Systems | Development | Analytics | API | Testing

2026 Predictions: What's Next for Data Streaming and AI | Life Is But A Stream

Jan 27, 2026 By Confluent In Confluent
AI isn’t just evolving—it’s reshaping who your customers are, how systems operate, and what real time really means. From machines making purchase decisions to agents increasing query volume across databases, the realities of 2026 are forcing leaders to rethink data architecture and governance strategies at a fundamental level. In this episode, Joseph is joined by Will LaForest (Field CTO, Confluent), Adi Polak (Director of Developer Advocacy & Experience, Confluent), and independent analyst, Sanjeev Mohan, to break down critical insights from Confluent’s 2026 Predictions Report.
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Powering agentic software quality with MCP servers | From the Bear Cave

Jan 27, 2026 By SmartBear In SmartBear
In this From the Bear Cave session, Dan Faulkner, CEO of SmartBear, and Vineeta Puranik, CTO/CPO of SmartBear, discuss why agentic automation is becoming essential in software development and delivery, how MCP Server enables connected autonomy across the SDLC, and what this transformation means for business outcomes and the future of human + AI collaboration.
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thoughtspot

SpotCache: Scale AI-ready data without cloud-spend surprises

Jan 27, 2026 By Shruthi Bezawada In ThoughtSpot
AI is changing how work gets done. But for many data leaders, it’s also creating a new challenge: managing the cloud bill. As more people (and more AI agents) query data, cloud data warehouse (CDW) spend can spike fast. Costs become harder to predict, and teams end up making tradeoffs—scaling AI insights or staying within budget. That tension creates a real bottleneck on the path to becoming AI-ready.
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tricentis

AI in QA: What leading quality experts want every team to know

Jan 27, 2026 By Sarah Welsh In Tricentis
Our goal with the Tricentis blog is to distill insights that help QA professionals navigate the massive, AI-driven transformation happening across the software delivery landscape. To that end, I reached out to experts across Tricentis, from product and services to marketing and strategy, to hear what they’re really thinking about AI in QA right now. This group brings decades of experience building testing products, guiding enterprise transformations, and shaping how organizations adopt AI.
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perforce

What is an MCP? Breaking Down the Model Context Protocol

Jan 27, 2026 By Perforce In Perforce
70% of teams are already integrating generative AI tools into their daily workflows, according to our 2025 State of Game Technology Report. Now more than ever, teams are looking to connect their AI tools to the services and applications they rely on to get work done. To address this issue, the industry has begun to standardize using the Model Context Protocol (MCP) to connect their existing tools and LLMs like Claude, GPT, and Gemini.
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kong

Building Secure AI Agents with Kong's MCP Proxy and Volcano SDK

Jan 27, 2026 By Eugene Tan In Kong
Modern AI applications are no longer just about sending prompts to an LLM and returning text. As soon as AI systems need to interact with real business data, internal APIs, or operational workflows, the problem becomes one of orchestration, security, and control. The challenge is to build secure AI agents without embedding fragile logic or exposing sensitive systems directly to a model. This is where a layered architecture using Volcano SDK, DataKit, and Kong MCP Proxy becomes compelling.
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TestQuality

Best Practices for AI in CI/CD QA Pipelines

Jan 27, 2026 By Jose Amoros In TestQuality
AI transforms CI/CD testing from reactive bug detection into proactive quality assurance that accelerates release cycles while improving software reliability. Start embedding AI into your testing workflows now because teams that wait will struggle to match the velocity of competitors who already have. Continuous integration and continuous deployment pipelines have become the backbone of modern software delivery.
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kong

A Developer's Guide to MCP Servers: Bridging AI's Knowledge Gaps

Jan 26, 2026 By Adam DeHaven In Kong
Have you ever asked an AI assistant to generate code for a framework it doesn't quite understand? Maybe it produces something that looks right, but the syntax is slightly off, or it uses deprecated patterns. The AI is working hard, but it lacks the specific context it needs to truly help you. The Model Context Protocol (MCP) was designed to bridge this knowledge gap by giving AI assistants access to domain-specific knowledge and capabilities they don't have built in.
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ClearML

Multi-Node Training with ClearML

Jan 26, 2026 By ClearML In ClearML
Orchestrating distributed AI workloads Distributed (multi-node) training has become a requirement rather than an optimization for many modern AI workloads. As model sizes grow, datasets expand, and training timelines tighten, teams increasingly rely on multiple machines, often with multiple GPUs each, to complete training efficiently.
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alphabin

Top 25 Test Generating Tools

Jan 26, 2026 By Pratik Patel In Alphabin
Software testing was once a slow and repetitive process that developers accepted as unavoidable, often consuming significant time without delivering proportional value. Traditional manual testing struggled to scale with growing application complexity and rapid release cycles. In 2026, test generating tools have reshaped this landscape by introducing automated test generation, AI-driven logic, and intelligent coverage strategies.
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