Systems | Development | Analytics | API | Testing

Chat with Your Data: The Official Databox MCP

Jan 31, 2026 By Databox: Modern BI Software In Databox
We are thrilled to launch the official Databox MCP (Model Context Protocol). This open standard server bridges the gap between your business data and your favorite AI tools, turning general-purpose LLMs into specialized data analysts that know your business data. Stop manually exporting CSVs or taking screenshots of dashboards. With Databox MCP, you can connect 130+ data sources (Google Analytics, HubSpot, Salesforce, Stripe, and more) directly to tools like Claude, ChatGPT, Cursor, and n8n.
View Video
kong

Agentic AI Cost Management: Stopping Margin Erosion and the Fragmentation Tax

Jan 30, 2026 By Alex Drag In Kong
While every organization races to deploy AI agents faster, finance departments are watching something alarming unfold—and it will play a large part in determining who survives the agentic era. The numbers are stark: 84% of companies report more than 6% gross margin erosion from AI costs. Within that, 26% report erosion of 16% or more. And only 15% of companies can forecast AI costs within ±10% accuracy—the majority miss by 11-25%, and nearly one in four miss by more than 50%.
Read Post
TestQuality

How to Evaluate an AI Test Case Builder for Your QA Workflow

Jan 30, 2026 By Jose Amoros In TestQuality
Choosing the right AI test case builder requires evaluating integration depth, not just feature lists. Evaluate AI test case builders based on how they enhance your current workflow rather than how many features they advertise. Your QA team is drowning in test cases. Requirements change daily, releases accelerate weekly, and manual test creation has become the bottleneck everyone acknowledges but nobody has time to fix. An AI test case builder seems like the obvious solution.
Read Post
databox

How an AI Assistant Can Work With Your Business Data with MCPs

Jan 30, 2026 By Alexander B. Pavlinek In Databox
And instead of getting a generic answer or being told to check your dashboard, the AI pulls the exact numbers from your company’s data and gives you a real answer in seconds. This is no longer science fiction. A new technology called MCP (Model Context Protocol) makes this possible. It’s a standardized way for AI tools to securely connect to your business intelligence and analytics platforms and actually work with your real data.
Read Post
smartbear

Comparing the top AI test automation tools

Jan 30, 2026 By SmartBear In SmartBear
AI is reshaping test automation fundamentals. Features that once required hours of manual scripting can now adapt automatically to UI changes, generate realistic test data on demand, and help teams predict which tests matter most. For QA engineers evaluating automation platforms, understanding how AI capabilities differ has become essential. This comparison examines SmartBear TestComplete, Tricentis Tosca, and Ranorex through their AI-powered features.
Read Post
kong

Agentic AI Governance: Managing Shadow AI and Risk for Competitive Advantage

Jan 30, 2026 By Alex Drag In Kong
While every organization races to deploy AI agents faster, a quieter crisis is compounding in the background, and it will play a large part in determining who survives the agentic era. The numbers are stark. Too many executives see AI governance as a brake on innovation or something to figure out later, after the speed problem is solved. With agentic AI, that's backwards.
Read Post
speedscale

Refactor Safely with AI: Using MCP and Traffic Replay to Validate Code Changes

Jan 29, 2026 By Matthew LeRay In Speedscale
So as software engineers using AI coding assistants, we’re quickly learning of a new anti-pattern: Hallucinated Success. You give your agent (e.g. Claude via terminal or various IDE code assistants) the command “refactor the billing controller.” The agent happily complies, churning out nice clean code. The agent even goes so far as to write a new unit test suite that passes at 100%. You integrate it. Your test suites pass. Your production code breaks. Why?
Read Post
ably

Why orchestrators become a bottleneck in multi-agent AI

Jan 29, 2026 By Faye McClenahan In Ably
Complex user tasks often need multiple AI agents working together, not just a single assistant. That’s what agent collaboration enables. Each agent has its own specialism - planning, fetching, checking, summarising - and they work in tandem to get the job done. The experience feels intelligent and joined-up, not monolithic or linear. But making that work means more than prompt chaining or orchestration logic.
Read Post

Copyright © 2026 OpsMatters™. All rights reserved.