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Problem & solution reads
Problem & solution posts on Yomu, summarised newest first.
- 891 posts
- 20 companies
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Problem & solution posts
How ERGO Hestia reduced time-to-market with Databricks Lakebase and Model Serving
ERGO Hestia redesigned its real-time pricing platform to reduce deployment friction across more than 100 models and 1,000 variables while preparing B2C capabilities. Previously, processed data moved from Databricks through extraction jobs, external Azure PostgreSQL, and a custom caching adapter, creating governance overhead, deployment coordination, and latency spikes during large refreshes. The new architecture uses Lakebase Sync Tables as an online serving layer and Databricks Model Serving Endpoints, keeping data, request logic, and model serving within the lakehouse; Unity Catalog supplies lineage, version tracking, access controls, and audit trails. An incremental migration started with a low-criticality endpoint, measuring 20ms latency and less than 5% CPU utilization at 40 requests per second, before expanding toward larger workloads and the planned decommissioning of PostgreSQL.
How Ecolab rebuilt retail intelligence on Databricks and Anthropic Claude
Ecolab needed to combine audits, health inspections, pest telemetry, and other data from nine systems so retail teams could answer location-specific compliance questions. Its Retail Intelligence application is a native Databricks App using Lakebase Postgres, Lakeflow, and Spark Declarative Pipelines to move governed data into a Unity Catalog lakehouse, while Foundation Model APIs serve Claude Sonnet, Claude Haiku, and Gemini. A Coordinator Agent delegates requests to specialized agents that use Vector Search, SQL, Unity Catalog Functions, and an external MCP server; a Response Agent returns cited answers, with short- and long-term memory stored through Lakebase. The system also applies five Judge LLMs, MLflow tracing, and ai_query() batch inference. Report preparation fell from two weeks to under two minutes, while the assistant supports approximately twelve languages at about 98% accuracy.
How to make the case for giving your AI Agent system access
AI Agents can answer support questions without backend access, but they cannot complete changes such as payment-plan updates, claims, or account edits. Connecting an Agent to CRM, billing, order-management, or other systems moves support from explanation to end-to-end resolution, and the source frames that shift as the basis for an engineering request. Data from a 2026 Customer Service Transformation Report shows improved metrics among 87% of teams with mature AI deployment, versus 62% overall, while only 10% report reaching that stage; an internal comparison found Procedures with system access produced the largest gains in workflows requiring judgment, branching, live data, or handoffs. The recommended case starts with one high-volume, repeatable workflow, maps its data and actions, and defines a narrow pilot. Teams can then progress from no integration to read-only access and finally write actions, using measured results to justify further work.
Metric Semantic Layer: How Lyft Governs and Scales Key Data Definitions
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.
AI Serving Platform That Adapts to Your Model
Databricks Custom Model Serving addresses the operational burden of serving custom models, whose resource profiles, traffic patterns, and latency requirements vary widely from small CPU classifiers to large GPU-backed language models. Its fully managed platform packages MLflow models and uses isolated Kubernetes deployments, model-appropriate runtimes, and a short request path to limit interference and per-request overhead. At the center, the AutoPilot Pod Autoscaler combines active concurrency and queue signals for horizontal scaling with CPU, GPU, and memory measurements for model-aware target-concurrency adjustment, allowing one controller to adapt across workloads. Warm pools, provisioned concurrency, and zero-downtime updates address startup and deployment concerns, while reported production results include 90%+ cost savings for some customers, up to 2x improvement in p99 and p50 latency, 100K+ QPS, and 99.99% availability.
Encoding Your Domain Expert: The Context Layer Behind Spotify's Data Assistant
Spotify developed an AI data assistant, Vedder, to scale access to over 70,000 internal datasets without overwhelming domain experts. Because raw warehouse schemas omit critical semantic nuances and overwhelm LLM context windows, engineers implemented a domain-owned context layer organized into clusters. Each cluster contains profiled datasets, expert-curated business documentation, and vetted question-and-SQL pairs that guide a ReAct-based agent. Automated query history extraction proved insufficient on its own, as domain curators accepted only 12.5% of inferred query pairs due to noise. Deployed across Slack, IDEs, and a dedicated web interface, the system maintains reliability through continuous cluster health monitoring that flags schema drift and degraded examples.
Modern BSA/AML compliance on Databricks
AML operations are strained by fragmented systems, high false-positive volumes, manual case documentation, and opaque vendor scoring, leaving analysts focused on backlog rather than financial-crime intelligence. The proposed Databricks Data + AI Platform unifies transaction monitoring, KYC, sanctions, case history, and policy data under Unity Catalog, using Lakeflow Connect and a Bronze–Silver–Gold Delta architecture with masking, row-level security, and lineage. MLflow, Model Serving, Lakehouse Monitoring, and inference tables support institution-specific detection models, while Agent Bricks coordinates agents for evidence gathering, recommendations, and SAR drafting with analysts retaining final decisions. The architecture also uses Lakebase for governed operational state and Databricks Apps for analyst and executive experiences. Reported outcomes include a 75% reduction in false positives reaching the analyst queue and compressing three-to-six-hour investigations to minutes.
From Chaos to Clarity: How We Built a Unified, Self-Routing Support Ops Ticketing System at Lyft
Lyft Urban Solutions' Support Ops team faced fragmented ticketing across four Jira Help Centers with duplicate intake forms, lack of routing logic, and no dashboards. Over five years, the team redesigned the intake architecture by replacing redundant forms with dynamic Jira Proforma forms featuring conditional branching. Automated Jira rules were introduced to assign tickets, apply a taxonomy of over 30 labels, and trigger PagerDuty alerts for P0 incidents. To unify operations across disparate backend projects, the system automatically clones and links tickets from a single portal to downstream project boards. Jira Structures and a Jira-to-Mode ETL pipeline provided cross-project rollups and business analytics before an upcoming Jira Cloud migration required dashboard rebuilds.
amazon.science
Real-world grounding in agentic AI
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.
Transforming solar and wind maintenance reports with Genie and AI agents
Plenitude and Databricks built an agent-based system that turns solar and wind plant maintenance PDFs into structured data for cross-plant analysis. Event-driven ingestion uses Databricks Jobs and the ai_parse_document AI Function to extract text, tables, figures, and metadata, then stores page- and object-level JSON records in Delta Lake with coordinates, version history, and links to source reports. A Genie space uses Unity Catalog metadata, knowledge-store instructions, and SQL generation to answer natural-language questions, produce visualizations, and export results, while Agent Bricks can orchestrate multi-step workflows and downstream actions. The design also applies automatic liquid clustering to dynamic queries and row-level security to restrict results by country. The resulting data layer supports historical trends, plant comparisons, recurring-fault analysis, and a foundation for predictive maintenance, although the source frames predictive use as a future improvement.
amazon.science
Bridging intent and execution in agentic systems
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.
Works on my machine: how we use AI to reproduce reported bugs
Sentry maintains 159 open-source SDK packages across multiple languages, leading to high triage volume and significant boilerplate setup overhead when reproducing reported bugs. To streamline this workflow, the team developed a Claude skill that parses GitHub issue URLs to extract language, framework, and SDK versions. The skill generates a reproduction branch, sets up minimal boilerplate using ecosystem package managers like uv or npm, attempts execution, and opens a pull request with explicit run instructions. By allowing the model to exit early when encountering overly complex environments, the tool reliably prepares reproducible playgrounds without stumbling on edge cases. This workflow saved several hours in testing scenarios like Django HTTPS proxy bugs while avoiding excessive bot noise through scoped human interaction.
Building Agents that Don't Break Themselves
Agents that call shell commands can damage their host, so the post separates the agent’s durable home from the environment where untrusted code executes. It presents Sprites as disposable, isolated sandboxes, with one Sprite per session for the multi-user SpriteDoc and one persistent Sprite per task for Hermes Agent. SpriteDoc injects each user’s flyctl credential only for the command that needs it, avoiding token storage while preserving the user’s permissions; Hermes can omit approval prompts because the sandbox protects the host. The same separation applies when Hermes itself runs inside a Sprite: its commands are dispatched to another Sprite with a different identity and boot. Copy-on-write checkpoints provide an undo path, restoring deleted files and tools in about nine seconds.
Building a File Semantic Analyzer: Guarding Outbound Data at Scale with AI
Organizations struggled to determine whether files leaving their environment contained business-critical information, personal data, or non-essential content. Uber’s File Semantic Analyzer (FSA) addresses the limits of keyword- and pattern-based DLP by extracting text from documents, using OCR for image-based files, chunking large inputs, and sending context-rich text to a fine-tuned large language model. The model produces summaries, named entities, key phrases, inferred intent, classifications, and explanations, which a rule-based engine uses for policy enforcement while analysts validate high-fidelity findings and provide feedback. The system reduced incident-response time from multiple hours to a few minutes per case, saves an average of five minutes per file, and is expected to analyze 150,000 files annually; the post reports a 97% reduction in false positives.
Agentic Supplier Management with MongoDB Atlas, Voyage AI, and Multi-Modal Search
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.
amazon.science
Ground truth is a process, not a dataset
Evaluating search-augmented AI research reports requires verifying complex, multi-source claims that depend heavily on surrounding context. Unassisted PhD-level domain specialists tasked with establishing static ground-truth labels achieved only 60.8% accuracy on a hidden test set during controlled testing. To resolve benchmark errors on cognitively demanding tasks, Amazon AGI introduced the audit-then-score evaluation protocol alongside DeepFact-Bench and the DeepFact-Eval verification system. Under this protocol, model disagreements serve as challenges requiring concrete counterevidence and written rationales, enabling human auditors to evaluate competing claims and update benchmark ground truth prior to scoring. Operating under this iterative workflow, expert test accuracy rose to 90.9% across four rounds, and DeepFact-Eval reached 83.4% accuracy with GPT-4.1.
Coding Is No Longer the Constraint: Scaling Developer Experience to Teams and Agents at Spotify
Spotify experienced rapid codebase growth alongside widespread adoption of AI coding tools, resulting in a 76% increase in pull request frequency. To manage fleet-wide codebase maintenance and complex refactoring, the company combined its Fleetshift automation platform with Honk, a background coding agent powered by Claude. Honk runs inside Kubernetes pods with access to CI build environments and integrates with Backstage via Model Context Protocol tools to inspect component metadata. Standardized service architectures and active lint guardrails enable the agent to maintain consistency and auto-correct invalid design patterns during execution. By automating routine migrations, Spotify reduced fleet-wide Java backend updates from multi-week cross-team efforts down to three days handled by a single engineer.
Beyond parsing X12: Closing the gap for revenue cycle workflows in healthcare
Healthcare X12 pipelines can normalize remittance and enrollment data without giving medical billers a place to act on denials, leaving short-pays in SQL queries and spreadsheets while filing deadlines approach. Genpact and Databricks address that workflow gap with a two-layer operational workbench inside Databricks: production extensions to the open-source x12-edi-parser and a secure web application over Databricks SQL. The engine adds contextual 834 enrollment tracking and decoded 835 CAS adjustments, while the interface provides claim and service-line drill-downs, denial queues, dashboards, quality gates, and appeal or correction drafting. Live Unity Catalog gold-view queries avoid ETL shadow copies and synchronized caches; PHI remains inside the secure perimeter, with row-level security and audit logging for reveals and workflow changes. The proposed next step is Claude-based appeal drafting through Databricks Foundation Model APIs, and a two-week configuration-based trial uses an organization's schema and twenty existing denials.
Winning under CMS TEAM: Building the learning health system to realize success in VBC today and tomorrow
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
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.
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- Architecture830
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