Systems | Development | Analytics | API | Testing

Unified Data Governance for Safe & Trusted AI Agents

Jul 7, 2026 By Cloudera, Inc. In Cloudera
Hey, did you know your AI agents could be making decisions based on data they were never meant to see? When enterprise data governance is fragmented across separate tools, it creates severe blind spots. Rogue AI agents can over-index, modify, or even accidentally delete production databases simply because proper data guardrails weren't uniformly enforced. In this video, we tackle the root cause of why 79% of enterprise AI initiatives stall and show you how to build a unified data fabric that secures your hybrid estate.
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appian

Solving Agent Sprawl: Why AI Agents Need an Operational Context Layer

Jul 7, 2026 By Donna Namorato In Appian
Since its inception, agentic AI has felt like a distant aspiration. Today, agents are here, and enterprise adoption is accelerating. Gartner predicts that by 2028, the average global Fortune 500 enterprise will have more than 150,000 AI agents in use, up from fewer than 15 in 2025. Agents arrive with incredible, broad intelligence, but lack the knowledge of your operating model: your customers, policies, approvals, exceptions, business rules, systems, and operational history.
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appian

Automating the Exception: How a Second LLM Judge Drives Straight-Through Processing

Jul 7, 2026 By Yin He In Appian
Document-centric workflows have been difficult to automate and required human intervention. Attempts to automate document handling often failed or did not scale, because legacy intelligent document processing (IDP) systems were fragile. They often required manually retraining models on dozens of documents just to identify specific fields—only to repeat the process whenever a format changes. The result was a costly cycle of maintenance and manual data entry.
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katalon

How to Build a Scalable Enterprise Testing Strategy for Engineering Teams

Jul 7, 2026 By Arvind Kumar Choudhary In Katalon
Enterprise software today isn't just complex, it's mission-critical. A single production issue can disrupt operations, impact revenue, and erode customer trust overnight. Yet despite years of investment in enterprise test automation and growing QA headcount, many organizations still ship broken software and miss release windows. The uncomfortable truth? Enterprise software doesn't fail because teams aren't testing enough.
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integrate

How to Consolidate Multi-Bank Transaction Data With Low-Code ETL

Jul 7, 2026 By Donal Tobin In Integrate
Every finance team managing multiple banking relationships knows the pain: downloading statements from six different portals, copying transaction data into spreadsheets, and spending hours reconciling figures that should match but don't always align. With businesses losing significant productivity due to manual data handling and delayed system synchronization, multi-bank data consolidation has become a critical operational challenge.
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integrate

Real-Time Fraud Detection Pipelines: How Fintechs Use ETL for Streaming Data

Jul 7, 2026 By Donal Tobin In Integrate
Your fraud detection system analyzes yesterday's transactions while criminals steal millions today. Financial institutions lose an estimated $33 billion annually to card fraud alone, much of it preventable with real-time detection capabilities. Traditional batch processing that analyzes data hours or days after transactions occur simply cannot keep pace with sophisticated fraud schemes exploiting the settlement window gap.
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How Product Teams Close Engineering Gaps Without Long Hiring Cycles

How Product Teams Close Engineering Gaps Without Long Hiring Cycles

Jul 7, 2026 By SystemsDigest In SystemsDigest
A product roadmap rarely stalls because the whole team is stuck. It stalls because one person is. Picture a release that depends on a payment integration, a real-time feature, or a migration to a framework nobody in-house has shipped before. The rest of the work is ready. But that one gap sits in the critical path, and everything downstream waits behind it.
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REPLAY: Stop Mocking APIs Manually | Use Digital Twin Sandboxes and Find Regressions in CI Quickly

Jul 6, 2026 By Keploy In Keploy
Your developers — and your AI agents — need a safe way to test against production-like behavior. Keploy records real API traffic and replays it as a digital twin sandbox, so you can catch regressions before they ship. No manual mocks. No production access. No complex test environment setup. Record → generate tests and mocks → replay in CI.
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ClearML

Automating the Embodied AI Pipeline: A ClearML and Dell Robotics Proof of Concept

Jul 6, 2026 By Adam Wolf In ClearML
Training models for physical robots is harder than training a typical model. The data has to be collected by hand through teleoperation, every change has to be tested on real hardware, and the loop from data to deployment runs constantly. In a recent proof of concept with a Singapore government agency, ClearML, Dell Technologies, and Hugging Face’s LeRobot framework turned that high-touch, manual process into an automated pipeline.
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bugfender

Debug logging for web and mobile apps

Jul 6, 2026 By Aleix Ventayol In Bugfender
Debug logging is a particular form of logging that records detailed information about how an application behaves during execution, so we can identify, understand, and fix issues. This guide will give you a rookie-to-pro guide to debug logging, showing you: By the end, you will have a clear, practical approach to using debug logs effectively in real applications.
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