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834 posts about Architecture. Every summary links to the original.
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Amazon Bedrock is the managed reasoning tier in an agent architecture, not the whole system. This layered view shows the pipelines, governed tables, query layer, and guardrails around it.
API versioning as an architecture: requests enter through a version header, pass through a chain of reversible version changes, and reach controllers that only ever see the latest model.
BIAN gives banks a shared vocabulary and service-domain contracts that sit between business concepts and the systems implementing them. See how domains, contracts, and governed access stack into one architecture.
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To support its expansion from Homes into Experiences and Services, Airbnb evolved its offline data warehouse architecture to balance domain-specific needs with organizational consistency. Rather than enforcing a single global pattern, data teams adopted a framework of foundational principles that paired separate data models for unique product features with monolithic models for cross-cutting domains like payments and messaging.
Qube Research & Technologies (QRT), a global quantitative investment manager headquartered in London, uses ClickHouse Cloud for a data platform that serves researchers and for near-real-time risk monitoring, management, and P&L. Founded in 2018 as a cloud-native firm after the spinout of Credit Suisse’s proprietary trading arm, QRT chose a cloud-based approach rather than inheriting decades of on-premise infrastructure. The platform supports two major systems, while ClickStack consolidates logs, metrics, and traces for observability; alerts, log queries, and metric aggregations run against a single ClickHouse instance using standard SQL. QRT says its previous database was constrained by writer and reader counts and total cluster load, whereas ClickHouse provides greater speed and scalability, and that a unified monitoring backend simplifies maintenance while remaining reliably fast.
A coding agent built a 3D Paris gallery by chaining two Hugging Face Spaces: an image-generation Space produced dark-background monument images, and VAST-AI/TripoSplat reconstructed each single image as a 3D Gaussian splat. It then handled integration work by correcting the splats’ Y-down orientation, auto-framing monuments, compressing .ply files to .ksplat at about one-third the size, and building and deploying a static Three.js viewer. The Spaces’ agents.md files provided schemas, call and polling templates, file-upload instructions, and authentication guidance, allowing the agent to call them without a client library. The same pipeline produced six-monument galleries for Egypt and Japan after the prompts changed. The account presents callable, documented multimedia components as a way to reduce integration work and make new multimedia applications cheaper to create.
amazon.science
Foundation models deployed as physical AI agents face critical risks when natural language hallucinations translate into physical law violations and operational hazards. To ensure physical consistency and reliability, researchers from the University of California, San Diego and Amazon Fulfillment Technology introduced four grounding pillars. Physics-guided deep learning embeds symmetries and differential equations into pretraining to obey conservation laws while reducing dataset requirements. The UQ4CT framework applies mixture-of-experts architectures to produce calibrated uncertainty estimates, cutting expected calibration error by over 25% and halting operations when safety thresholds are breached. Furthermore, the Adapting-While-Learning framework and formal verifiers like Hilbert integrate numerical simulators and interactive proof systems to enforce mathematical and scientific correctness.
amazon.science
AI agent performance increasingly depends on the harness mediating interactions between language models and tools rather than raw model reasoning. To address mismatches between model intent and harness execution, the authors developed Simple Strands Agent, an open-source single-agent harness. The architecture implements robust tool-interface practices, such as condensing middle log outputs from bash commands, enforcing line-aware string replacements with ambiguity checks, and returning diff files after edits for verification. Evaluated across benchmarks like SWE-Bench-Verified, SWE-Bench-Pro, and Terminal-Bench 2, these model-harness alignments yielded consistent performance gains across frontier models without task-specific tuning. The findings show that model families require tailored interface codesign alongside stable evaluation infrastructure to achieve optimal benchmark reliability.
An enterprise data strategy connects organizational data assets to measurable business outcomes, while fragmented architectures can leave data investments uncoordinated and limit real-time analysis and action. The roadmap starts with purpose, scope, executive sponsorship, measurable objectives, KPI mapping, and use-case prioritization based on business impact, feasibility, time to value, and organizational readiness. It then organizes governance, lifecycle management, data quality, target-state architecture, integration, analytics, team structure, compliance, and measurement as interdependent capabilities, emphasizing owners, stewards, decision rights, executable quality rules, and automated cleansing. Implementation proceeds through a time-boxed cross-functional pilot, documented learnings, and incremental scaling, with steering-committee oversight and governance that evolves through feedback. The text gives indicative timelines of 60 to 90 days for a focused pilot, 12 to 18 months for a foundational platform across multiple business units, and multiple years for a mature data-driven culture.
OpenEnv is becoming a community-coordinated, open-source tool for building agentic execution environments such as terminals and browsers, with the stated goal of helping train open-source agents. Starting now, a committee including Meta-PyTorch, Reflection, Unsloth, Modal, Prime Intellect, Nvidia, Mercor, Fleet AI, Microsoft, Hugging Face, and RadixArk will coordinate the project, which now lives at huggingface/OpenEnv and is supported or adopted by numerous AI organizations. Technically, OpenEnv is being narrowed to an interoperability and protocol layer: environments expose a Gymnasium-style reset(), step(), and state() API through client/server deployments, standard HTTP or WebSocket protocols, Docker packaging, and MCP compatibility. It does not define rewards or training loops; those remain in specialized libraries, while OpenEnv connects harnesses, environments, and trainers across ecosystems. Planned work includes external rewards, dataset-backed tasksets, harness integrations, end-to-end examples, and automatic environment validation.
Airbnb modernized sitar-agent, a Kubernetes sidecar that delivers dynamic configuration updates to thousands of polyglot service instances without requiring redeployments. The architecture uses periodic AWS S3 snapshots to enable fast, decoupled pod startups and maintains local file-based storage for the main container to read configs with in-memory caching. Key design decisions include retaining an isolated sidecar over an in-process library and optimizing pull-based polling with server-side caching.
Real-time analytical databases must serve low-latency queries over data that is continuously inserted at high volume, rather than merely query a static dataset quickly. The article frames time-to-insight as the combination of time to ingest, time to transform and prepare data, and time to query it. It argues that batch-oriented ingestion, delayed visibility, scheduled materialized-view refreshes, and cache invalidation during writes can make production latency far worse than headline benchmarks. It recommends evaluating freshness under concurrent ingest and queries, whether one engine supports structured analytics, text search, vector similarity, and JSON, and whether index maintenance can avoid blocking ingestion. As an example, Bullet reportedly moved to ClickHouse Cloud, reducing data latency by 99.4% to under five seconds, achieving millisecond queries, and handling 1,000× more data at comparable cost.
Retailers face major operational bottlenecks when managing supply chain disruptions through legacy enterprise resource planning systems and siloed documents. Traditional tabular structures struggle to index multi-modal data like images and PDF contracts, delaying critical supplier discovery during regional crises or tariff changes. To overcome these constraints, organizations decouple supplier management into a dedicated operational data layer using MongoDB Atlas and Voyage AI. This architecture stores polymorphic supplier profiles and high-dimensional vector embeddings within a unified collection, enabling natural language semantic search for alternate partners. Real-time updates propagate across systems using MongoDB Change Streams, allowing businesses to rapidly assess supply chain impacts and identify alternative vendors in minutes.
Reachy Mini’s conversation app can now call tools hosted in public Hugging Face Spaces through MCP, allowing capabilities such as weather checks and web search to be added without editing the app or downloading tool code locally. The app retains built-in robot tools and custom local tools, while profiles use tools.txt to control which tools the model may call. The tool-spaces command validates a Space, probes its MCP endpoint, discovers tool IDs, and can append them to a selected profile; remote names are namespaced to avoid collisions. The feature supports public MCP-compatible Gradio Spaces, multiple Spaces, and backend-agnostic registration, but not private or authenticated Spaces, non-Gradio Spaces, arbitrary MCP URLs, or guaranteed parallel orchestration.
Agentic BI uses autonomous AI agents to automate work between raw business data and actionable insight, including data preparation, query execution, chart and narrative generation, and report distribution. Traditional BI depends on analysts to gather data, write queries, maintain dashboards, and assemble reports, while agentic systems let business users ask natural-language questions and receive governed answers. The guide identifies a governed semantic layer as foundational, because shared metric definitions and deterministic execution help keep outputs consistent, auditable, and trustworthy, with human approval checkpoints for higher-risk handoffs. It recommends inventorying data structure, schema drift risk, and integration costs, then piloting a narrowly defined workflow and measuring time to insight, analyst hours reclaimed, satisfaction, and accuracy before expansion.
When historical travel data became uninformative during the asynchronous post-COVID recovery, Airbnb redesigned its demand forecasting to borrow signals across geographies rather than waiting for local data. Using a hierarchical Bayesian framework, the team propagated updated posterior estimates from early-recovering corridors as informative priors for structurally similar, later-recovering corridors. This allowed accurate corridor-level demand forecasting in near real time during periods of high disruption and uneven market reopening.
Uber’s 2025 Airport Awards recognize airports in San Antonio, San Diego, Dallas, and Pittsburgh for improving rideshare reliability, infrastructure, and rider experience. San Antonio International Airport won Quickest Pickup, reporting an average on-demand pickup time of just over four minutes after implementing a nearby staging lot, advanced dispatch technology including ExpressMatch, defined pickup zones, and efficient loading practices. San Diego International Airport and Dallas Love Field received Experience Obsessed recognition for redesigned pickup arrangements, while San Diego added a pedestrian-island setup and secondary staging lot and Dallas replaced a difficult garage location with a terminal-adjacent area. Pittsburgh International Airport won Smartest Space for pull-through loading bays, coordinated signage, Rematch and Prematch technology, and a larger driver waiting lot designed to improve matching, reliability, and pickup speed.
The article presents practical data lakehouse patterns for streaming analytics, IoT pipelines, machine learning workflows, and enterprise reporting, addressing the gap between theoretical definitions and deployable examples. It explains how a lakehouse combines low-cost, schema-flexible object storage with schema enforcement, ACID transactions, data versioning, lineage tracking, and query performance, allowing SQL and ML workloads to use shared open-format tables. Examples include second-level fraud detection, medallion-based historical analytics, predictive maintenance from sensor data, and governed customer 360 profiles. The implementation guidance covers raw storage and partitioning, centralized catalogs, decoupled compute, role-based access, time travel, migration coexistence, SLAs, observability, and lifecycle policies. Together, these patterns are presented as a unified architecture that reduces duplication and data movement while supporting governed analytics at scale.
Data governance architecture is presented as a blueprint for aligning policies, roles, processes, and technologies with business outcomes. It defines objectives including consistent data definitions, data integrity, layered access controls, and secure self-service analytics, while assigning responsibilities across executives, architects, engineers, analysts, managers, and compliance teams. Core principles are accountability, transparency, consistency, and stewardship, supported by federated ownership through councils, data owners, and embedded stewards. The discussion compares DAMA-DMBOK, TOGAF, and Zachman according to organizational scale, regulatory context, and architecture maturity, and describes modern patterns including lakehouse, data mesh, and data fabric. It concludes that effective programs require executive sponsorship, documented roles, measurable quality controls, iterative implementation, and sustained change management.
The guide presents data warehouse design as a business-aligned process for building, modernizing, or scaling analytics infrastructure, rather than merely storing data. It recommends defining analytics use cases and stakeholder needs first, then organizing a three-tier architecture of source, storage, and semantic output layers; cloud designs can decouple compute and storage and use open formats. A Bronze–Silver–Gold medallion flow preserves raw lineage, applies cleansing and deduplication, and produces consumption-ready dimensional models, while retention and archival policies control sprawl. For modeling, it favors star schemas for user-facing BI, uses snowflake normalization when redundancy is material, and stresses explicit fact-table granularity, domain-owned data marts, and workload-specific refresh cadences. Governance and operations include Unity Catalog, access controls, masking, lineage, multi-region deployment, disaster recovery, and CI/CD, followed by phased rollout through high-value domains.
Cloud agents have evolved from local-agent extensions into systems running on dedicated virtual machines, with isolated environments, dependencies, network access, parallel execution, and unattended long-running work. That shift makes the development environment a product: reliable execution increasingly depends on rebuilding tools, VM hibernation and resumption, image checkpointing and forking, integrations, and controlled access to secrets and networks. After a fragile work-stealing design produced roughly one 9 of reliability, the team migrated the agent loop to Temporal, whose retries and scheduling across failures helped exceed two 9s; Temporal now handles more than 50 million actions daily across over 7 million workflows. Decoupling agent-loop, machine, and conversation state enables independent pod lifecycles, asynchronous subagents, and retry-aware streaming, while shorter workflows simplify upgrades. The next goal is self-healing environments that can report and resolve missing secrets, blocked network access, or other obstacles.
The tutorial builds a .NET distributed application around a YARP API gateway, two backend APIs, an analytics API, a Blazor dashboard, a load generator, and ClickHouse. Aspire’s AppHost coordinates these resources, injects connection strings and service endpoints, orders startup with dependency health checks, and exposes selected services through stable or external endpoints. The gateway records proxied requests in ClickHouse while emitting OpenTelemetry metrics for request counts, duration, and backend failures. A bounded queue and background ingestion keep request logging off the hot path, while a narrow schema and materialized view precompute minute-level aggregates for volume, p95 latency, error rates, and route comparisons. Aspire supplies live telemetry, whereas ClickHouse retains request history for analytical queries and drill-down into slow requests.
Enterprise workflows are dynamic, long-running, API- and data-rich, and constrained by policies or regulations, creating challenges that an LLM alone may not handle efficiently. The article defines agent logic as software primitives—including knowledge graphs, algorithms, and program-analysis libraries—within an agent harness that steer models toward relevant workflow context. Examples span legacy COBOL and PL/1 understanding, automated test generation, incident investigation and code remediation, and compliance modernization; reported results include ~30× lower token consumption for application understanding, 20–45% higher test coverage, and up to 4.0× better incident-investigation performance than a ReAct baseline. A Condition Insights example reports 57% fewer unsupported claims and 77% lower average token usage, supporting the conclusion that structured guidance can improve quality, cost, and rule adherence.
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