New Tech Forum provides a venue to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content.
Git provides the structure that makes agentic workflows in software engineering viable. Other disciplines need an equivalent backbone.
Devops teams that cling to component-level testing and basic monitoring will struggle to keep pace with the data demands of AI.
Retrieval-augmented generation breaks at scale because organizations treat it like an LLM feature rather than a platform discipline. Enterprises that succeed with RAG rely on a layered architecture.
For IT and HR teams, SLMs can reduce the burden of repetitive tasks by automating ticket handling, routing, and approvals, while providing substantial cost savings versus LLMs.
The most significant advances in the coming year won’t come from building larger models.
AI initiatives don’t stall because models aren’t good enough, but because data architecture lags the requirements of agentic systems.
MongoDB is still the most popular NoSQL document database for developers, but compatible alternatives such as DocumentDB offer more choices than ever.
Supply chain risk is unavoidable, but not unmanageable. Proactively prevent supply chain attacks by embedding YARA into developer workflows.
Data contracts are foundational to properly designed and well behaved data pipelines. Kafka and Flink provide the key capabilities.
Four big lessons, seven practical tips, three useful patterns, and five common antipatterns we learned from building an AI CRM.
From generating test cases and transforming test data to accelerating planning and improving developer communication, AI is having a profound impact on software testing.
Treating annotation as a data understanding problem, rather than a labeling workflow challenge, can systematically drive down error rates and reduce the time and cost of producing high-quality data sets.
Agentic AI systems need a deep understanding of where they are, what they know, and the constraints that apply. Context engineering provides the foundation.
While computer-use models are still too slow and unreliable, browser agents are already becoming production-ready, even in critical sectors such as healthcare and insurance.
With agentic AI, the database must evolve from a passive ledger to an active reasoning engine that informs, guides, and enables autonomous action.
If you force developers to learn Helm, Kustomize, or how Kubernetes manifests work, you are wasting their time. Give them environments instead.
Understanding the difference between deterministic and non-deterministic systems is key to thriving in this new world of AI.
Telecom networks are no longer just dumb pipes for connecting applications. New APIs are giving developers access to device location and other powerful network functionalities.
A successful IDP removes barriers to efficiency and puts both developers and platform engineers on self-service golden paths. Build your IDP with these 10 principles in mind.
A six-step framework for building a production-ready AI agent that handles those repetitive tasks you don’t want your IT team spending their days on.
How OpenRewrite uses Lossless Semantic Trees to deliver a full-fidelity representation of code, making transformations precise, repeatable, and auditable.
Most of today’s operational models were built for stability and predictability. Agentic AI doesn’t play by those rules.
Asynchronous I/O, OAuth authentication, expanded SQL standards support, and new extension capabilities give developers faster performance, stronger security, and greater flexibility.
Rather than spending their time executing manual queries, data analysts will increasingly operate like AI engineers—reviewing, refining, and validating AI-generated outputs.
AI in healthcare and other industries won’t fly unless compliance is baked in from day one — not bolted on after.
Why Kubernetes is powering the next wave of enterprise infrastructure.
Out of 18 new features, three improvements sharpen Java’s edge for cloud-native, containerized, and cost-sensitive deployments.
The GitHub OAuth attack exposed a security blind spot in the ever-growing web of permissions spanning developers, service accounts, and third-party OAuth apps. Here’s how to address it.
A stack-based approach to IDPs emphasizes reusability, autonomy, and visibility, creating a standardized but flexible system where teams can define and deploy their own devops stack.
Microsegmentation used to mean separating network zones. Today, it means separating workload behaviors. It’s a new discipline that requires new tools.
Databases will soon be capable of monitoring their own health, identifying bottlenecks, adjusting configurations, and even rerouting traffic in real time.
CodeRabbit combines code graph analysis and the power of large language models to identify issues in pull requests and suggest improvements, or even generate those improvements in a new branch.
Chasing the goal of zero CVEs may tick off some compliance check boxes, but it will not fully address the evolving and holistic threats to enterprise security.
Combining low-code, generative AI, and AI-driven guidance, Mentor provides intelligent, context-aware support from application creation through monitoring and governance.
Technical steps your organization can take to protect itself from the Codefinger ransomware attack and other modern exploits.
Dead code, security false positives, and idle cloud capacity are the leading drags on devops in Java environments. Here’s how to address them.
Containers give developers flexibility, speed, and simpler deployment. Virtual machines offer superior workload isolation and security. We can have both.
A step-by-step guide to deploying, configuring, and testing a multi-AZ, multi-region SQL Server FCI in the Azure cloud, complete with a PowerShell script that handles the networking configuration.
It’s time to clarify misperceptions and examine what serverless really looks like, especially in a world increasingly shaped by AI and the need for rapid innovation and scaling.
The embedded Python Processing Engine in InfluxDB 3 allows developers to write Python code that analyzes and acts on time series data in real time.
Event-driven architecture needs the same governance and control as APIs. Kong’s new Event Gateway brings familiar Kong API management, plugins, and policies to Apache Kafka.
Security should be a core competency of every developer. Follow these 10 steps to bring security into every phase of the software development life cycle.
It’s time to bridge the technical gaps and cultural divides between DevOps, DevSecOps, and MLOps teams and provide a more unified approach to building trusted software. Call it EveryOps.
Platform engineering teams face mounting challenges across their expanding technological landscape. Automation and self-service are the way forward.
Application modernization isn’t a one-time project but an ongoing commitment to innovation. Here’s how to get started.
The Vector API gives Java developers everything they need to tap into CPU-level performance gains for numerically intensive operations.
Pet projects are an excellent way for developers to learn new skills and technologies and to test fresh concepts and approaches. Companies can benefit too.
Automated RAG, PII sanitization, and LLM orchestration in Kong AI Gateway 3.10 help teams simplify, scale, and secure their AI development workflows.
By automating routine tasks and reducing friction, AI-powered IDEs allow developers to spend more time developing. Here’s what they add to the traditional IDE feature set.
Flexera 2025 State of the Cloud Report reveals a rise in workloads returning to data centers even as generative AI use and public cloud adoption accelerate.