Share individual MITechCon 2026 sessions with ready-to-use copy, a public mitechcon.org destination, and a UTM-ready partner link.
Built from the current main-site generated agenda data and reviewed session social copy where available.
Artificial intelligence is changing the way organizations operate, but one of the biggest questions leaders face remains the same: Should we build it ourselves or buy it? The answer isn’t just about cost.
5G promises low latency, but Android stacks can still bottleneck real-time apps. Real-time mobile experiences need the network, operating system, and edge architecture to work together. "5G-Optimized Android System Architecture for Low-Latency Applications" gives Patel Riddhi a way to make the issue concrete through architecture patterns for faster mobile and edge workloads.
Synthetic USB models miss the messiness of real devices. Verifying USB Host designs-under-test (DUTs) is increasingly difficult as the USB ecosystem continues to expand across device types, protocol modes, and class behaviors. "Accelerating USB Host Verification Using Real USB Devices in a Virtualized Device Modeling Framework" turns that scenario into a session where Suchir Gupta shows how physical hardware can accelerate host verification in virtualized environments.
Static routing struggles when latency, cost, and reliability change in real time. Modern distributed systems increasingly rely on real-time decisions across complex service ecosystems. In "Adaptive Machine Learning for Real Time Transaction Routing in Distributed Systems", Jayaseelan Shanmugam connects adaptive ML to routing choices that respond as conditions change.
Pipeline failures should not wait for someone to notice a bad report. Every data engineer knows the 2 AM pipeline failure — the one nobody notices until Friday's report is wrong. "Agentic Loops in the Data Stack: From Pipeline Failure to Auto-Remediation" turns that scenario into a session where Varun Joshi walks through agentic patterns for monitoring, root cause analysis, and auto-remediation.
Sepsis care depends on fast signals across fragmented workflows. The hard part is keeping trust intact as volume, ownership, definitions, and expectations change. In "AI and Digital Transformation in Sepsis Management for Smarter Healthcare Delivery", Swapna Chimanchodkar makes the next step concrete: how AI and predictive analytics can support earlier detection and response.
AI-powered vision can improve quality control only when it adapts to real production conditions. Quality systems have to respond to variation on the line, not just perform in a clean lab setup. "AI Influence on Reliable Machine Vision" lets Mike Koper, CmfgE connect vision data, AI, and manufacturing quality controls to the conditions where defect detection has to work.
Some AI decisions need to happen where the data is created. The architecture has to account for devices, models, telemetry, and observation close to where the signal starts. In "AI on the Edge", Jared Rhodes moves from the symptom to a repeatable path for choosing, testing, shipping, and observing edge models, helping attendees see where better decisions start.
Modernization stalls when teams chase technology before value. When dealing with a 15-year-old platform, the question isn’t just how to modernize but what to modernize. The session "Blending Product Thinking with Software Modernization" picks up from there, with Brian McKeiver grounding how product thinking helps decide what to refactor, rebuild, or leave alone in real implementation choices.
Finance AI breaks when metrics and pipelines are not trusted. Before finance can safely use AI or agentic workflows, the data foundation has to be trusted, governed, and explainable. The answer gets practical in "Building an AI-Ready Finance Lakehouse with dbt, Governed Metrics, and Databricks", where Mou Rakshit shows how dbt, Databricks, Unity Catalog, and governed metrics create an AI-ready lakehouse.
Oracle AI Database 26ai introduces a unified, enterprise-grade foundation for building AI-powered applications close to the data. This session explores vector search, in-database embeddings, and generative AI orchestration.
Multi-agent AI gets harder when agents need to coordinate at scale. Coordination patterns matter once agents have to share state, communicate, and keep work moving across a system. Mary Grygleski uses "Building Scalable Multi-Agentic AI Systems: Orchestrating Agents with Event-Driven Approach" to follow that thread into event-driven patterns for communication, memory, workflow state, and MCP, giving attendees a practical way to bring the lesson back to their own systems.
Enterprise investigations slow down when evidence is scattered across systems. The challenge is bringing retrieval, agent workflow, and human oversight together without burying investigators in more noise. In "Case Study: Designing AI-Augmented Enterprise Investigation Systems", Santosh Vasudevan connects AI agents and RAG to investigation workflows where speed still needs human judgment.
Public-sector AI works best when it fits existing workflows. Public agencies have already experimented with basic AI tools, and many are now asking a more meaningful question: how can AI actually support the work that public staff do every day? The answer gets practical in "Civic Agents in Practice: The Next Phase of AI for Government Workflows", where Himanshu Goil shows how Civic Agents can help staff review requests, summarize cases, and reduce repetitive steps.
Public agencies need AI that improves services without adding complexity. Governments are starting to use AI agents to improve how services are delivered—from permits and public works to citizen support. Himanshu Goil brings that problem into "Civic Agents: The Rise Of AI In Modern Public Service" by focusing on practical Civic Agent patterns for pilots, playbooks, and scale, not just the tool names around it.
Conversational BI fails when answers are not trusted. Self-service analytics only works when the data, metrics, and context behind each answer are governed. In "Designing Conversational BI: How Azure Databricks AI/BI Genie Unlocks Self-Service Insights", Mou Rakshit connects Azure Databricks AI/BI Genie to the governance patterns that make self-service insights credible.
Data pipelines rarely fail on day one. The failure usually arrives later, when schema drift, volume growth, and ownership changes expose early shortcuts. Chris Birie uses "Designing Data Pipelines That Don’t Hate You Six Months Later" to follow that thread into design patterns for schema change, idempotency, observability, and growth before handoff gets painful, giving attendees a practical way to bring the lesson back to their own systems.
You can not predict what the AI will do, and that is the whole point. However, this presents some real security issues, as you can not guarantee the agent will not do anything destructive.
Digital transformation fails when leaders start with tools instead of problems. Most organizations do not fall behind because the technology is missing; they fall behind when decisions, change, and readiness stay fuzzy. "Digital Transformation or Digital Chaos? A Practical Guide for Leaders Who Are Done Playing Catch-Up" gives Matt Russell a way to make the issue concrete through a framework leaders can use before another tool rollout turns into noise.
Your architecture mirrors how your teams communicate. To ignore Conway’s law is a trap that will lead to software that is more expensive, and less effective, especially in the micro-services and composable era. Luis Fernandez uses "Don’t Let Your Org Chart Write Bad Code" to connect the warning signs to the underlying mechanics, showing how Conway's Law and Team Topologies can reduce silos and improve software design.
Autonomous vehicles expand the attack surface across AI, connectivity, and control systems. Cybersecurity threats are not static; it’s a dynamic environment and constantly changing. In "Driving into the Future: Navigating Emerging Cybersecurity Threats in Autonomous Technology", Hemanth Tadepalli moves from the symptom to where emerging risks appear and how to reduce them, helping attendees see where better decisions start.
Enterprise prompts break down when too many rules compete for attention. The issue is not that LLMs are weak; reliability changes once instructions, policies, exceptions, and governance collide inside one workflow. In "Engineering Beyond the “Rule of Eight”", Harsh Ranjan turns that failure mode into architecture patterns for governed AI pipelines that hold up beyond a fragile prompt.
Azure App Service remains the best platform for deploying and scaling .NET applications. It's fully managed, loaded with features, and ready for production use without heavy lifting. But just because your app is running doesn't mean it's optimized.
AI enablement fails when tools arrive without trust, patterns, or coaching. Once the idea reaches a real workflow, the hard questions shift to context, trust, cost, and reliability. In "Floors, Not Ceilings: Lessons from 6 Months of AI Enablement at a $10B Company", Adam Broadbent makes the next step concrete: what a six-month enterprise rollout changed and measured.
LLM features can become new attack surfaces. As AI systems move into enterprise applications, prompt injection, extraction, and jailbreaks turn from demos into real design risks. Ethan DeMott uses "Fortifying AI: Understanding Prompt Attacks and How to Defend Against Them" to connect those attacks to layered defenses teams can design before the feature is exposed.
Organizations do not just need dashboards. They need systems that can sense operational issues, reason over trusted data, and help people act before small problems become expensive disruptions.
Digital tools stall when standard work and operators are disconnected. The idea only matters if it survives real equipment, operators, data quality, and production pressure. Bryan Bauw uses "From Standard Work to Scalable Smart Manufacturing: Making Data and AI Work in Assembly Operations" to show how MES, real-time data, and AI can scale assembly improvements without losing the human workflow they are meant to support.
Analytics teams need scale without losing flexibility or control. The session will also cover different ways to ingest data into BigQuery and best practices, and will conclude with a live demo of the BigQuery console and a showcase of a petabyte demo. In "Google BigQuery - Heart of Smart Analytics toolset on Google Cloud", Vijaykumar Jangamashetti makes the problem concrete by showing how BigQuery supports ingestion, ML, and petabyte-scale analysis on Google Cloud.
Plant data is valuable, but industrial devices and old protocols make collection intimidating. The first useful step is often less about dashboards and more about getting trustworthy signals out of the equipment already on the floor. Grant Pinkos uses "How to get started with collection and storage of industrial data" to make that starting point concrete, from collection and storage to analysis and alerts.
AI can speed up concepting without replacing creative judgment. Once the idea reaches a real workflow, the hard questions shift to context, trust, cost, and reliability. The answer gets practical in "Knights, Castles, and Creative Cloud: Rapid Illustration Concepting with AI", where Rex Rainey shows how Adobe Firefly supports faster iteration while leaving room for craft.
LLMs are powerful tools, but entity extraction still has classic CS problems. A tool can produce results while the underlying computer science problems are still very much alive. Milena F and Andy Schmidt use "LLMs are tools, not solutions: Entity extraction as a computer science problem" to show how context, ambiguity, and pipelines shape reliable results, giving attendees a practical way to bring the lesson back to their own systems.
Medallion architecture is harder under HIPAA. Bronze, silver, and gold layers are easy to sketch, but regulated healthcare data adds PII handling, lineage, access control, and audit pressure to every step. In "Medallion Architecture in Regulated Healthcare: A Hands-On Playbook", Raziullah Khan turns that architecture into patterns teams can use for cleaner, safer, audit-ready data work.
AI-era security demands more than point tools. In an era where cybercrime rivals the largest global economies, securing digital landscapes has never been more critical. In "Microsoft’s Approach to Comprehensive Security in the Age of AI", David Giard moves from the symptom to the decisions underneath, showing how Microsoft frames identity, data, devices, and threat protection across a modern security platform.
Most production code is not going to space, but reliability still matters. Ever wonder how NASA writes software that literally can’t afford to fail? The answer gets practical in "Mission-Critical Code: What NASA’s Power of Ten Can Teach Us", where Jonathan "J." Tower shows how NASA's Power of Ten can simplify everyday software design.
Multi-agent systems get slow and expensive when every agent sees everything. Once the idea reaches a real workflow, the hard questions shift to context, trust, cost, and reliability. In "Multi-Agent Without the Bloat: Designing Lean AI Systems on Azure", Carey Payette ties Azure patterns for scoped context, handoffs, memory, and routing to leaner systems that are easier to debug and scale.
Manufacturing teams need clearer instructions, quality control, and process visibility. Operators need guidance in the moment, not after a quality issue has already moved downstream. Yanesh Naidoo uses "Next Evolution of Advanced Manufacturing with ODIN Digital Solutions" to connect ODIN Workstation to real-time operator guidance and the process visibility advanced manufacturing depends on.
AI security changes when systems answer in probabilities instead of certainties. When I say "probabilistic" outcomes, your mind likely jumps to coins or dice. "Probably Secure: What We Misunderstand About AI And Determinism Is Making Us Less Secure" keeps the conversation practical as Dwayne McDaniel works through where deterministic controls still matter and where they fall short.
Digital transformation programs often stall because they treat technology as the solution rather than one part of it. This paper presents a cross-industry framework showing that sustainable transformation depends on aligning four elements: process excellence, governance, people, and technology.
RAG is only as good as the metadata behind it. Large language models are only as useful as the context you give them. "RAG for Data Engineers: What It Is, Where It Fits, and Why Your Metadata Is the Missing Piece" keeps the conversation practical as Varun Joshi works through where schemas, lineage, query history, and dbt models fit in data engineering workflows.
EV battery leaks are hard to find without slowing production. The idea only matters if it survives real equipment, operators, data quality, and production pressure. "Robotic Tracer Gas Sniffing for Leak Localization in EV Battery Packs and Energy Storage Systems" gives Davy Leboucher room to explain how robotic tracer gas sniffing improves localization across complex battery assemblies.
Municipal infrastructure needs security that respects always-on operations. The risk is easiest to reduce before it becomes an incident, so the workflow matters as much as the warning sign. In "Securing Cyber Physical Systems in Municipal Infrastructure Environments", Venkata Kartheek Reddy Somasani makes the next step concrete: segmentation and field-tested controls for OT/IT cyber physical systems.
Agentic AI can be technically correct and business-wrong. Agents need business meaning, not just access to more tools and data. In "Semantic Layers: The Missing Foundation for Agentic AI in Enterprise", Sajitha Sritharan shows why semantic layers, knowledge graphs, and governance are the foundation agents need before they can answer business questions reliably.
Manual testing slows delivery and weakens release confidence. The testing strategy has to scale across browsers and platforms without burying teams in maintenance work. "Test Smarter with AI: Playwright Automation at Scale on Azure" keeps the conversation practical as Randy Pagels connects AI-generated Playwright tests with Azure App Testing for broader, faster coverage.
AI tools do not fix broken processes, siloed data, or manual approvals. You bought a thousand Copilot licenses. You sent half the company through a prompting workshop. Balazs Horvath uses "The AI is ready, but you are the problem. How to finally be ready for a real AI." to connect the warning signs to what has to change underneath before AI can run real work, keeping the focus on decisions teams can actually make.
Modern products rely on operations customers never see. Delivery, reliability, data trust, and cloud spend all depend on disciplines that become visible only when one gap slows the business. In "The Invisible Stack: Black-Ops of Modern Enterprise", Robert Gray III maps DevOps, AIOps, DataOps, and FinOps as one operating system for modern enterprise work, showing why the invisible stack matters before delivery, incident, or cost problems surface.
Technical leadership can narrow who gets heard and who advances. But, this is not just a talk about enterprise architecture—it is a conversation about courage, purpose, leadership, and creating space for more people to thrive in technology. That is the entry point for "Three Lessons from a Woman in Enterprise Architecture", where Sarah Wimberley shows three lessons for building curiosity, authenticity, and courage in architecture in practice.
AI agents can damage production data before a human sees the mistake. No confirmation prompt fired, no override path existed, and the database changed before anyone could intervene. In "Your AI Agent Just Ran DELETE Without WHERE. Now What?", Jonathan Stewart turns that failure mode into infrastructure-level gates that block destructive commands at execution time.
Coding agents waste context when they read entire files. Every AI coding agent I've used does the same thing: it reads your entire file, every single time. That is the entry point for "Your AI Reads Entire Files. It Only Needs 6%. Here's How We Fixed It.", where Rajkumar Sakthivel shows how AST-aware retrieval and local vector search can give agents only the code they need in practice.
Screenshot evidence
Annotation mode — click an item to leave a note.
