Crossing the Rubicon: How Advanced Prompting Turns Business Leaders into Developer Wizards

On this episode of the FINOS Open Source in Finance Podcast, host Grizz Griswold sits down with James Calise (Pre-Sales Engineering Lead) and Travis Liles (Cloud Architect) from Oracle Cloud Infrastructure (OCI) Financial Services. Together, they explore how Oracle is shifting from proprietary models to open-standards-based cloud architecture to help financial institutions prevent vendor lock-in, meet strict DORA multi-cloud resiliency mandates, and scale grid computing across on-premise and multi-cloud environments with OpenGris. Plus, they share how autonomous AI agents are becoming the next-generation operational management layer for open-source enterprise platforms. 🕒 Timestamps:0:00 Welcome to the FINOS Open Source in Finance Podcast0:53 Guest Introductions: James Calise & Travis Liles (Oracle Cloud)5:17 Verticalized Cloud Engineering for Financial Services8:11 Why Financial Compute Remains Heavily On-Premise & Legacy10:23 Mainframe Migrations vs. Performance Realities11:18 Oracle’s Shift to Open Standards & Breaking Cloud Lock-In14:46 DORA Regulations & Multi-Cloud Resiliency Mandates17:25 Introducing OpenGris: Bursting Grid Compute Across Multi-Cloud20:45 Bridging High-Performance Computing (HPC) with AI24:00 Pace-Layered Architecture: Secret Sauce in the Agent Layer28:22 Avoiding Hype: Actionable AI without Vendor Box-In32:05 Autonomous AI Agents as 24/7 Open-Source Operations Managers36:50 Oracle’s Expanding FINOS Contributions & OSFF Preview 📊 The Problem: Cloud Lock-In, On-Prem Ceilings & Admin OverheadCloud Lock-In & DORA Compliance Barriers: Proprietary cloud services lock financial institutions into closed ecosystems, making multi-cloud resiliency and regulatory compliance (e.g., DORA exit plans) nearly impossible to execute. On-Premise Compute Ceilings: Financial risk modeling and overnight grid calculations require massive bursts of compute power that exceed fixed on-premise hardware ceilings, yet low-latency requirements keep core systems tied to legacy infrastructure. Administrative Overhead for Open Source: Adopting self-hosted open-source platforms turns internal engineering teams into full-time IT administrators tasked with manual monitoring, patching, and operational upkeep. 🏗️ The Solution: Open Standards, OpenGris & Autonomous AI AgentsOpen Standards Cloud Foundation: OCI builds its financial cloud infrastructure around open standards, enabling institutions to deploy workloads seamlessly across on-premise, dedicated regions, and multi-cloud environments. OpenGris Elastic Bursting: Leveraging the OpenGris project to schedule and scale financial compute jobs elastically across cloud providers based on performance, availability, and cost. Agentic AI Operations Layer: Deploying 24/7 autonomous AI agents as a management layer that actively monitors open-source projects, applies patches, tests builds, and automates operational maintenance. ⚙️ Why This Matters for Financial EngineeringPace-Layered Innovation: Standardizes baseline infrastructure and APIs, allowing capital markets firms to build their core IP and competitive advantage directly into the agentic AI layer. Smooth Onboarding & Multi-Cloud Flexibility: Eliminates re-skilling delays and proprietary rewrites, giving financial engineering teams true mobility across hybrid and multi-cloud platforms. 🌐 More about FINOS: https://www.finos.org/ 📧 Join our newsletter: https://www.finos.org/sign-up 🎙️ Listen to our Open Source in Finance Podcast: https://www.youtube.com/@FINOS/podcasts LinkedIn: https://www.linkedin.com/company/finosfoundation #FINOS #OSFFNewYork #Oracle

On this episode of the FINOS Open Source in Finance Podcast, host Grizz Griswold sits down with Nichola Hammerton, Head of Architecture at Deutsche Bank, in Canary Wharf, London. Nichola shares her unique career trajectory—from studying chemistry and co-founding a tech consulting firm to restructuring the British Red Cross and leading large-scale capital markets transformations. She details how Deutsche Bank is embedding "control by design" into its enterprise AI architecture platforms, navigating the interplay between legacy mainframes and cutting-edge AI agents, and why cross-industry open-source collaboration on projects like FINOS Fluxnova is essential for solving shared industry challenges. 🕒 Timestamps:0:00 Welcome & Catching Up on the FINOS Open Source in Finance Podcast1:15 Current Role: Head of Architecture at Deutsche Bank3:30 Career Journey: From Chemistry to Consulting & The Red Cross7:14 Large-Scale Tech Transformation During the Credit Crunch9:45 What is Architecture? Focus on Business Outcomes Over Tech Hype10:55 Control by Design: Embedding AI Governance & Controls13:12 Mainframes & Legacy Infrastructure in the Age of AI14:36 Data Quality, Freshness & Semantic Layers for AI Agents16:00 Open Source Consolidation & Cross-Firm Collaboration (Fluxnova)18:08 Why Concrete Use Cases Drive Effective Architecture19:21 Building Architecture vs. Technology Architecture 📊 The Problem: AI Governance Overload & Architecture Disconnected from OutcomesRapid AI Adoption Risks: Deploying autonomous AI agents without embedded governance creates massive code review backlogs, data entitlement risks, and quality control overhead. Technology for Technology's Sake: Architectural designs often get caught up in technological hype rather than focusing strictly on measurable business outcomes and operational use cases. Legacy Interoperability: Modern AI platforms must operate alongside decades-old core banking infrastructure (such as mainframes) that remains essential for daily operations. 🏗️ The Solution: "Control by Design" & Use-Case-Driven Open CollaborationNichola Hammerton outlines a strategic approach to modern enterprise architecture: Embedded Control-by-Design: Building security, compliance, and governance frameworks directly into the underlying architecture platform so developers can innovate safely without managing controls manually. Data Quality & Semantic Layers: Establishing robust data freshness, entitlement management, and semantic layers to power reliable end-user AI agents. Consolidation Through Open Source: Collaborating across buy-side and sell-side institutions on shared infrastructure (such as FINOS Fluxnova) to pre-solve common use cases and access broader talent pools. ⚙️ Why This Matters for Financial EngineeringShift from Delivery to Strategic Architecture: Empowers technology leaders to establish guardrails that scale across vast enterprise portfolios while maintaining operational stability. Grounding AI in Real-World Use Cases: Ensures AI investments deliver business value by anchoring architectural designs to specific operational problems rather than abstract tech capabilities. 🌐 More about FINOS: https://www.finos.org/ 📧 Join our newsletter: https://www.finos.org/sign-up 🎙️ Listen to our Open Source in Finance Podcast: https://www.youtube.com/@FINOS/podcasts LinkedIn: https://www.linkedin.com/company/finosfoundation

In this episode of the Open Source in Finance Podcast, host Grizz Griswold introduces an expert OSFF panel moderated by Hilary Carter (Linux Foundation) featuring Jose Plehn (Bright Query / open-data.org), Nora Anwar (Overture Maps Foundation), and Jane Gavronsky (FINOS COO). The panelists explore why financial services urgently need a shared open data commons to eliminate duplicate reference data maintenance, establish a universal entity graph across companies, people, and places, and ground AI models to prevent hallucinations. 🗽 Catch Us in New York! Ready to explore open data, shared entity graphs, and collaborative open-source engineering? Join global financial technology leaders at OSFF New York on November 4–5, 2026.🎟️ Register Now: https://hubs.ly/Q04n_bZL0🔥 20% OFF DISCOUNT CODE: 26YTOSFFNY20C 🕒 Timestamps:0:00 Podcast Intro with Grizz Griswold & Panel Setup1:34 Panelist Introductions: Bright Query, Overture Maps & FINOS3:45 Why Open Data Matters: Breaking Down Financial Data Silos5:00 The Vision of an Open Entity Graph: Companies, People & Places7:57 Governance & Community Scale: Cross-Referencing Master IDs11:06 Winners & Losers in the Open Data Landscape13:11 Financial Reference Data Pain Points: The "Light Switch" Analogy16:55 Crowdsourcing Entity Resolution & Identifier Mapping20:13 Overture Maps Origin Story: Meta, Microsoft, Amazon & TomTom Collaboration25:06 Geospatial Data Use Cases: From Insurance Risk to Fraud Detection29:10 Open Data for AI Grounding: Reducing LLM Hallucinations with RAG34:07 Success Metrics & How Financial Institutions Can Join 📊 The Problem: The "Light Switch" Reference Data Tax & Fragmented SilosRedundant Reference Data Maintenance: Financial institutions spend millions of dollars individually ingesting, cleaning, and enhancing identical vendor reference datasets just to keep basic systems operational. Entity Resolution & Identifier Disconnects: Mismatched entity IDs (LEIs, CUSIPs, ISINs, addresses) across disparate corporate databases create massive operational friction, slowing down KYC/KYB compliance, onboarding, and risk management. 🏗️ The Solution: A Shared Open Data Commons & Global Entity GraphGlobal Reference & Entity Graph: Bright Query and open-data.org are establishing an open global directory covering over 400M companies, 1B people, and 200+ countries mapped with unique, permanent entity IDs. Overture Maps Collaboration Model: Following the proven Linux Foundation model of Overture Maps (founded by Meta, Microsoft, Amazon, and TomTom), competitors mutualize base data maintenance costs so teams can focus on proprietary innovation layers. Grounding AI & RAG Pipelines: Joining LLM knowledge graphs to deterministic open entity graphs drastically reduces hallucinations during AI training and inference, providing the verifiable data lineage required in regulated finance. ⚙️ Why This Matters for Financial Engineering Mutualizing Non-Competitive Costs: Crowdsourcing entity matching, validation rules, and cross-referencing tables removes the "thankless" overhead of reference data cleanup across buy-side and sell-side firms. Spatial & Corporate Context for AI Agents: Integrating open entity graphs with spatial systems (GERS IDs) gives AI agents accurate organizational and geographic awareness to automate risk assessment and site selection. 🌐 More about FINOS: https://www.finos.org/ 📧 Join our newsletter: https://www.finos.org/sign-up 🎙️ Listen to our Open Source in Finance Podcast: https://www.youtube.com/@FINOS/podcasts LinkedIn: https://www.linkedin.com/company/finosfoundation

Learn how CustomerXR CTO Chris Fonte uses Spatial Computing, VisionOS & AI to revolutionize financial workflows & enterprise training.Christopher Fonte joins Peter Smulovics to explore how Extended Reality (XR) and Spatial Computing are transitioning from consumer gaming into enterprise financial applications. Discover how immersive training models slash operational costs and knowledge retention gaps, while pass-through spatial hardware expands desktop multi-screen real estate. Learn how combining spatial interfaces (VisionOS, Horizon OS) with Generative AI enables 3D market visualizations and collaborative wealth management advisory.Episode Chapters 00:00 - Welcome & Guest Intro: Chris Fonte’s 10-Year XR Journey02:08 - Why Enterprise Needs XR: Immersive Procedural Training & Retention04:05 - Immersive Security Training: Gamifying Artifact Discovery in VR06:01 - VR to Mixed Reality Evolution: Passthrough vs See-Through Lenses08:08 - Meta Connect Predictions & Next-Gen Spatial Hardware Form Factors09:20 - Pass-Through Hardware Debates: Compute Pucks vs Integrated Lenses12:12 - Multi-Screen Real Estate: Spatial Workspaces for First-Line Workers16:21 - Next-Gen Spatial UX: Eye Tracking, Hand Tracking & Smart Rings18:28 - HoloLens Architectural Lessons & Apple VisionOS Spatial Personas22:36 - Real-World Spatial Apps: CAD Ingestion & Lighting Inference Engine25:42 - Spatial Computing in Finance: 3D Market Data & Wealth Advisory29:25 - Generational Technology Adoption: Gen Z Intuitive Spatial Interfaces33:09 - Audience Q&A: Carrier Hardware Financing, Mass Adoption & PricingLinksRegister for OSFF New York (20% OFF Code 26YTOSFFNY20C): https://hubs.ly/Q04n_bZL0

On this episode of the Open Source in Finance Podcast, host Rob Moffat (Chief Architect at FINOS) is joined by an executive panel featuring Mimi Flynn (Vice President & Open Source Developer Advocate at Morgan Stanley), Elspeth Minty (Managing Director at RBC Capital Markets), and Mark Paulsen (Head of Open Source Program Office at TD Bank). Together, they explore the operational realities of open-source adoption in regulated banking. The panel breaks down the hidden costs and security risks of internal code forking, strategies for measuring OSPO Return on Investment (ROI), automated contribution controls via GitProxy, and how to prepare engineering teams for the convergence of open source, rising global regulations, and generative AI. 🗽 Catch Us in New York! Ready to scale your Open Source Program Office (OSPO), mitigate supply chain risks, and streamline enterprise contribution workflows? Join us at OSFF New York on November 4–5, 2026.🎟️ Register Now: https://hubs.ly/Q04n_bZL0🔥 20% OFF DISCOUNT CODE: 26YTOSFFNY20C📊 The Problem: The Hidden Trap of Internal Code Forks & Governance DragRegulated financial institutions face exponential increases in regulatory oversight, open-source dependency trees, and CVE vulnerabilities. When engineering teams bypass contribution approvals by creating private internal code forks, they incur severe technical debt, untracked security risks (such as missing upstream CVE patches), and long-term maintenance overhead. 🏗️ The Solution: Enterprise OSPOs, GitProxy & Standardized Maturity ModelsThe panel details how financial institutions build mature OSPOs using FINOS Open Source Readiness (OSR) frameworks: Developer Training & Maturity Roadmaps: Mandating foundational developer training (such as Linux Foundation’s FSOSD) to educate engineers on licensing, IP, and risk management. Automated Guardrails (GitProxy): Implementing automated commit controls at push time to screen outgoing code for secrets, proprietary logic, and licensing compliance without disrupting developer velocity. Standardized Maturity Benchmarks: Utilizing the FINOS OSR Maturity Model as a competitive roadmap to transition from passive software consumption to active open-source sponsorship and code contribution. ⚙️ Why This Matters for Financial EngineeringMeasuring True ROI: Demonstrating OSPO value through hard metrics (Evident AI indicators) alongside soft metrics like recruiting appeal, developer career progression, and talent retention. Collaborative Industry Security: Mutualizing open-source governance tools allows banks to solve shared compliance challenges together rather than re-inventing the wheel in isolation. 🌐 More about FINOS: https://www.finos.org/📧 Join our newsletter: https://www.finos.org/sign-up🎙️ Listen to our Open Source in Finance Podcast: https://www.youtube.com/@FINOS/podcastsLinkedIn: https://www.linkedin.com/company/finosfoundation

Amy Rose (CTO of the Overture Maps Foundation) joins the podcast to explore the structural differences between open-source software and open data. Amy breaks down how Overture—founded by AWS, Meta, Microsoft, and TomTom—builds interoperable global map layers, resolves complex entity definitions across disparate datasets, and why modern agentic AI workflows require record-level metadata rather than dataset-level summaries. 🗽 Catch Us in New York! Ready to explore open data, high-performance computing, and enterprise AI architecture? Join us at OSFF New York on November 4–5, 2026.🎟️ Register Now: https://hubs.ly/Q04n_bZL0🔥 20% OFF DISCOUNT CODE: 26YTOSFFNY20C 🕒 Timestamps:4:36 Interview Start: Amy Rose & The Overture Maps Foundation6:43 From Landscape Architecture to GIS: Amy's Career Journey11:05 Real-World Geo Applications: Transportation, Disaster Recovery & Oak Ridge Lab14:02 What is Overture Maps? Building Interoperable Global Map Data15:38 Open Source Software vs. Open Source Data17:13 Defining High-Quality Data: Fitness for Purpose & Use Cases18:39 Case Study: Built Environment Mapping & Entity Resolution24:05 Why Agentic AI Workflows Require Record-Level Metadata29:45 The Monthly Release Engine: GERS IDs, Bridge Files & Change Logs34:08 Solving Spatial Interoperability: Geometric Joins & Entity Matching42:05 The Future: Native Location Integration in Enterprise AI 📊 The Problem: The Open Data Lineage & Interoperability ChallengeUnlike open-source software—where source code acts as a single point of truth—open data requires merging disparate, opinionated input sources with varying licenses, coordinate systems, and quality levels. Traditional dataset-level metadata fails when applied to autonomous systems or agentic AI workflows, which parse and combine individual records rather than entire tables. Furthermore, performing spatial joins and resolving duplicate entities across different municipal or corporate datasets creates massive computational and manual overhead. 🏗️ The Solution: Overture's GERS Infrastructure & Record-Level MetadataAmy Rose outlines how Overture Maps standardizes global spatial data: Global Entity Reference System (GERS): Assigning persistent, canonical GERS IDs to physical entities (buildings, places, roads) to maintain lineage and bridge external datasets (e.g., Esri, Meta, Microsoft, Google). Record-Level Provenance: Embedding metadata (source, license, transformation steps, coordinate reference frames) directly into individual records rather than appending summary text at the dataset level. Monthly Aggregated Releases: Distributing prepackaged monthly releases alongside bridge files and change logs, allowing downstream consumers to verify fitness for purpose in navigation, logistics, or risk modeling. ⚙️ Why This Matters for Financial & Technical ArchitectureVerifiable Risk & Asset Modeling: Provides clean, open spatial foundations for underwriting flood risk, supply chain logistics, ESG impact, and real estate valuation without expensive manual data wrangling. Trusted AI Agent Evidence: Equips AI models and automated agents with explicit record-level evidence, making spatial queries verifiable and reproducible. 🌐 More about FINOS: https://www.finos.org/ 📧 Join our newsletter: https://www.finos.org/sign-up 🎙️ Listen to our Open Source in Finance Podcast: https://www.youtube.com/@FINOS/podcasts LinkedIn: https://www.linkedin.com/company/finosfoundation#FINOS #OSFFNewYork #OvertureMaps #OpenData #GIS #Metadata #AgenticAI #DataEngineering #SpatialData

Is operating under strict financial regulation an innovation killer or its greatest catalyst? Host Peter Smulovics (Distinguished Engineer at Morgan Stanley and FINOS Zenith Lead) sits down with Dr. Aditya Vikram Kashyap (FINOS Ambassador, IEEE Senior Member, and Forbes Technology Council member) for an insightful episode of the Open Source in Finance Podcast from the FINOS Zenith Special Interest Group. Innovation and regulation are often portrayed as opposing forces: innovation wants speed and freedom, while regulation introduces constraints and controls. But in financial services, and increasingly with AI, the reality is much more complicated. Good governance can actually make innovation possible at scale, while bad governance can become its own source of risk.Together Dr. Kashyap and Peter challenge the narrative that speed and control are opposing forces. Dr. Kashyap breaks down how regulatory frameworks like model risk management (SR 11-7) and NIST post-quantum cryptography (PQC) standards force architectural clarity, why the "cost of delay" is the most under-managed enterprise risk, and how to structure continuous governance for agentic AI fleets. 🗽 Catch Host Peter Smulovics Live in New York! Ready to navigate enterprise AI governance, post-quantum migration, and open-source standards? Join Peter Smulovics and global finance leaders at OSFF New York on November 4–5, 2026.🎟️ Register Now: https://hubs.ly/Q04n_bZL0🔥 20% OFF DISCOUNT CODE: 26YTOSFFNY20C📊 The Problem: The "Cost of Delay" & End-Gate Approval FlawsMost regulated enterprises fail to quantify the risk of standing still. While risk matrices track the downside of deploying new technology, they completely ignore the cost of delay—leading organizations into a false sense of security while their innovation cycles stall. Furthermore, traditional Software Development Life Cycle (SDLC) models place risk, legal, and compliance teams at the end of the development pipeline as an "approval gate". By the time a finished prototype reaches reviewers, every decision (to approve, reject, or send back for rework) is catastrophically expensive. 🏗️ The Solution: Embedded Governance & Machine IdentitiesDr. Aditya Kashyap outlines how financial institutions can transform governance into an operational engine: Embedding Risk from Day One: Shifting compliance, legal, and cybersecurity teams from external gatekeepers to active core members of the innovation team during initial design. "Accountability Follows Authority": Ensuring human oversight remains legally bound to system outputs while assigning distinct machine identities and permissions to autonomous AI agents interacting across multi-model ecosystems. Goodhart's Law & Production Metrics: Replacing vanity metrics (patent counts, hackathons) with actionable indicators: pilot-to-production conversion rates, cycle-time splits, decommissioning velocity, and time-to-fault detection. ⚙️ Why This Matters for Financial EngineeringSR 11-7 & PQC Preparation: Early adoption of model risk management and NIST post-quantum cryptography standards builds the exact architectural inventory required to safely scale generative AI and quantum-resilient infrastructure. Standards as Lasting Power: Establishing open, transparent industry standards through foundations like FINOS prevents regulatory capture and creates common baselines that compound value across global financial markets for decades. 🌐 More about FINOS: https://www.finos.org/📧 Join our newsletter: https://www.finos.org/sign-up🎙️ Listen to our Open Source in Finance Podcast: https://www.youtube.com/@FINOS/podcastsLinkedIn: https://www.linkedin.com/company/finosfoundation

What happens when a business-focused professional discovers that AI can translate vision into working software?In this episode, we follow the journey of a business-aligned leader who never saw herself as a developer. Armed with curiosity, domain expertise, and increasingly sophisticated prompting techniques, she crosses the Rubicon from consumer of technology to creator of technology.We explore how modern AI tools are redefining who gets to build software, contribute to projects, experiment with emerging technologies, modernize financial systems, and solve real-world business problems. The conversation examines what changes when technical barriers fall, and domain expertise becomes a superpower.This isn't a story about replacing developers. It's a story about expanding the circle of creators. Host Peter Smulovics (Distinguished Engineer at Morgan Stanley and FINOS Zenith Lead) sits down with Fintech Product Manager Sarah Hilton-Burroughs for a special edition of the Open Source in Finance Podcast. Together, they explore how domain experts are leveraging AI agents, Spec-Driven Development (SDD), and custom component prompts to build full-stack RegTech platforms—without abandoning architectural guardrails or regulatory auditability. 📊 The Problem: The Context-Window "Credit Suck" & AI FabricationWhen non-technical domain leaders attempt to build complex software using basic chat prompts, they quickly encounter two major barriers: context decay and AI fabrication. Re-teaching an entire multi-tenant codebase to a general AI model on every iteration rapidly consumes token credits without maintaining state. Furthermore, unguided models fabricate plausible-sounding code and logic rather than adhering strictly to empirical laws or financial regulations, creating massive compliance risks if deployed blindly. 🏗️ The Solution: Component-Driven SDD & Specialist Agent FleetsSarah Hilton-Burroughs demonstrates how business leaders can structure their domain knowledge into a scalable software pipeline: Spec-Driven Prompt Libraries: Replacing endless conversational chats with structured markdown specification libraries and component-level rule sets. Specialized Agent Delegation: Customizing dedicated AI agents for specific architectural layers (e.g., a schema agent that exclusively manages database mutations). Domain Knowledge Moats: Recognizing that code writing has been democratized, shifting the builder's focus to requirements precision, data science fundamentals, and cross-jurisdictional compliance. ⚙️ Why This Matters for Financial EngineeringEmbedded Regulatory Auditing: Integrating NIST AI RMF, ISO, and GDPR frameworks directly into the SDLC to generate immutable data provenance, lineage, and mathematical state logs that pass strict regulatory scrutiny. Expanding the Creator Base: Empowering product managers and domain experts to rapidly prototype and validate solutions alongside senior system architects, drastically accelerating time-to-market for financial tools. 🌐 More about FINOS: https://www.finos.org/📧 Join our newsletter: https://www.finos.org/sign-up🎙️ Listen to our Open Source in Finance Podcast: https://www.youtube.com/@FINOS/podcastsLinkedIn: https://www.linkedin.com/company/finosfoundation

In this edition of our special bi-weekly FINOS Zenith / Emerging Technologies SIG highlight, Peter Smulovics (Distinguished Engineer at Morgan Stanley and FINOS Zenith Lead) delivers a comprehensive breakdown of the major shifts reshaping deep tech. Peter explores why artificial intelligence has transitioned from a standalone category into the primary horizontal layer powering quantum fault tolerance, spatial interfaces, physical robotics, and fusion energy. 🗽 Catch Peter Smulovics Live in New York! Ready to explore the bleeding edge of enterprise technology and open-source financial engineering? Join Peter and global technology leaders at OSFF New York on November 4–5, 2026.🎟️ Register Now: https://hubs.ly/Q04n_bZL0🔥 20% OFF DISCOUNT CODE: 26YTOSFFNY20C 🕒 Timestamps:0:00 Introduction: AI as the Universal Horizontal Enabling Layer1:15 Quantum Computing Shift: Moving from Physical Qubits to Fault-Tolerant Logical Units1:55 Commercial Quantum Moats: IBM’s $10B Commitment & Microsoft’s Majorana 2 Lifetimes2:45 Accelerating Post-Quantum Cryptography Migration Targets to 2028–20293:10 Quantinuum’s IPO, Non-Abelian Anyons & Protected Universal Gates4:10 QuEra’s Fault-Tolerance Roadmap & D-Wave’s Gate Model Transition5:05 Spatial Computing Evolution: Snap Ambria Specs, Qualcomm Reality Elite & TART6:40 Enterprise Spatial Upgrades: Apple visionOS 27 & Meta Luxottica Smart Glasses8:00 Orbital Infrastructure: Space Tech Commercialization & Artemis Timelines8:50 Physical AI & Industrial Robotics: NVIDIA Halos, BMW Figure 03 Deployment & Jetson Thor10:50 Photonic AI Accelerators: Running Generative Models on Optical Chips11:15 Energy Infrastructure: US DOE Fusion Roadmap & Microreactor Criticality Milestones12:30 Brain-Computer Interfaces: Paradromics BCI Feasibility & Medtech Innovations 📊 The Paradigm Shift: AI as the Horizontal Enabling LayerThe definition of "emerging technology" has fundamentally inverted. Artificial intelligence is no longer viewed as a isolated software vertical; rather, it has become the underlying operational layer enabling rapid iteration across quantum error correction, spatial computing platforms, physical robotics, and orbital manufacturing. 🏗️ Key Technological Breakthroughs Analyzed:Logical Quantum Computing: The industry has moved away from physical qubit vanity metrics toward fault-tolerant architectures, topological anyon gate protection, and accelerating post-quantum cryptography (PQC) deadlines to 2028–2029. Ambient Spatial Computing: Heavy VR headsets are giving way to AI-native smart glasses, cross-compiler spatial platforms, and advanced lighting/splatting spatial rendering engines. Physical AI & Autonomous Hardware: Full-stack safety frameworks (such as NVIDIA Halos) and Jetson architectures are driving humanoids (like Figure 03) directly into automated automotive manufacturing plants. ⚙️ Why This Matters for Financial EngineeringCryptographic Agility: Hyper-scalers accelerating PQC targets to 2028 require financial systems to immediately audit and upgrade legacy encryption standards. Spatial & Physical Operations: Integrating ambient spatial interfaces and AI-native hardware expands the operational boundary for risk management, quantitative trading setups, and physical asset tracking. 🌐 More about FINOS: https://www.finos.org/📧 Join our newsletter: https://www.finos.org/sign-up🎙️ Listen to our Open Source in Finance Podcast: https://www.youtube.com/@FINOS/podcastsLinkedIn: https://www.linkedin.com/company/finosfoundation#finos #OSFFNewYork #MorganStanley #ZenithProject #EmergingTech #QuantumComputing #PostQuantumCryptography #SpatialComputing #PhysicalAI #Robotics #DeepTech

Erik Seliger (Lead Product Engineer at Sourcegraph) and Robert Lin (Lead Platform Engineer at Sourcegraph) join FINOS Zenith Project Lead Peter Smulovics to break down the mechanics of Agentic Batch Changes. They reveal how implementing a multi-layered agent architecture solves the high economic cost of pure "vibe coding" by combining deterministic scripting pipelines with localized, token-efficient AI coding models.🗽 Catch Us in New York! Ready to scale your repository automation while defending corporate token budgets? Join the enterprise DevOps community at OSFF New York on November 4–5, 2026. 🎟️ Register Now: https://hubs.ly/Q04n_bZL0 🔥 25% OFF DISCOUNT CODE: FINOSREDOSFF25📊 The Problem: The Economic Nightmare of Unbounded "Vibe Coding" When global financial institutions attempt large-scale codebase modifications—such as upgrading framework dependencies across 25,000 active code services—relying on a single coding model introduces severe fragmentation. Traditional coding assistants immediately blow through millions of context-window tokens trying to execute code changes repo-by-repo. Furthermore, when these blind refactoring attempts trigger continuous integration (CI) failures, the model encounters a non-deterministic loop, spinning out out-of-bounds calculations, custom exceptions, and configuration drift that burns cash without hitting production.🏗️ The Solution: The Multi-Layered Agentic Batch Architecture Sourcegraph delivers a structural framework that decouples project coordination from individual script executions:The Outer Loop Project Manager: Running an orchestration agent governed by immutable system prompts to research, manage repository staging, and continuously audit diff outputs.Deterministic Baseline Integration: Using the planning agent to write efficient, low-cost scripts for programmatic tasks (like straightforward find-and-replace or package manager updates) instead of burning tokens on every file.Inner Loop Containment: Deploying localized coding engines (such as Cody or Cloud Code) strictly inside isolated base Docker images to handle ambiguous, complex code edge cases on demand.⚙️ Why This Matters for Financial EngineeringAutomated CI Hook Blocks: Injecting localized AI hooks directly into failing build logs, empowering inner loop agents to dynamically correct pipeline issues in real time based on compilation feedback.Deep MCP Integration: Setting up customized Model Context Protocol (MCP) layers to bridge context lines, letting inner agents instantly ask the master outer orchestrator for repository-scale search data when hitting technical roadblocks.🌐 More about FINOS: https://www.finos.org/📧 Join our newsletter: https://www.finos.org/sign-up🎙️ Listen to our Open Source in Finance Podcast: https://www.youtube.com/@FINOS/podcastsLinkedIn: https://www.linkedin.com/company/finosfoundation