infoq

In this podcast, Michael Stiefel spoke to Luca Mezzalira about software architecture as a socio-technical discipline that balances technological design with an organization's social and managerial context. They explored how architectural choices must be adapted to a company's unique culture and team structure rather than relying blindly on universal best practices. By Luca Mezzalira

This InfoQ Trends Report offers readers a comprehensive overview of emerging trends and technologies in the areas of AI, ML, and Data Engineering. This report summarizes the InfoQ editorial team’s and external guests' view on the current trends in AI and ML technologies and what to look out for in the next 12 months. By Srini Penchikala, Chip Huyen, Daniel Dominguez, Hien Luu, Arthur Casals

Andrew Martin discusses why AI security is fundamentally tied to Kubernetes security. He shares insights into the evolving AI threat landscape, covering non-deterministic agent behavior, GPU supply chain vulnerabilities, shared compute risks, and practical defense strategies - from prompt firewalling to agentic identity - to help secure cloud-native AI workloads in production environments. By Andrew Martin

Hillel Wayne discusses how formal methods principles - such as property strength, state-space verification, and environment assumptions - help software engineers uncover critical architectural bugs early. He shares practical mental models like "adversarial non-determinism" to reason about concurrency, edge cases, and distributed system failures without needing complex mathematical tools. By Hillel Wayne

Erik Peterson discusses why AI spend is eclipsing cloud bills, triggering a major software engineering cost crisis. He explains tokenmaxxing, unit economics, forced developer promotions, and why fully autonomous AI will write, deploy, and govern code by 2028. By Erik Peterson

Prasanna Vijayanathan and Renzo Sanchez-Silva share how Netflix tackles observability across 38M events/sec. They discuss replacing reactive monitoring with an AI-driven operational ontology and agentic workflows using Claude and graph databases. They explain how unifying MELT telemetry into queryable knowledge graphs enables automated triaging, root-cause analysis, and self-healing systems. By Prasanna Vijayanathan, Renzo Sanchez-Silva

Hannah Foxwell shares how the surge of agentic coding forces engineering leaders to rethink team dynamics. She discusses three key anchors for navigating AI-driven velocity: building software worth building, prioritizing automated safety over speed, and preserving the human element through generalist "broken comb" skillsets and sustainable on-call practices. By Hannah Foxwell

Susan Chang explains how Elastic transitioned from siloed, ad-hoc AI agent evaluations to a unified, production-grade framework. She discusses balancing LLM-as-a-judge with deterministic rules, bridging Python data science evals with TypeScript production code, and implementing deep tracing to catch regressions across complex RAG and cybersecurity workloads while preserving domain context. By Susan Chang

Swaroop Chitlur and Sidd Kodwani share DoorDash’s journey building an internal GenAI platform. They discuss core architectural bets, transitioning from vendor-first setups to open-weights models, navigating LLM and agent gateways, and balancing accuracy, latency, and cost for over 5,000 internal users. By Siddharth Kodwani, Swaroop Chitlur

The panelists discuss how production operations are evolving with AI, turning operational data into actionable insights for incident response. They explain how automation and new architectural practices help engineering teams build more understandable systems, while exploring how AI reshapes software delivery and changes the historically deterministic nature of production applications. By Michael Hausenblas, Sujana Sooreddy, Noam Levi, Renato Losio

Patrick Debois discusses how to manage, evaluate, distribute, and observe context using proven software engineering practices. He shares how treating context like code - complete with testing, CI/CD, package managers, and security scanning - enables engineering leaders to reliably scale AI coding agents, maintain control over non-deterministic outputs, and build long-term organizational knowledge. By Patrick Debois

In this podcast, Michael Stiefel spoke to Erin Doyle about how artificial intelligence tools are transforming the software development process, and how they are affecting software engineers. Architecture skills are now critical because engineers need to understand how to build systems and handle the constraints and associated ambiguity in the requirements. By Erin Doyle

Mallika Rao explains that the true complexity of adaptive recommendation systems lies outside model architecture. She discusses how real-time feedback loops, retrieval freshness, multi-stage orchestration, and end-to-end latency budgeting enable systems to continuously learn and evolve in production under real-world operational constraints like latency, cost, and observability. By Mallika Rao

Christine Lemmer-Webber and David Thompson explain how Spritely's architecture solves core decentralization challenges. They share insights on capability-based access control with Goblins, actor-model communication via OCapN, and petname systems. Discover how Spritely leverages Scheme, WebAssembly (Hoot), and local-first CRDTs to build secure, peer-to-peer applications without central servers. By Christine Lemmer-Webber, David Thompson

Jim Gough and Andreea Niculcea explain how Morgan Stanley uses Architecture as Code with CALM to modernize its API program. They demonstrate integrating Model Context Protocol (MCP) and Agent-to-Agent (A2A) communications, enforcing automated governance through deployment gates, and executing zero-downtime platform upgrades to safely scale enterprise AI and agentic workflows. By Jim Gough, Andreea Niculcea

OpenAI’s Vinoth Govindarajan discusses why production AI agents fail beyond model hallucination. Using real-world case studies like OpenClaw, he explains the key principles of reliable agent harnesses: establishing explicit state ownership, serializing concurrent state mutations, scoping execution authority, and validating actions at the user-visible edge. By Vinoth Govindarajan

Ajay Prakash discusses how LinkedIn overcomes AI agent limitations in large codebases. He explains Contextual Agent Playbooks and Tools - built on Model Context Protocol (MCP) - which serves procedural memory, code search, and runbooks directly to coding agents. Prakash shares architectural details and operational guardrails that deliver a 20% productivity boost with zero loss in reliability. By Ajay Prakash

In this podcast Shane Hastie, Lead Editor for Culture & Methods spoke to Elisabeth Hendrickson and Joel Tosi about systems thinking as a lens for software delivery, the cultural "levers" leaders and teams can pull to shape organizations, and building thriving, sustainable teams amid AI-driven change. By Elisabeth Hendrickson, Joel Tosi

In this episode, members of the InfoQ AI and ML editorial staff and friends of InfoQ discuss the current trends in the domain of AI, ML and Data Engineering. One of the regular features of InfoQ are the trends reports, which each focus on a different aspect of software dev. These reports provide the InfoQ readers and listeners with a high-level overview of the topics to pay attention to this year. By Chip Huyen, Daniel Dominguez, Hien Luu, Arthur Casals

Rachel Lee Nabors, former React core team member, dives into the architecture and philosophy behind local-first applications, exploring how small language models (SLMs) running directly on-device can deliver privacy-focused, low-latency, and cost-effective alternatives to cloud-hosted LLM wrappers. By Rachel Lee Nabors

Brendan O'Leary discusses how real-world AI adoption depends on building trust rather than chasing model benchmarks. Drawing from 40 trillion tokens of usage data, he explains the four rungs of the AI adoption ladder and analyzes key failure points in context construction, multi-model routing, and feedback loops to help engineering leaders successfully scale agentic workflows. By Brendan O’Leary

Nicholas Herring shares how EVE Online tackles distributed tracing and observability across a 20-year-old MMO architecture. He discusses leveraging head-based, dynamic exponential moving average (EMA), and deterministic sampling techniques to maintain high system fidelity, control telemetry costs, and avoid performance degradation under high-throughput real-time game simulations. By Nicholas Herring

In this podcast, Michael Stiefel spoke to Joy Ebertz about how the use of artificial intelligence is changing the way architects and developers do their jobs. Focusing on the long term success of the business is one of the most neglected tasks of an architect. This task becomes particularly important as the nature of artificial intelligence changes the landscape of software development. By Joy Ebertz

In this podcast, Shane Hastie, Lead Editor for Culture & Methods, spoke to Matt Gjertsen, founder of Built and author of Minimum Viable Manager, about the three foundational skills every first-time manager needs, how to build trust and give effective feedback, and why human leadership skills matter even more in the age of AI. By Matt Gjertsen

Matthew Liste explains the 12 core principles of building scalable, reliable platforms for enterprise tech teams. From avoiding technical debt and managing deprecation to building a strong engineering culture, Liste discusses how to design infrastructure platforms that reduce complexity, streamline developer workflows, and deliver long-term value for senior architects and engineering leaders. By Matthew Liste

In this episode, Chris Swan, engineer at Atsign and security track host at QCon London, reflects on the ideas shared during QCon London 2026 and teases topics of the future edition. The discussion explores how security engineering is shifting from relying on human vigilance toward automated, system-level defences across the entire application stack. By Chris Swan

Ian Cooper discusses managing asynchronous APIs in event-driven architectures at scale. He explains the "ABCs" of messaging (Address, Binding, Contract) and shares how to tackle discovery, governance, and provisioning using AsyncAPI, CloudEvents, schema registries, and automated CI/CD infrastructure pipelines based on real-world engineering practices at Just Eat Takeaway. By Ian Cooper

Dhruva Juloori discusses how Uber maintains green mainlines across massive monorepos handling 65,000+ monthly changes. He explains how SubmitQueue uses binary speculation trees, conflict analysis, and machine learning models to predict build success and execution times. Juloori shares how bypassing large diffs slashed CI resource usage by 53% while accelerating PR landing times by 37%. By Dhruva Juloori

Gonzalo Maldonado explains how technical leaders can translate engineering skills into startup success using their "VC Abstraction Layer Knowledge" (VALK). They share hard-won lessons on finding product-market fit, structuring pitches like technical docs, avoiding co-founder drama, and evaluating internal platforms as commercial ventures in a post-ZIRP environment. By Gonzalo Maldonado

Ben Maraney shares how Forter demystified AI agent creation for technical and non-technical staff. He discusses leveraging custom MCP servers, combining no-code and code-based platforms, sidestepping complex RAG setups, and aligning security and legal teams to accelerate internal agent adoption across R&D. By Ben Maraney

In this episode, Meryem Arik, Clara Higuera Cabañes, and Jeff Smith demystify the current state of AI in the enterprise. The discussion navigates the "industrial revolution" moment in AI adoption, exploring why companies are rushing toward these technologies to maintain competitive advantages while grappling with reliability, ethical considerations, and the evolving role of software engineering. By Meryem Arik, Clara Higuera Cabañes, Jeff Smith

Balakrishnan Ramdoss discusses how to architect production-grade, AI-powered conversational apps at scale. He explains how to overcome model latency, leverage server-driven UI and Backend-for-Frontend patterns to dynamically render multi-modal interfaces, optimize prompts for UI selection, and integrate low-latency, privacy-first on-device AI for mobile applications. By Balakrishnan Ramdoss

David Gudeman discusses battle-tested architectural patterns for resource-constrained engineering teams. Drawing from over a decade of startup experience, he explains how pairing GCP, Firebase, and Cloud Run accelerates product-market fit. Learn how to eliminate redundant frontend state, structure event-driven backends, and maintain lean DevOps while building for long-term scalability. By David Gudeman

In this episode, Sahil Agarwal talks about the critical challenges of identity, authorisation, and security in the age of AI agents. Agarwal introduces the DPACT framework (Delegation, Policy, Auditability, Context, and Time) as a blueprint for building responsible, guardrailed agentic systems, moving away from simple token-based access toward bounded, delegated authority. By Sahil Agarwal

Phillip Mortimer discusses the shift toward write-only software driven by AI code generation. He explains why traditional pull requests are broken and shares how engineering leaders can manage complexity by decoupling intent from implementation, automating code reviews, and building self-healing architecture to unleash developer creativity across senior engineering and architecture teams. By Phillip Mortimer