# Architecture
> 245 posts about Architecture, summarised, each linking to the original.

## Articles

### [Automated Schema Evolution in Pinterest’s Next-Generation DB Ingestion Framework](https://yomu.fyi/post/automated-schema-evolution-in-pinterest-s-next-generation-db-ingestion.md)
- Company: [Pinterest](https://yomu.fyi/company/pinterest.md)
- Author: Pinterest Engineering
- Published: Jun 24, 2026

Pinterest's CDC ingestion platform relies on a multi-stage pipeline built with Kafka, Flink, Spark, and Iceberg, where upstream schema updates risk disrupting data transformations and offline storage. To prevent pipeline failures and schema drift, Pinterest introduced an automated schema evolution framework based on a staged convergence model. Upstream changes are detected via push-based DDL messages and daily pull-based validation checks. The workflow automatically restricts automated updates to additive changes and numeric precision widening, updating Iceberg table definitions and regenerating Flink and Spark code through auditable pull requests. By decoupling schema divergence, code deployment, and data convergence, the system maintains continuous ingestion availability while working toward eventual schema consistency across all storage layers.


### [Your AI shipped a backend that boots. That is the whole problem.](https://yomu.fyi/post/your-ai-shipped-a-backend-that-boots-that-is-the-whole-problem.md)
- Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md)
- Author: Devlin Duldulao
- Published: Jun 23, 2026

Backend security vulnerabilities often emerge from permissive default settings in web frameworks, a risk amplified when AI agents generate code focused solely on passing tests. Common failure points include unmetered request body parsing, unvalidated schemas, prototype pollution, and wildcard cross-origin resource sharing policies. To eliminate these foot-guns, the TypeScript framework DaloyJS enforces secure-by-default behavior across its entire routing and middleware model. It provides built-in request timeouts, streamed body size caps, prototype-stripping JSON revivers, and unified Zod schemas that simultaneously validate payloads and generate OpenAPI contracts. While secure defaults lower the risk of common infrastructure exploits, developers still remain responsible for application-level authorization and business logic correctness.


### [How Netflix Simplified Batch Compute with Kueue](https://yomu.fyi/post/how-netflix-simplified-batch-compute-with-kueue.md)
- Company: [Netflix](https://yomu.fyi/company/netflix.md)
- Author: Netflix Technology Blog
- Published: Jun 22, 2026

Netflix transitioned its managed batch compute infrastructure from a homegrown solution called Compute Managed Batch to Kueue on its Titus container platform. CMB previously relied on custom scheduling and admission-only fair sharing without preemption, making feature development cumbersome as the Kubernetes ecosystem evolved. To modernize the platform transparently, Netflix mapped internal tenants to Cohorts and leaf tenants to ClusterQueues and LocalQueues while routing jobs through a custom Kueue router. Kueue operates alongside existing Titus scheduling profiles rather than replacing the kube-scheduler, preserving cluster placement efficiency. The migration was completed in four weeks across millions of batch workloads, significantly increasing average resource utilization through preemption-based fair sharing.


### [Palana (Part 2): Architecting isolation, identity, and auditability for AI agents](https://yomu.fyi/post/palana-part-2-architecting-isolation-identity-and-auditability-for-ai.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Kevin Littlejohn
- Published: Jun 21, 2026

Grab's Palana platform provisions isolated, Kubernetes-native runtime environments for autonomous AI agents using dedicated per-agent namespaces and role-based access controls. The architecture separates network enforcement across layers, applying Layer 3 and Layer 4 containment with Cilium and NetworkPolicy alongside Layer 7 application filtering evaluated by Open Policy Agent. Agent interactions with large language models route through a LiteLLM proxy wrapper that retrieves credentials from HashiCorp Vault based on Kubernetes pod context rather than client headers. Secrets management is divided between directly readable agent paths and proxy-only placeholder paths that prevent raw tokens from residing in runtime filesystems. An automated reaper monitors multi-source activity signals to shut down idle compute resources while preserving persistent storage and configuration state.


### [Dispatches from O'Reilly: From capabilities to responsibilities](https://yomu.fyi/post/dispatches-from-o-reilly-from-capabilities-to-responsibilities.md)
- Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md)
- Author: Artur Huk
- Published: Jun 19, 2026

High-stakes AI agents capable of mutating external state often face governance failures when relying on system prompts or manual Human-in-the-Loop approval queues that quickly degrade into alert fatigue. The Responsibility-Oriented Agent architecture addresses this operational bottleneck by shifting system design from open-ended capability framing to deterministic, contract-enforced responsibilities. Under this model, underlying orchestration frameworks like LangChain operate in User Space with their side-effecting tools removed, isolating the agent to epistemic reasoning. The agent expresses its intended action exclusively by emitting a structured policy proposal to a privileged Kernel Space runtime. The runtime deterministically evaluates the proposal against versioned YAML contracts registered in an agent registry, ensuring that only genuine policy exceptions are escalated to human supervisors.


### [Palana (Part 1): Why Grab built a secure platform for autonomous AI Agents](https://yomu.fyi/post/palana-part-1-why-grab-built-a-secure-platform-for-autonomous-ai-agent.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Kevin Littlejohn
- Published: Jun 19, 2026

Autonomous AI agents introduce significant operational and security risks when granted network access, persistent state, and credentials. To address these concerns without impeding developer productivity, Grab created Palana, an in-house Kubernetes-native execution substrate. The platform isolates each agent workload within its own namespace, pairing it with dedicated storage, network policies, and role-based access control. Network egress is funneled through an Envoy and Open Policy Agent proxy layer that audits requests and injects credentials from HashiCorp Vault using placeholder tokens, keeping raw secrets outside the agent runtime. This design allows Grab to securely host hundreds of long-running workflows, remote coding environments, and automation bots.


### [AI agents are a confused deputy with the keys to your kingdom](https://yomu.fyi/post/ai-agents-are-a-confused-deputy-with-the-keys-to-your-kingdom.md)
- Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md)
- Author: Fabio Salvadori
- Published: Jun 17, 2026

Attackers recently compromised over twenty thousand Instagram accounts by manipulating Meta's AI support assistant to rebind recovery email addresses without verifying account ownership. This incident illustrates the classic confused deputy security problem, where a privileged process is persuaded by an unprivileged user to perform unauthorized operations. Because large language model interfaces operate purely on natural language and cannot distinguish instructions from data, the model itself cannot serve as an authorization boundary. Securing AI agents requires verifying caller identity through external policy checks against authenticated sessions rather than relying on chat context or prompt engineering. Teams must enforce least privilege with short-lived scoped credentials, place irreversible actions behind hard policy gates or human approvals, and maintain audit trails of agent actions.


### [Metric Semantic Layer: How Lyft Governs and Scales Key Data Definitions](https://yomu.fyi/post/metric-semantic-layer-how-lyft-governs-and-scales-key-data-definitions.md)
- Company: [Lyft](https://yomu.fyi/company/lyft.md)
- Author: Iraklikhorguani
- Published: Jun 10, 2026

As Lyft scaled, different teams developed conflicting definitions for key business metrics due to the lack of centralized version control and shared standards. To resolve this, Lyft implemented an internal Metric Semantic Layer as a Python package that stores authoritative metric definitions in YAML files with Jinja SQL templates. The system restricts onboarding to Golden Metrics used across multiple applications, requiring team-based approval from both Business and Operational Owners for any definition changes. Standardized definitions are exposed through Python APIs, integrated into the Amundsen data catalog and self-service user interfaces, and surfaced to AI tools via a Model Context Protocol.


### [Graviton5’s improved design increases speed and energy efficiency — beyond Moore’s law](https://yomu.fyi/post/graviton5-8217-s-improved-design-increases-speed-and-energy-efficiency.md)
- Company: [Amazon](https://yomu.fyi/company/amazon.md)
- Author: Ali Saidi
- Published: Jun 10, 2026

Amazon announced the general availability of M9g and M9gd EC2 instances powered by the new Graviton5 processor. Built on a three-nanometer process, Graviton5 features 192 Neoverse V3 cores, DDR5-8800 memory support, PCIe gen6 interconnects, and 192 megabytes of level-three cache. The chip transitions from seven discrete dies to four unified chiplets connected at 420 gigabytes per second, eliminating dedicated I/O and memory controller dies. These architecture updates deliver up to 25% higher computational performance over Graviton4, with up to 35% faster performance for web applications and machine learning inference. The new instances also introduce the Nitro Isolation Engine, which uses formal verification to enforce hardware and software isolation between virtual machines.


### [Scaling beyond one: How Airbnb evolved its data architecture for a multi-product world](https://yomu.fyi/post/scaling-beyond-one-how-airbnb-evolved-its-data-architecture-for-a-mult.md)
- Company: [Airbnb](https://yomu.fyi/company/airbnb.md)
- Author: Patrick Lam
- Published: Jun 9, 2026

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.


### [Real-world grounding in agentic AI](https://yomu.fyi/post/real-world-grounding-in-agentic-ai.md)
- Company: [Amazon](https://yomu.fyi/company/amazon.md)
- Author: Rose Yu
- Published: Jun 8, 2026

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.


### [Bridging intent and execution in agentic systems](https://yomu.fyi/post/bridging-intent-and-execution-in-agentic-systems.md)
- Company: [Amazon](https://yomu.fyi/company/amazon.md)
- Author: Gaurav Gupta, Vatshank Chaturvedi
- Published: Jun 8, 2026

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.


### [Sitar-agent: Building a reliable dynamic configuration sidecar at scale](https://yomu.fyi/post/sitar-agent-building-a-reliable-dynamic-configuration-sidecar-at-scale.md)
- Company: [Airbnb](https://yomu.fyi/company/airbnb.md)
- Author: Bo Teng
- Published: Jun 4, 2026

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.


### [When history fails you, borrow from geography](https://yomu.fyi/post/when-history-fails-you-borrow-from-geography.md)
- Company: [Airbnb](https://yomu.fyi/company/airbnb.md)
- Author: Harrison Katz
- Published: Jun 2, 2026

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.


### [From decentralized Docs-as-Code to a centralized repository: Evolving Grab's documentation strategy](https://yomu.fyi/post/from-decentralized-docs-as-code-to-a-centralized-repository-evolving-g.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Karen Kue
- Published: May 29, 2026

Grab originally adopted a decentralized Docs-as-Code workflow to manage Markdown documentation alongside service code, but the model strained as engineering scaled. Dispersed repositories led to fragmented terminology, uncoordinated repository moves, uneven linting standards, and noisy search results. To resolve these issues, the organization transitioned to a centralized documentation repository with standardized templates, automated CI quality gates, and a unified search overlay combining fuzzy title search with Glean. Consolidating Markdown into a single repository also established a clean, structured knowledge base tailored for internal AI agents. The centralized setup fostered continuous maintenance, averaging 27 daily commits and an update merged roughly every 50 minutes.


### [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

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](https://yomu.fyi/company/amazon.md)
- Author: Giacomo Bernardi, Ratul Mahajan, Seshadhri Comandur
- Published: May 28, 2026

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%.


### [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

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

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.


### [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

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.


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