What can Pokémon teach Cybersecurity Insiders about securing agentic AI? You might be surprised. At first glance, Pokémon and cybersecurity have nothing in common. One is a nostalgic game about catching digital creatures, the other a high stakes battle over autonomous AI systems. But the story of Pokémon GO shows something more important for agentic AI security: the most powerful systems are often built on data whose value only becomes clear years later, while the risk builds quietly in the background. When millions of players captured Pokémon, they unknowingly generated one of the most detailed spatial datasets ever created, including billions of geotagged images, movement signals, and environmental data. That foundation now powers Niantic Spatial, enabling real-world robotics and navigation in environments where GPS alone fails. A game became infrastructure. Agentic AI is now repeating that pattern inside the enterprise, only faster, more distributed, and harder to control. How Pokémon Helps Pizza Delivery Drivers Navigate The game Pokémon GO provides delivery robots with a centimetre-accurate view of the world thanks to AI and augmented reality (AR). This unusual example demonstrates that AI's paths are difficult to predict and the value of a dataset may only become apparent decades later. Understanding and carefully controlling the data is crucial, throughout its lifetime. It's rare to get detailed insights into an AI-driven project with reliable figures. But the two companies Niantic Spatial and Coco Robotics offer a glimpse behind the scenes. The startup Coco Robotics is working on delivery robots that will deliver pizza, amongst other things, in several cities in the US and Europe. And Niantic Spatial, the AI spin-off of Niantic, creator of the Pokémon GO app, provides precise visual data that Coco's robots use for orientation in urban jungles from Helsinki to Los Angeles, because GPS alone isn't sufficient. This virtual orientation system is AI-driven and has analysed data from Pokémon GO's massive dataset. For those less familiar with the app's functionality and popularity, consider this: within 60 days of its release in July 2016, 500 million people installed it and roamed the world's major cities in search of digital Pokémon. In the process, they shared billions of images of urban landmarks and their surroundings from every imaginable angle, including precise location data. In 2024, eight years after its launch, the game still boasted over 100 million players. Niantic Spatial utilises this vast repository of crowdsourced data from hundreds of millions of players to calculate a digital world model. A staggering thirty billion images were analysed using AI for this purpose, all of which followed typical smartphone image formats, and every image was linked to precise location coordinates, camera orientation, and device movement data. The data is therefore highly standardised and, in a sense, structured. It was all fed into Google's BigQuery and BigTable for analysis and long-term storage which, over time, was used to create the centimetre-accurate 3D map that Niantic Spatial and Coco Robotics are benefitting from today. Hidden Infrastructure from Scale What this use of Pokémon GO demonstrates is a pattern that is becoming foundational to agentic AI security: large volumes of low-value interaction data can evolve into long-lived, strategic infrastructure over time. No individual player thought they were contributing to robotics-grade spatial intelligence. Yet collectively, they built something far more durable than a game economy: a continuously updated digital twin of the physical world. That is the key lesson for enterprise AI. Agentic systems do not just consume data, they generate it at scale, across every workflow, decision, and API call. Over time, this creates hidden infrastructure such as logs, embeddings, prompts, tool outputs, and behavioural traces that may later become mission-critical assets or become liabilities in the form of emerging risk. The problem is that organisations rarely treat this early stage "low value" data as strategic. By the time its importance is understood, it is already deeply embedded across systems, and those liabilities have already become systemic risk. AI is Collapsing Cyber Timelines Another shift is accelerating this risk. Developments in frontier AI capabilities are shrinking the gap between vulnerability discovery and exploitation, turning cyber risk into a continuous pressure rather than a periodic event. In earlier software eras, defenders had time to patch, isolate, and respond. In agentic environments, models can interpret systems and chain tools to automate exploitation workflows. That compression of time changes the security model: detection, response, and recovery must now happen almost simultaneously. When combined with highly distributed AI agents operating across cloud services, SaaS platforms, and internal APIs, the attack surface expands rapidly in both speed and complexity. What used to be a "breach window" is becoming a permanent exposure state. Agentic AI Expands the Attack Surface Today's agentic AI systems are not passive tools. They act as privileged digital actors across enterprise platforms, reading emails, executing workflows, calling APIs, and interacting with other agents. That creates identity sprawl at scale. Every agent effectively becomes a new identity with permissions, context, and autonomy. Unlike human users, these identities operate continuously, at machine speed, and often across multiple systems simultaneously. This introduces new failure modes: * Over-permissioned agents accessing sensitive data * Orchestration chains that escalate privileges unintentionally * Cross-system automation loops that propagate errors Taken together, these failure modes show that resilience is shifting from reacting to incidents to continuous readiness and clean recovery, with the requirement to be owned at the executive board level. This requires three connected capabilities: * Resilience for identity systems. Agent identities must be centrally governed, continuously monitored, and kept within least privilege constraints. Without this, autonomous systems accumulate unchecked authority across the enterprise. * Protection and anomaly detection. As agentic systems operate at scale with data sources across on-premises, hybrid, and cloud environments, the data must be continuously validated and protected against corruption or manipulation. * Cyber recovery must extend to AI systems themselves. Organisations need the ability to restore clean versions of both data and autonomous agents, including a known-good operational state, often referred to as a Minimum Viable Company (MVC), to ensure continuity after compromise. Without this, autonomous systems amplify risk faster than traditional recovery models can respond. The Real Lesson from Pokémon Pokémon GO is not just a consumer tech success story. It shows how modern AI infrastructure is quietly built from mass participation and long-term data accumulation, with unexpected reuse over time. What begins as entertainment becomes infrastructure. What looks like noise becomes training data. And what feels optional becomes foundational. Agentic AI is following the same trajectory, but in enterprise systems - where the stakes are far higher and exposure is growing beyond organisations' ability to keep pace.

The Cloud-Native Workflow Behind Coco's Next-Gen Delivery Robot. READ TIME: Quick Summary * Coco Robotics is developing the Coco 2, an urban delivery robot designed for reliable operation across varied weather conditions, terrain types, and city environments. * The team uses PTC's Onshape and Arena to maintain a single source of truth across design, manufacturing, and the supply chain. * Cloud-native CAD and PLM have improved BOM management, release processes, vendor communication, and overall iteration speed. Building a delivery robot is one thing. Building one that survives Chicago winters, navigates rain-slicked Los Angeles streets, climbs steep gradients, and keeps running reliably, consistently, and at scale is something else entirely. That's the challenge Los Angeles-based Coco Robotics is solving with its next-generation delivery robot, the Coco 2. At the core of the effort is a product development process powered by one truth: To build a reliable product, teams need reliable design tools. A Delivery Robot built to handle anything. The Coco 2 is Coco's latest urban delivery robot, designed to operate in the kind of environments that would stop most robots cold. It's larger in cargo capacity than its predecessor, built from tougher materials for better field performance, and faster. Swappable terrain-specific tires handle wet streets and snow. Powerful motors climb steep gradients. A zero-turn-radius capability lets it navigate tight sidewalks and crowded urban corridors. For rain, an air blade system uses directed airflow to clear water droplets from the robot's sensors so it can see clearly in any weather. On board is a solid-state LiDAR system for reliable object detection, improved cameras, better communications hardware, and an ambient light ring that signals the robot's status and intent to people around it. Quick-swappable batteries make energy replenishment fast and simple in the field. The Coco 2 is designed for a roughly two-mile delivery range and currently operates in cities including Los Angeles and Chicago with plans for rapid global expansion. How Coco balances speed, cost, and quality. For Abhi Bhakuni, lead mechanical engineer at Coco, the core engineering challenge is straightforward to describe and difficult to solve: Build a product reliable enough to operate most of the time, across thousands of units, in dozens of environments, all while keeping cost and manufacturability in check. "Reliability is really important," he said during a webinar panel. "We want these robots to be operating most of the time." That means every design decision carries weight. Material choices, geometry, and structural design all get evaluated not just for performance but for how it holds up in the field and how practical it is to produce at scale. Iteration is essential to get that balance right. But iteration only works if the process behind it is tight. Mistakes that reach manufacturing cost time, real money, and create downstream problems that ripple through the program. The faster a team moves, the more important it is that their data, their files, and their processes stay in sync. Onshape-Arena Connection: one source of truth, across teams. Coco has been using Onshape for nearly 3 years, with Abhi personally on the platform for close to a decade. The company's product development process runs through the tightly integrated Onshape-Arena Connection that keeps design data, BOM, and sourcing details aligned across the entire organization. The workflow is straightforward in concept and significant in practice. Modeling happens in Onshape. When a design reaches a release milestone, the Onshape-Arena integration captures that data in a single shared system. From that point, everyone, including designers, supply chain teams, external vendors, is referencing the same version of a model. There is no ambiguity about which version a manufacturer is working from. "The big difference with the Onshape and Arena integrate workflow is it's like everything is aligned and files stay current," Abhi said. "The release process is cleaner, more visible and... it's really improved communication with vendors. In Arena, we can have our sourcing details very clearly laid down so our supply chain teams can just reference those. There is no confusion." For a team scaling globally, there's no room for a vendor working from the wrong file. The team at Coco streamlined design workflows with Onshape and Arena. 4 things that work better with Onshape and Arena. The shift from a legacy file-based workflow to Onshape and Arena has changed how Coco's team works in four specific ways. * BOM management is cleaner and more current. Design data flows directly from Onshape into Arena without manual export, keeping the bill of materials aligned with the live design at every stage * The release process is more visible. Every release is captured in Arena with a clear record of what was approved, when, and by who. There's no chasing files or reconstructing decisions after the fact. * Vendor communication is more reliable. Sourcing details live in Arena alongside the design data, accessible to supply chain teams and external partners without requiring a separate handoff. Abhi notes this has meaningfully reduced confusion with overseas vendors who still work primarily from formal documentation. * The overall system stays current. Unlike file-based PDM environments, where assemblies break and syncing is manual, cloud-native design means the data is always up to date. "In the past, I would have to be careful about managing data and communicating that carefully," Abhi said. "Now I just share a link." A platform that keeps getting better. One of the less obvious advantages of building on Onshape is that the platform itself evolves on a consistent cadence - releasing updates every three weeks. For a team like Coco's, iterating on a product that operates in the field every day, that matters. New features, improved workflows, and capabilities shaped by real customer feedback arrive regularly, without requiring upgrades, migrations, or IT intervention. For Abhi, that consistency is part of what makes Onshape a reliable foundation for a fast-moving product development team. "The platform is constantly evolving," he said. "New features are coming in at a consistent cadence and it genuinely feels like it's shaped based on user feedback and real customer needs." Building a reliable product is hard enough. The last thing a team scaling globally needs is to outgrow their tools. The Onshape discovery program. Learn how qualified CAD professionals can get Onshape Professional for up to 6 months - at no cost! * * Case Study * Robotics How Fauna Robotics Is Leading Humanoid Robot Development with Cloud-Native CAD & PDM 03.30.2026 See how Onshape cloud-native CAD+PDM accelerated Fauna Robotics' humanoid robotics design development with real-time collaboration and open API tools. learn more * * Blog * Evaluating Onshape * Electronics * Integrations * PCB Studio How Onshape PCB Studio Pulls 3D Component Models Straight from Altium 365 05.21.2026 Learn how the Onshape Altium Connector, a cloud-to-cloud integration, brings supplier-sourced 3D components into your mechanical assembly automatically. learn more Michael LaFleche * * Blog * Evaluating Onshape * Collaboration * Data Management What's the Difference Between File-Based and Cloud CAD? 05.20.2026 Not all CAD is the same. Compare file-based, cloud-hybrid, and cloud-native CAD across collaboration, file management, performance, accessibility, design history, security, and cost. learn more The Onshape Team * * Blog * Customers & Case Studies * Arena PLM Connection The Cloud-Native Workflow Behind Coco's Next-Gen Delivery Robot 05.14.2026 How the Coco Robotics team uses PTC's Onshape and Arena to manage design data, streamline releases, and scale globally without sacrificing quality. learn more The Onshape Team