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Explainer posts on Yomu, summarised newest first.
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Explainer posts
Best AI solutions for contact center in Australia: a 2026 buyer’s guide
AI self-service is deployed in 51% of Australian contact centers but only 38% deliver expected results, with handover failures and regulatory changes driving a new evaluation approach. Only 13% achieve smooth self-service-to-human handovers, and 84% of Australians rate straightforward handover as important, yet 98% expect self-service volume to grow. The Privacy Act amendment effective 10 December 2026 requires disclosure of automated decision-making, affecting routing and scoring logic, while APRA’s April 2026 letter flags single-provider concentration as a risk under CPS 230. Australia reversed its voluntary AI stance in July 2026, announcing mandatory AI Standards and a Digital Duty of Care, with legislation expected in early 2027. Vendor selection now depends on security classification, composability for substitution, and decision logging, with the Commonwealth Bank’s experience—cutting then reversing AI-driven job cuts after increased call volumes—illustrating that containment quality and agent capability are better objectives than headcount reduction.
AI-powered agents for contact center in Philippines: what the term actually means
In the Philippines, an AI-powered agent usually means a human agent assisted by AI for transcription, answer retrieval, live coaching, and wrap-up, not an autonomous software agent. The contact center sector employs 1.68 million people and earned $33.9 billion in 2025, with 56% of IBPAP members actively implementing AI. Published figures show a 22% reduction in call handling time from Concentrix and a 37% increase in agent satisfaction from TELUS Digital, but no benchmark measures Taglish speech. The IBPAP survey found 8% of operators reduced headcount, 13% increased it, and 24% saw job role shifts, while the IT-BPM sector added 70,000 net staff in 2025. Platforms like Twilio allow composable assist layers using models from OpenAI, Anthropic, or Azure, important where the language problem is unsolved and no Philippine rule requires disclosing AI use.
Building Localised, Enterprise-Grade AI Voice Agents
Building enterprise-grade AI voice agents in production demands managing real-time latency, telecommunication networks, regional accents, compliance frameworks, and unpredictable customer behaviour—issues that standard text-based LLM benchmarks do not capture. The recommended approach combines Microsoft Azure for hyperscale infrastructure, Twilio for global communications, and WIZ.AI for localisation, so engineering teams avoid building low-level infrastructure and focus on unique business value. Field data from WIZ.AI across Asia reveals an ideal conversational response window of 500–800 ms, with a P90 latency under 2.0 s currently acceptable, and real-time code-switching support for languages like Singlish and Hinglish. Success metrics shift from pilot capability to commercial production benchmarks such as connection rate, recognition and tagging accuracy, and measurable business outcomes like Promise-to-Pay conversion. Engineering practices to maintain code quality include cross-model code reviews and strict pull-request line-count guardrails enforced by pre-commit hooks.
Managing edge solutions with Red Hat: A layered approach
Managing edge infrastructure at scale across hundreds of locations demands consistent management rather than disparate tools. Red Hat’s layered approach organizes tools into three compartments: a universal foundation, OS-level fleet management for RHEL, and Kubernetes lifecycle management for OpenShift. The universal layer includes Ansible for agentless automation, Quay for container image registry with geo-replication, Lightspeed for proactive drift detection and compliance, and Trusted Software Supply Chain for end-to-end security. For RHEL-based edges, Edge Manager provides SaaS fleet management with OTA updates while Satellite offers on-premise content management for air-gapped environments. For OpenShift-based edges, Advanced Cluster Management handles cluster lifecycle and zero-touch provisioning while Advanced Cluster Security enforces container security across the fleet.
The #1 AI behavior hurting your customers’ experiences
91% of brands report a drop in customer satisfaction from AI, and the top behavior causing this is losing context when customers change channels, cited by 40% of businesses. Three of the four main behaviors are context-related, with lack of empathy at 39%, inability to handle exceptions at 38%, and failing to understand at 36%. The infrastructure problem includes session boundaries, identity mismatches, channel-specific tooling, and batch timing that prevent context from following the customer, causing 74% of consumers to repeat themselves and 71% to abandon conversations. Twilio’s structural solutions—Conversations, Conversation Memory, and Conversation Orchestrator—attach context to the customer rather than the session and recommend starting with the chat-to-voice transition. To measure improvement, track repeat-explanation rate after a channel change, recognition rate, and abandonment at transition points.
What is AI agent orchestration? How it works in 2026
AI agent orchestration coordinates multiple AI agents working on a single task, deciding routing, carrying context, resolving contradictions, and escalating to humans when needed. It operates at two levels: the agent level, where frameworks like LangGraph route between specialized agents, and the conversational AI level, which preserves customer identity and history across Voice, SMS, and WhatsApp channels. Orchestration comprises four jobs: routing, shared memory and state, conflict resolution, and human escalation, with parallel execution requiring robust memory management. Twilio’s Conversation Orchestrator provides a shared conversation record as a system of record, while Conversation Memory extracts observations and resolves conflicts by updating records with the most recent version. The FAQ section addresses differences from workflow automation, comparison criteria for orchestration tools, and the mechanism for agent-to-human handoffs within the same session without callbacks.
Same Query, Same Score: Consistent Ranking with StableTfl in Atlas Search
Paginated search in Atlas Search can show duplicate or skipped results across pages because BM25 scores drift between replicas due to changing corpus statistics. The usual workarounds like sticky routing or index snapshots require extra state and are cumbersome. The solution is StableTfl, a scoring function that uses only term frequency and document length, with term length as a proxy for rarity based on Zipf’s brevity law, eliminating corpus statistics and requiring no extra state. StableTfl is contributed to Apache Lucene and is available as a per-field similarity option in Atlas Search. Benchmarks on BEIR show StableTfl achieves 0.315 NDCG@10 compared to BM25’s 0.346, a 9% relative drop, making it suitable for paginated search, RAG pipelines, and multi-replica deployments where consistency matters more than peak relevance.
Stop Building MFA from Scratch (And What to Do Instead)
Building multi-factor authentication (MFA) in-house is a much larger project than teams expect, requiring authenticator app integration, QR code generation, secure backup recovery codes, and edge cases like lost phones. Auth0 positions its MFA offerings as a way to offload that work so developers can focus on core product features. The company’s paid plans include MFA directly, with Pro MFA covering standard authenticator apps in B2C and B2B Essentials plans. Enterprise MFA adds WebAuthn, push notifications, SMS, and voice, available as an add-on to B2B Essentials or included in Professional tiers. Auth0 argues that using its MFA helps teams pass compliance, unblock enterprise deals, and avoid weeks of building login infrastructure.
Validate GPU Cluster Readiness Before AI Workloads Land
GPU clusters can pass standard health checks yet fail under real AI training workloads due to a single degraded GPU, network link, or misrouted traffic. NVIDIA Cluster Readiness Engine (NVCRE) is an open source Kubernetes controller that runs real distributed workloads across topology-aware node groups before production jobs land, measuring results and reporting which nodes failed each test. Its layered API uses Custom Resource Definitions for Certification, Workflow, and Job resources, attributing each failure to a specific node and category such as NCCL bandwidth or training goodput. The WorkloadRun API handles platform detection, NCCL settings, shared-memory volumes, and gang scheduling, so operators do not need to write those manifests by hand. NVCRE integrates with NVSentinel for passive telemetry and is available under Apache 2.0, with a roadmap for inference and automated lifecycle validation.
How Concurrence governs clinical AI at a trillion-token scale with Unity Gateway
Concurrence processes 100.8 billion input tokens and 11.2 million LLM calls every 30 days, at an annualized rate of 1.2 trillion tokens. To govern these workflows, the company consolidates on Databricks with Lakebase for operational state, Unity Catalog for governance, and Unity Gateway for centralized AI access. Patient data is recorded as immutable events in Delta tables, from which a current patient state—called the world model—is computed while preserving provenance. All agents are tested against simulated patients before deployment; simulation traffic is approximately seven times production traffic, using immutable event history to replay states. Concurrence routes all coding-agent traffic through Unity Gateway, attributing each request to the engineer, and enforces compliance-gated model routing where HIPAA requirements are met first.
Small Talk With Prasun Kumar, CEO and Founder of Oppex AI
Oppex AI builds AI agents that help developers resolve production incidents by gathering context before the engineer is paged. The agents collect logs, cloud metrics, database health, affected customers, recent code changes, and past incident history, then present a recommendation. Because developer logs are not plain English, the team constructs a knowledge graph of how the entire system connects. They harden the agent by pitting it against a chaos monkey that breaks test systems without revealing the method, forcing the agent to learn. The 15-person team has launched the product with early enterprise customers and aims to bring mean time to resolve under 10 minutes, eventually moving toward autonomous system healing.
What Dropbox has learned from deploying AI at company scale
Dropbox has learned that deploying AI at scale requires examining workflows end to end, addressing new bottlenecks like too many code reviews or infrastructure strain. The company uses a framework measuring speed, effectiveness, quality, and impact, with proxy metrics including pull request throughput and change failure percentage. Dropbox reports around 70% AI-generated code, comparable to top technology peers, and its pull request throughput ranks in the top 5% of a custom benchmark while change failure percentage is at the 75th percentile. The company connects AI token usage to engineering value through its internal agent service Nova, and it uses ground-up initiatives like boot camps and aligned incentives to drive adoption. Human skills like problem solving, judgment, and leadership become more critical as AI lowers the cost of producing software, and Dropbox aims to extend AI workflows beyond engineering to product, design, and customer experience teams.
How to serve trillions of tokens for trillion-parameter coding agents
Serving inference for coding agents requires trillion-parameter sequence models like Moonshot AI’s Kimi K2.6 to process trillions of input and output tokens at extremely high relative and absolute performance. To be economically viable, these services must achieve large fractions of hardware peak performance, using contemporary matrix math accelerators like Tensor Cores at petaFLOP per second scale. Modal optimized by batching multiple sequences together, caching KV state, and patching SGLang with NVFP4 micro-scaling on Blackwell GPUs like the B200. Per-replica performance improved 2.8x per user and 5.6x across users, transforming ruinously expensive into a price-competitive service. One deployment processed hundreds of billions of tokens daily and trillions total, showing that careful engineering and benchmarking are key to scaling inference.
Database Branching: A Developer's Guide to Git-Style Workflows
Database branching creates isolated database environments from a known parent state using copy-on-write, where branches share unchanged data and only store modifications. For a large parent database like one that is 40 GB, two branches may require just 5.6 MB of additional storage instead of 80 GB with traditional full copies. Developers can test schema migrations like adding a NOT NULL column to a table with millions of rows against realistic data and constraints, CI pipelines provision per-pull-request branches for isolated tests, and AI agents experiment across short-lived environments without risking the parent database. Safe operation relies on protecting parent branches from writes and deletions, restricting sensitive data in disposable branches, automating creation and cleanup, and keeping environments reproducible through version-controlled configuration. Databricks Lakebase provides database branching, enabling teams to adopt Git-style workflows for development, CI/CD, and AI agent use cases.
Genie One MCP: Give any AI Agent the Right Business Context
Business leaders often have access to plenty of data but still cannot get a reliable answer to a simple question like why net revenue declined 8% at their largest account last week. The answer may span sales, finance, promotions, inventory, and account data, with each source potentially correct in isolation yet different in definitions, detail, relationships, and authority. Without the right business context, general-purpose agents cannot reliably determine what metrics mean, which sources are authoritative, how data should connect, or what each user is allowed to see. Genie One MCP gives governed context to any MCP-compatible AI agent, connecting assistants like ChatGPT, Claude, Microsoft Copilot, and coding agents to Genie One. This lets them answer business questions using approved definitions, trusted data relationships, and permission-aware access controls, with traceable, permission-aware answers grounded in governed business context.
Enabling Private High-Performance Production AI Inference with NVIDIA Confidential Computing
NVIDIA Confidential Computing (CC) enables secure AI inference by using memory-encrypted confidential VMs, confidential GPUs, and encrypted NVLink. Running inference frameworks like TensorRT LLM in a CC environment introduces performance overhead because secure execution changes assumptions about memory movement, timing, and multi-GPU communication. To quantify this overhead, the post presents a controlled methodology comparing CC-on and CC-off performance on a workload with long input context, extended output generation, and low concurrency. With CC-aware adaptations—including pageable memory selection, asynchronous readback, GPU %globaltimer for autotuning, and CC-aware multi-GPU communication—TensorRT LLM retained 96.1–98.2% of baseline throughput on eight NVIDIA B200 GPUs while keeping per-token latency overhead below 5%. The post concludes that security configuration and inference optimization should be treated as a single deployment problem for organizations moving private inference into production.
Topology-Aware Workload Scheduling with NVIDIA Topograph
AI factories are power-limited systems where GPU workload placement is a key optimization; poor placement fragments topology domains and forces traffic across shared links, reducing throughput and raising job costs. NVIDIA Topograph discovers cluster topology from cloud APIs or on-premises fabric systems, normalizes it into a common model, and publishes it in formats each workload manager expects, such as Kubernetes node labels, Slurm topology configuration, or Slinky ConfigMaps. It uses five components: API Server, Node Observer, Node Data Broker, Provider, and Engine to keep the topology view current as the cluster changes. The post covers deploying Topograph with Helm on Kubernetes, as a system package on Slurm, and as a Helm chart on Slinky, with support for cloud providers including Google Cloud, Lambda, Nebius, Nscale, and OCI. Topograph works alongside Dynamic Resource Allocation and KAI Scheduler to enable topology-aware gang scheduling, improving AI factory efficiency and tokens per watt.
Code Quality Q&A With the JetBrains Qodana Team
The Qodana team answers frequently searched questions about code quality, distinguishing technical debt from poor code quality and offering practices to reduce cognitive complexity in nested loops. They explain that code coverage measures how much production code is executed by unit tests, while mutation testing evaluates test quality by checking whether tests detect deliberate code changes. Unit tests provide the fastest feedback on code changes by catching regressions close to their introduction and making refactoring safer. To track improvement, teams should monitor static analysis trends such as new issues and technical debt, and also look for fewer Sev 1 issues in production and faster mean time to repair. False positives in automated scanning can be reduced by giving the tool more repository context, such as excluding inspections in qodana.yaml or silencing them with inline comments.
What is AI analytics? Why it only works on governed data
AI analytics applies AI and machine learning to data analysis, enabling natural-language queries and automated insights without manual query-building, shifting from retrospective dashboards toward agentic investigations. Unlike traditional BI, which requires users to navigate predefined dashboards and filters, AI analytics systems understand intent, determine relevant data and business context, and can conduct multi-step investigations on behalf of the user. A key challenge is that AI analytics only works when grounded in governed data: without shared metric definitions, authoritative sources, and permission-aware execution, the system produces untrustworthy results. Trust in AI analytics is a system property, not a model property; it requires governed data, shared business context, permission enforcement, and verifiability of queries and calculations. Better AI models do not eliminate the need for business context; as AI becomes more autonomous, semantic definitions and governance become more critical.
Data Ontology defined: The context layer your AI agents are missing
The Post argues that AI agents are breaking a long-standing assumption in enterprise data: that a human analyst can always resolve ambiguity. Richard Tomlinson explains that while analysts rely on tribal knowledge—like which revenue table is trusted or what ‘active customer’ means—agents lack this context. This gap causes inconsistent answers and erodes trust in self-service tools. The conversation examines why previous ontology efforts stalled and what must be true for an ontology to support agents. The ontology is positioned as the missing context layer that companies have not yet built.
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