---
title: "Architecture"
description: "834 posts about Architecture, summarised, each linking to the original."
---

# Architecture
> 834 posts about Architecture, summarised, each linking to the original.

## Articles

### [Enterprise Business Software and the Mixed-Up Chameleon Problem](https://yomu.fyi/post/enterprise-business-software-and-the-mixed-up-chameleon-problem.md)
- Company: [Palantir](https://yomu.fyi/company/palantir.md)
- Author: Palantir
- Published: May 29, 2026
- Type: Explainer

Traditional ERP practice prioritizes a standardized, stable core, but the post argues that forcing every process into it can erode an organization’s identity and differentiated ways of creating value. It proposes keeping the ERP as the system of record while placing an interoperability layer above it. This layer uses a shared ontology to reconcile concepts across systems and supports workflows, automation, user experiences, agentic AI, and human engineering under shared security and governance. Because these capabilities remain outside the core, organizations can preserve distinctive processes during ERP upgrades and treat migration as a continuous software- and ontology-driven activity rather than a one-time manual exercise. The intended model is structured adaptability: standardized ERP functions retain discipline, auditability, and financial integrity while operational software adapts to the business.


### [Winning under CMS TEAM: Building the learning health system to realize success in VBC today and tomorrow](https://yomu.fyi/post/winning-under-cms-team-building-the-learning-health-system-to-realize.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Kayla Grieme, Benjamin Goldsteen, Shankara Ettigi
- Published: May 29, 2026
- Type: Problem & solution

CMS’s Transforming Episode Accountability Model (TEAM), beginning January 1, 2026, requires selected organizations to manage total cost and quality across five surgical episode categories and a 30-day post-discharge window. The post argues that retrospective dashboards and delayed claims data cannot support intervention or prepare hospitals for downside risk, especially amid complex integration across EHR, claims, post-acute, social-determinants, and provider data. It proposes a modern, cloud-native lakehouse foundation with unified data, operationalized AI/ML, embedded clinical workflows, and scalable architecture. Recommended capabilities include risk stratification, next-best-action decision support, post-acute optimization, provider benchmarking, and predictive performance alerts. In a composite 500-bed medical-center example, the approach is associated with 15% lower SNF costs, 8% better risk-capture completeness, and 12% fewer readmissions.


### [From Rigid Rules to Empathetic Agents: Rebuilding Swiggy’s Order Cancellation Experience with LLMs](https://yomu.fyi/post/from-rigid-rules-to-empathetic-agents-rebuilding-swiggy-s-order-cancel.md)
- Company: [Swiggy](https://yomu.fyi/company/swiggy.md)
- Author: Aishwary Yadav
- Published: May 29, 2026
- Type: Problem & solution

Swiggy rebuilt its “I Want To Cancel” (IWTC) support journey because a roughly 150K-line deterministic decision tree treated cancellation keywords alike, repeated canned deterrence, and could leave users stuck without handoff. The replacement is a stateful, multi-turn framework orchestrated with LangGraph: an intent layer identifies needs such as delay help, coupon support, direct cancellation, or human access, while live order signals and deterministic policy checks determine the next action. Full graph state is checkpointed in Redis by order- and session-level thread IDs, allowing conversations to resume across turns. Swiggy evaluated intent reliability using more than 100k historical chats and a pre-deployment suite of about 500 representative queries, then operated the system with reporting and alerts for behavioral and service metrics. The conclusion favors a hybrid design in which LLMs handle language while deterministic systems anchor truth and business decisions.


### [Compliance versus conversion: Tax ID validation at checkout](https://yomu.fyi/post/compliance-versus-conversion-tax-id-validation-at-checkout.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Jake Estes
- Published: May 29, 2026
- Type: Explainer

Tax ID collection at checkout supports compliant tax invoices and tax determination, including reverse-charge treatment for qualifying cross-border business transactions. Validation can be performed synchronously before purchase completion or asynchronously after checkout: synchronous checks reduce correction and audit risk but may increase abandonment and expose conversion to government-service downtime, while asynchronous checks preserve completion but require follow-up on failures. The implementation discussion covers direct national-registry integrations, third-party validation services, and Stripe’s CheckoutSessions and PaymentIntents integrations, where the Tax ID Element uses verification modes of if\_supported or none. Stripe first applies a country-specific format check, routes validly formatted IDs to government APIs, may fall back to asynchronous processing after four seconds, and exposes status through customer.tax\_id.updated and the customer object.


### [How enterprise leaders are scaling AI agents across their organization](https://yomu.fyi/post/how-enterprise-leaders-are-scaling-ai-agents-across-their-organization.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Christy Maver, Aly McGue
- Published: May 28, 2026
- Type: Explainer

Executives from Danone, Capital One, Warner Bros. Discovery, Ford Credit, Gilead Sciences, and Databricks discuss scaling AI agents for measurable business value while maintaining governance, trust, and cost control. They describe deployment as only the first step, followed by monitoring, observability, performance assessment, and continuous learning, with risk reviews and ongoing evaluation integrated into the agent lifecycle. Organizations are shifting from single-task automation toward specialized multi-agent orchestration for complex workflows, while shadow capabilities and controlled sandboxes test accuracy without exposing live operations. Leaders recommend building momentum through low-risk wins such as Capital One’s Chat Concierge, training employees to prompt, and providing natural-language interfaces, certified data, consistent guardrails, and a secure architecture for deployment and monitoring.


### [Slack AI: The Path to Multi-Cloud](https://yomu.fyi/post/slack-ai-the-path-to-multi-cloud.md)
- Company: [Slack](https://yomu.fyi/company/slack.md)
- Author: Shaurya Kethireddy
- Published: May 28, 2026
- Type: Problem & solution

Slack evolved its Slack AI serving infrastructure across multiple phases to handle enterprise LLM workloads reliably and securely. The initial deployment on AWS SageMaker provided zero-knowledge escrow VPC isolation and FedRAMP compliance, but engineers faced scaling latency, GPU scarcity, and significant operational overhead. Slack then migrated live traffic to Amazon Bedrock to leverage managed Model Units and eliminate model release lag without customer-facing incidents. However, fixed Provisioned Throughput commitments and regional peak traffic variations created persistent underutilization challenges. Consequently, Slack expanded into a multi-cloud orchestration architecture that normalizes disparate provider APIs, integrates unified cross-cloud telemetry, and routes traffic dynamically around latency spikes and outages.


### [How flat is replacing fat in AWS data center networks](https://yomu.fyi/post/how-flat-is-replacing-fat-in-aws-data-center-networks.md)
- Company: amazon.science
- Author: Giacomo Bernardi, Ratul Mahajan, Seshadhri Comandur
- Published: May 28, 2026
- Type: Problem & solution

Traditional data center networks rely on hierarchical fat-tree topologies, which introduce overhead, upper-layer congestion, and vulnerability to router failures. Although flat random networks theoretically maximize route diversity and resilience, routing computation and cabling complexity historically made them impractical for physical data centers. AWS addressed these constraints by introducing Resilient Network Graphs, a quasi-random architecture powered by a passive optical device called ShuffleBox and a routing algorithm named Spraypoint. Spraypoint sprays packets across random neighbors toward designated waypoints, unlocking twice as many independent paths while operating within commodity router memory limits. Adopted as the default architecture across new AWS facilities, this design reduces router counts by 69%, lowers projected network power consumption by 40%, and increases throughput by up to 33%.


### [Under the River](https://yomu.fyi/post/under-the-river.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: May 28, 2026
- Type: Explainer

Shopify describes how its AI agent, River, grew from an infrastructure bet aimed at making a large, fragmented codebase usable by AI. In early 2024, the company adopted a monorepo called World and Nix as a reproducible substrate for development, CI, and production, despite substantial migration costs. River operates in public Slack channels, where it reads code, runs tests, queries data, inspects production traces, and opens pull requests; in a recent 30-day period it handled 59,918 sessions and contributed to 3,536 merged pull requests. Underneath it, Aquifer provides durable sessions, sandboxes, gateways, event logs, credentials handling, and observability, allowing agent profiles to share one platform across interactive, automated, and batch modes. The central design principle is that the session must survive process and machine failure, while public transcripts create a compounding knowledge corpus.


### [How the lakebase architecture stays resilient to cloud failures](https://yomu.fyi/post/how-the-lakebase-architecture-stays-resilient-to-cloud-failures.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Jasraj Dange, Hans Norheim
- Published: May 27, 2026
- Type: Explainer

Lakebase is adapting its reliability architecture to agentic and on-demand workloads, which create far more control-plane activity, database starts, and pressure on finite cloud capacity. Lakebase uses stateless Postgres compute over zone-resilient storage, dedicated multi-availability-zone computes for optional high availability, and a vertically autoscaling virtualization layer that reduces dependence on cloud provisioning control planes. A dedicated data-plane controller is being developed for hot-path start and suspend operations, while cell-based regional deployments isolate failures and allow capacity to expand incrementally. The reliability program also uses component and whole-availability-zone failure injection, consistency checks, Service Level Indicators, and Service Level Objectives, including startup, failover, storage, and API measurements. The stated target is for every database to exceed 99.99% monthly availability; reported 2026 attainment through April shows 99.75% to 99.85% of databases meeting that threshold.


### [Announcing Lakebase Change Data Feed (CDF)](https://yomu.fyi/post/announcing-lakebase-change-data-feed-cdf.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Pranav Aurora, Cheng Chen, Hristo Stoyanov
- Published: May 27, 2026
- Type: Announcement

Lakebase Change Data Feed (CDF) is available in Public Preview to reduce the manual effort of moving data from operational databases into downstream systems. The feed is enabled once for all tables in a project, stored and governed in Unity Catalog Managed Tables, and readable by engines, models, and agents without separate extraction pipelines. From one shared feed, teams can build streaming pipelines with SDP, create materialized views with DBSQL, or compute and store embeddings with Agent Bricks, while consumers remain isolated from the primary operational workload. The announcement positions Lakebase as the native Bronze layer in a medallion architecture, complementing Synced Tables and providing governance and lineage across the data lifecycle.


### [Shipping a Trillion Parameters With a Hub Bucket: Delta Weight Sync in TRL](https://yomu.fyi/post/shipping-a-trillion-parameters-with-a-hub-bucket-delta-weight-sync-in.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Amine Dirhoussi, Quentin Gallouédec, Kashif Rasul, Lewis Tunstall, Edward Beeching, Albert Villanova del Moral, Leandro von Werra, Sergio Paniego
- Published: May 27, 2026
- Type: Problem & solution

Async RL weight synchronization currently requires the trainer to send a complete model to the inference engine after each optimizer step, creating a critical-path transfer that can reach roughly a terabyte for a frontier 1T-parameter checkpoint. The article attributes the opportunity for reduction to bf16 arithmetic: at RL learning rates, many updates fall below the bf16 visibility threshold, leaving about 99% of weights bit-identical between steps and more than 98% even in the reported worst case. TRL's delta-weight-sync implementation records changed elements around the optimizer step, encodes them in sparse safetensors, uploads each delta to a Hugging Face Bucket, and lets vLLM fetch and apply it. On Qwen3-0.6B, this reduces each per-step payload from 1.2 GB to 20–35 MB, while a demonstrated setup connected separate trainer, vLLM, and Wordle Spaces through one bucket. Open issues include extra CPU bf16 snapshots, fixed anchor cadence, and unmeasured multi-node FSDP2 support.


### [Introducing House Mates: the ClickHouse partner community and program](https://yomu.fyi/post/introducing-house-mates-the-clickhouse-partner-community-and-program.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: May 27, 2026
- Type: Announcement

ClickHouse introduces House Mates, a partner community and program bringing together more than 60 ISV, consulting, services, and channel partners to help customers build, scale, and procure ClickHouse solutions. The inaugural cohort includes over 25 ISVs developing validated, production-ready integrations—among them dbt Labs, Fivetran, Temporal, Grafana Labs, Confluent, and Sigma—and over 35 consulting, services, and reseller partners trained and certified through ClickHouse Academy. The program addresses an ecosystem with more than 200 integrations in the wild, demand for trusted regional expertise and preferred procurement channels, and the limits of managing partnerships through one-off arrangements. Its Ignite, Accelerate, and Prime tiers define progression from enablement and first projects or integrations to deeper go-to-market, incentives, co-innovation, and strategic collaboration at scale, while customers gain validated tools, regional experts, and preferred purchasing routes.


### [Open House 2026 Day 1: Real-time data without lock-in and what teams can build next](https://yomu.fyi/post/open-house-2026-day-1-real-time-data-without-lock-in-and-what-teams-ca.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: May 27, 2026
- Type: Announcement

ClickHouse’s Open House 2026 Day 1 announced updates spanning ClickHouse Cloud, Postgres, distributed query execution, observability, AI agents, and lakehouse interoperability. ClickHouse Postgres entered public beta with local NVMe storage, high availability, S3-backed point-in-time recovery, and a native CDC path into ClickHouse; early benchmarks reported more than 5x the transactions per second of AWS RDS. Multi-stage distributed queries repartition intermediate data between stages to scale joins and high-cardinality aggregations, reducing TPC-H SF100 runtime from 117.6 seconds on one node to 54.7 seconds on eight nodes. Additional launches included a Claude-powered ClickHouse Agents public beta, an AI-enhanced query builder, Monitoring v2, and Langfuse V4’s reported 200x query performance improvement, while the wrap-up framed the releases around real-time systems without lock-in.


### [Postgres managed by ClickHouse is now in beta](https://yomu.fyi/post/postgres-managed-by-clickhouse-is-now-in-beta.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Sai Srirampur
- Published: May 27, 2026
- Type: Announcement

ClickHouse Cloud has launched a fully managed Postgres service in beta to combine transactional and analytical workloads within a single platform. Backed by local NVMe storage, the service provides up to ten times faster transactional performance alongside native change data capture into ClickHouse. Developers can query across both transactional and analytical layers using the pg\_clickhouse extension without managing custom data pipelines. The platform also offers managed migration workflows via ClickPipes, database branching, point-in-time recovery, high availability with up to two standbys, and support for over ninety PostgreSQL extensions. More than fifty virtual machine configurations are available, ranging from single vCPU instances to clusters with ninety-six vCPUs and sixty terabytes of NVMe storage.


### [Introducing multi-stage distributed query execution in ClickHouse Cloud](https://yomu.fyi/post/introducing-multi-stage-distributed-query-execution-in-clickhouse-clou.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Alexander Gololobov
- Published: May 27, 2026
- Type: Announcement

ClickHouse Cloud introduced multi-stage distributed query execution to improve scaling for large joins and high-cardinality aggregations across multi-node clusters. Prior execution models relied on physical sharding or parallel replicas, which lacked the ability to freely repartition intermediate data between query stages. By splitting query plans into stages connected by exchange operators, worker nodes can shuffle, broadcast, scatter, or gather intermediate data during execution. In TPC-H benchmarks at scale factor 100 on eight nodes, total query time dropped from 117.6 seconds to 54.7 seconds, with scan-dominated queries showing near-linear 7.4x speedups. While currently in private preview with a rule-based planner, future work includes developing a cost-based optimizer to select optimal aggregation and join strategies automatically.


### [You don’t need to pick one: how Sentry and OpenTelemetry work together](https://yomu.fyi/post/you-don-t-need-to-pick-one-how-sentry-and-opentelemetry-work-together.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: Lazar Nikolov
- Published: May 27, 2026
- Type: Explainer

Integrating Sentry into systems already instrumented with OpenTelemetry does not require replacing existing backend SDKs or rewriting service instrumentation. A hybrid architecture pairs the Sentry SDK on the frontend for browser tracing, Session Replay, and logs with existing OpenTelemetry configurations across backend services. Frontend requests propagate W3C traceparent headers to backend endpoints, which then export traces and logs over OTLP directly to Sentry endpoints or through an intermediate OpenTelemetry Collector. Ingested telemetry attaches backend spans and standard Python logs to the initial frontend user actions, establishing a unified distributed trace across the entire request path. Although Sentry's OTLP ingest currently supports logs and traces rather than metrics, dedicated backend Sentry SDKs remain an optional addition later for backend exception tracking and profiling.


### [Beyond the Menu Tree: How Yelp Built a Smarter Customer Success Chatbot with AI](https://yomu.fyi/post/beyond-the-menu-tree-how-yelp-built-a-smarter-customer-success-chatbot.md)
- Company: [Yelp](https://yomu.fyi/company/yelp.md)
- Author: Lina Lee, Machine Learning Engineer; Nelson Lee, Engineering Manager
- Published: May 27, 2026
- Type: Problem & solution

Yelp transitioned from a legacy customer support chatbot that relied on rigid phrase matching and static menus to a retrieval-augmented generation system. The updated architecture classifies incoming queries using a large language model to route users into one of five specialized workflows, including QA, Billing, Refund, Cancel, and Review. For general QA, the system uses FAISS to search an in-memory vectorstore built exclusively from article metadata such as titles, summaries, and headers rather than raw article text. Retrieved articles are supplied to the language model as prompt context, followed by automated validations for safety, character limits, and hallucinated hyperlinks. In production A/B testing, this metadata-driven design doubled the chatbot resolution rate while achieving approximately 94% recall@5.


### [What it feels like building with Stripe Projects](https://yomu.fyi/post/what-it-feels-like-building-with-stripe-projects.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Ben Smith
- Published: May 26, 2026
- Type: Explainer

The post recounts a 60-minute attempt to build a GitHub traffic dashboard with Stripe Projects, motivated by GitHub’s 14-day limit on traffic history. Using Cursor’s agentic chat workflow alongside Projects, the builder assembled a Next.js application with Auth0 authentication, Supabase persistence, Prisma schema management, Vercel hosting, scheduled jobs, environment wiring, and periodic GitHub traffic synchronization. The experiment largely avoided direct coding and instead tested whether AI-mediated orchestration could produce a production-shaped stack quickly. As services and credentials appeared across providers, the main difficulty shifted from implementation to understanding ownership, deployment topology, environment management, and operational flow. The repositories synced, charts populated, and historical data appeared, while the experience also exposed a need for clearer generated architecture or infrastructure-as-code descriptions of the assembled system.


### [Harness, Scaffold, and the AI Agent Terms Worth Getting Right](https://yomu.fyi/post/harness-scaffold-and-the-ai-agent-terms-worth-getting-right.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Sergio Paniego, Aritra Roy Gosthipaty
- Published: May 25, 2026
- Type: Explainer

AI-agent terminology has outpaced shared definitions, leaving terms such as harness and scaffold blurred across frameworks and contexts. The glossary separates the model, which produces text but has no memory or execution loop, from scaffolding, which defines instructions, tools, output parsing, and context management, and the harness, which runs model calls, routes tool use, and decides when to stop. It presents an agent as a model plus the surrounding system that lets it act repeatedly, while distinguishing tools, skills, sub-agents, and orchestrators by their roles. For training, it defines the environment, trainer, rollout, and reward, including verifiable or learned scores and sparse or dense feedback. Because usage is not universal, the glossary offers a practical mental model rather than enforcing one vocabulary.


### [AI as the Next Abstraction Layer: How I see engineering evolving at Thumbtack](https://yomu.fyi/post/ai-as-the-next-abstraction-layer-how-i-see-engineering-evolving-at-thu.md)
- Company: [Thumbtack](https://yomu.fyi/company/thumbtack.md)
- Author: Ananda Kanagaraj Sankar
- Published: May 22, 2026
- Type: Explainer

Thumbtack is transitioning its engineering workflow from ad-hoc tool experimentation to end-to-end agentic software development. Unlike previous deterministic abstractions such as compilers or high-level languages, artificial intelligence introduces non-determinism where identical prompts can produce varying code and trade-offs. Because consumer marketplace experiments still require weeks for behavioral signals to stabilize, accelerated implementation primarily helps teams prepare cleaner experiment designs and parallelize hypotheses within experimentation bandwidth constraints. Consequently, day-to-day engineering responsibilities shift upstream from manual coding toward problem framing, system architecture, validation, and co-designing features with product managers. Teams maintain technical sharpness by rigorously reviewing generated code while accepting that long-term specialization above the abstraction layer will become standard across the organization.


[Newer posts](https://yomu.fyi/topic/architecture/page/17.md) · [Older posts](https://yomu.fyi/topic/architecture/page/19.md)