How Smartsheet's Data, AI, & Platform Engineering teams use Monte Carlo to catch issues before they reach the business. Overview. Smartsheet, the enterprise platform for modern work management, serves over 85% of Fortune 500 enterprises. It also runs a data and AI platform that powers internal analytics, ML models, and product intelligence at scale. As the volume and complexity of Smartsheet's data estate grew, so did the cost of data downtime: silent pipeline failures, freshness gaps, and schema drift that reached downstream dashboards before anyone noticed. Monte Carlo's agent trust platform became a central pillar of Smartsheet's reliability strategy by helping the team implement proactive, automated observability across data and AI. The challenge. Before Monte Carlo, Smartsheet's data engineering teams faced a set of compounding reliability problems common to any organization scaling its data estate aggressively: * Data issues reaching dashboards and business decisions before engineers could catch them * Manual, custom unit tests that were expensive to write and slow to catch schema changes or volume drops * Incident ownership spread across Slack threads with no structured workflow for assignment or resolution tracking * A growing blind spot in agentic and ML workflows, where LLM interactions were a "black box" for validation teams * Alert fatigue from misconfigured or overly sensitive monitors during initial setup phases As Dharmendra D., Senior Software Engineer on Smartsheet's Data & AI Platform team, described it: teams were handling incident coordination "in a much messier way across Slack threads" - with no clear ownership or resolution tracking to prevent the same issues from resurfacing. The solution: Monte Carlo's agent trust platform. Smartsheet deployed Monte Carlo as an end-to-end observability layer across their data and AI stack, integrating tightly with Databricks, Snowflake, Looker, PagerDuty, and Slack. The platform's automated ML-driven monitoring, field-level lineage, and incident management workflows replaced the manual, fragmented approach that had previously left teams reactive rather than proactive. Proactive anomaly detection before issues reach the business. Monte Carlo's ML models learn baseline behavior for each data asset and flag deviations - freshness drops, volume anomalies, schema changes - before downstream consumers notice. Rather than flooding teams with noise, the system surfaces only meaningful anomalies, "significantly reducing alert fatigue and helping our team focus on real issues rather than chasing false positives." Seamless integration with the modern data and AI stack. Smartsheet's engineers operate across a multi-tool environment spanning Databricks, Snowflake, Looker, PagerDuty, and Slack. Monte Carlo's broad integration surface made centralized observability possible without disrupting existing workflows. End-to-end lineage that cuts debugging time. Lineage visualization across data and AI assets has been a recurring need at Smartsheets, with engineers describing it as the feature that most directly translated to hours saved. The ability to trace an issue from a Looker dashboard all the way back to a Snowflake warehouse, or from a pipeline failure to its upstream source, eliminated the manual investigation that previously consumed debugging cycles. Being able to trace data from source to consumption in a clean, interactive graph saved Smartsheet engineers hours of investigation during incidents. Structured incident management replacing ad-hoc Slack coordination. One of the most operationally significant improvements across Smartsheet's teams was the shift from informal incident handling to structured, ownership-driven workflows. Monte Carlo's incident management module brought clear assignment, severity classification, and resolution tracking to what had previously been a coordination problem. "The incident management workflow is a highlight as well," noted Dharmendra D. "It keeps the team aligned on data quality issues with clear ownership and resolution tracking - something we previously handled in a much messier way across Slack threads." The result: fewer escalations and faster resolution across Smartsheet's Data & AI Platform. On ROI: "For a platform team, the ROI shows up as fewer escalations and faster incident resolution." The time saved debugging incidents, the reduction in manual monitoring effort, and improved organizational trust in data all compounded into measurable returns. Agent observability for emerging AI workloads. Smartsheet engineers are seeing the value of Monte Carlo beyond traditional data pipelines and into the agentic layer - a particularly resonant point given Smartsheet's active AI and ML development. Ruchir K. described the team's challenge: "In terms of Agent Observability, LLM interactions can be a bit of a black box for validation teams. We implemented an internal judge system for LLM-based projects, but Monte Carlo has also helped us get the big picture on how well our models are performing." Monte Carlo provided the visibility layer where internal tooling fell short. For teams managing multiple data squads under a larger analytics function - like Smartsheet's - Monte Carlo also simplified coverage tracking: "One of our ongoing challenges has been making sure all the different teams have proper coverage for our IP. We have a lot of squads under Analytics, and this has helped us keep the process moving so we can consistently ensure our products are covered appropriately." Results. Across Smartsheet deployments, Monte Carlo delivered on three fronts: engineering efficiency (eliminating manual unit test writing, cutting debugging time through field-level lineage, and freeing teams for higher-order work), data and AI reliability (catching issues before they reached dashboards, reducing alert fatigue through ML-calibrated detection, and building cross-organizational trust in data and agents), and operational maturity (replacing ad-hoc Slack coordination with structured incident ownership, clear resolution tracking, and scalable multi-team coverage). About Smartsheet. Smartsheet (NYSE: SMAR) is the enterprise platform for modern work management, helping organizations plan, capture, manage, automate, and report on work. Over 85% of Fortune 500 companies trust Smartsheet. Headquartered in Bellevue, WA. Its promise: MonteCarlo will show you the product.

PagerDuty announces Arnaud Lagarde, vice president of EMEA. SAN FRANCISCO-(BUSINESS WIRE)- PagerDuty, Inc. (NYSE: PD), a leader in AI-first operations management, today announced the appointment of Arnaud Lagarde as vice president of EMEA. Lagarde will lead PagerDuty's next phase of growth in the EMEA region, bringing the entire incident management lifecycle to customers across EMEA to solve their biggest digital challenges. "We are thrilled to appoint Arnaud as vice president of EMEA, since he brings a wealth of enterprise sales relationships and years of experience growing this region," said Todd McNabb, chief revenue officer at PagerDuty. "Arnaud brings a specific combination of deep technical expertise and leadership that will be critical for PagerDuty's customers, partners and employees. He is a great fit for PagerDuty and we look forward to his impact." Lagarde brings to the role over 20 years of experience spanning companies like Automation Anywhere, CA Technologies and BMC. Over the past two decades, he has worked closely with founders, investors and executive teams to establish product-market fit, develop enterprise go-to-market strategies and build high-performing revenue organizations. He brings extensive sales and domain expertise in AI-driven companies and has a strong track record for building high-performing regional teams and executing go-to-market strategies. "PagerDuty has a massive opportunity across EMEA at a critical turning point for AI-first digital operations," said Arnaud Lagarde, vice president of EMEA, PagerDuty. "With my experience industrializing AI at scale in mission-critical processes and regulated industries, I look forward to working with our regional customers to unlock entirely new levels of operational efficiency. Moving forward, my priority is to empower our teams and ecosystem partners to accelerate growth, drive measurable business outcomes, and foster enduring enterprise relationships." Lagarde received his Master of Art in Business Studies from ESIC ESSICA Bordeaux and graduated from IUT de Bordeaux earning a degree in business studies. About PagerDuty Inc. PagerDuty, Inc. (NYSE: PD) is the global leader in AI-first digital operations. By automatically detecting, diagnosing, and remediating issues, the PagerDuty Operations Cloud acts as the central control plane for the modern enterprise - orchestrating AI agents and automated workflows with context from over 750 integrations. Trusted by approximately two-thirds of the Fortune 100 and nearly half of the Fortune 500, PagerDuty is the industry standard for organizations scaling resilient, autonomous operations. Learn more and try it for free at www.pagerduty.com. The PagerDuty Operations Cloud The PagerDuty Operations Cloud is an AI-powered platform that automates and orchestrates the entire incident management lifecycle - from detection to resolution, providing resilience at scale. Designed for mission-critical operations, the platform empowers teams to identify and diagnose disruptions in real time, mobilizing the right teams to quickly streamline workflows to solve digital issues before they become incidents. The PagerDuty Operations Cloud is essential for delivering flawless, always-on digital experiences that organizations and consumers expect today.

AI Orchestrations: Your easy button for proactive operations. This blog post is part of PagerDuty's ongoing series on how PagerDuty, Inc. is helping customers navigate their journey towards autonomous operations. Read on to learn about how AI Orchestrations builds towards this vision. "We should automate this." Sound familiar? For many operations teams, that sentence never becomes action. Building event orchestration rules demands deep platform expertise, time no one has, and the ability to spot which patterns in your data actually matter. So alert fatigue lingers. Toil piles up. Automation stays on the distant roadmap. PagerDuty's new AI Orchestrations capability was built to close that gap - for good. From reactive to proactive, automatically. AI Orchestrations analyzes your historical event and incident data (including how responders have manually intervened during past incidents) and surfaces ready-to-apply recommendations for event orchestration rules. No rule syntax. No expert configuration. Just a plain-language description of what the rule does, how many alerts or incidents it would have improved, and a confidence score based on past accuracy. The result is a global recommendations view across all of your services: every AI-generated suggestion, ranked, filterable by team, and actionable in a single click. What gets recommended. The system surfaces several types of rules based on detected patterns in your data: * Suppression - silence low-value alerts before they become incidents * Severity - dynamically set alert severity so responders know what demands immediate attention * Priority - automatically triage incidents so teams focus on what matters Each recommendation includes impact metrics - how many past events would have been affected - and a precision score so teams can validate before applying. Human in the loop, friction removed. AI-suggested rules live in a dedicated AI Orchestration Segment that runs after your existing manual rules. Your current logic always runs first. When a recommendation looks right, you apply it with one click. When it's not relevant - or when you'd rather fix the underlying monitor than suppress the alert - you dismiss it, and that feedback improves future recommendations for your service. This keeps humans in control while eliminating the #1 barrier to automation adoption: the expertise required to build rules in the first place. The flywheel effect. Every incident your team responds to contains signal. AI Orchestrations turns that signal into automation - and better automation means fewer incidents, less noise, and faster resolution. Teams using event-driven automation see 46% fewer incidents and up to 30% lower FTE costs. Over time, the platform gets smarter as your data evolves, creating a continuous flywheel: incidents inform automation, automation prevents incidents. This is how PagerDuty delivers on the vision of Autonomous Operations - not by replacing human judgment, but by making it easier to apply at scale. Available now for AIOps customers. AI Orchestrations is Generally Available today for PagerDuty AIOps customers. If your team is spending time manually triaging repetitive alerts, struggling with low event orchestration adoption, or onboarding new members who don't have bandwidth to configure automations - this is the place to start.