecosystem simplifies the 'build vs. buy' decision by providing pre-vetted models and agents that integrate with existing stacks while ensuring governance and cost optimization through cloud consumption commitments.
- •Microsoft Marketplace provides a central catalog of over 11,000 AI models and 4,000 apps to support build, buy, or hybrid AI strategies.
- •Pro-code developers can access foundational models from Anthropic, Meta, and OpenAI via Azure Foundry to maintain full control over custom logic and IP.
- •Low-code development is enabled through Microsoft Copilot Studio, allowing teams to build agents grounded in organizational data with minimal coding.
- •Ready-made agents and multi-agent systems can be deployed directly into Microsoft 365 Copilot to accelerate time-to-value for common business use cases.
- •Governance tools like Private Azure Marketplace allow IT teams to curate approved solutions and maintain oversight of AI deployments.
- •Marketplace transactions can be applied toward Microsoft Azure Consumption Commitment (MACC), helping organizations optimize cloud spend and procurement.
Why it matters: This acquisition signals a shift from chaotic web scraping to structured, licensed data for AI. For engineers, it introduces new patterns like pub/sub content indexing and machine-to-machine payments (x402), moving away from inefficient crawling toward a sustainable, automated web economy.
- •Cloudflare has acquired Human Native, a UK-based marketplace that transforms unstructured multimedia content into high-quality, licensed AI training data.
- •The acquisition aims to address the strain on the internet's economic model caused by skyrocketing crawl-to-referral ratios from AI bots.
- •Cloudflare is developing an 'AI Index' using a pub/sub model, allowing websites to push structured updates to developers in real time instead of relying on blind crawling.
- •The integration supports Cloudflare's existing tools like AI Crawl Control and Pay Per Crawl, giving content owners granular control over bot access.
- •Cloudflare is partnering with Coinbase on the x402 Foundation to establish protocols for machine-to-machine transactions and digital resource payments.
Why it matters: Traditional engagement metrics like watch time don't always reflect true user interest. By integrating direct survey feedback into ranking models, engineers can reduce noise, improve long-term retention, and better align content with niche user preferences in large-scale recommendation systems.
- •Facebook Reels transitioned from relying solely on engagement metrics like watch time to integrating direct user feedback via the User True Interest Survey (UTIS) model.
- •The UTIS model acts as a lightweight alignment layer trained on binarized survey responses to predict user satisfaction and content relevance.
- •Research indicated that traditional interest heuristics only achieved 48.3% precision, highlighting the gap between engagement signals and true user interest.
- •The system addresses sampling and nonresponse bias by weighting survey data to ensure the training set accurately reflects the broader user base.
- •Integrating survey-based interest matching led to significant improvements in long-term user retention, engagement, and satisfaction across video surfaces.
Why it matters: As AI adoption scales, engineers need unified tools to manage model lifecycles, security, and compliance. Microsoft’s integrated approach reduces operational risk and simplifies the deployment of responsible, agentic AI systems across complex multicloud environments.
- •Microsoft recognized as a Leader in the 2025-2026 IDC MarketScape for Unified AI Governance Platforms.
- •Microsoft Foundry serves as the developer control plane for model development, evaluation, deployment, and monitoring.
- •Microsoft Agent 365 provides a centralized IT control plane for managing and securing agentic AI across the enterprise.
- •Integrated security features include real-time jailbreak detection, agent identity management via Entra, and AI-specific threat protection in Defender.
- •Automated compliance tools in Microsoft Purview support over 100 regulatory frameworks for hybrid and multicloud environments.