Systems | Development | Analytics | API | Testing

confluent

New in Confluent Intelligence and AI Tools: Making Agents Native to the Stream, Expanded Model Support, New Agent Skills, and Copilot

Aug 18, 2026 By Confluent Staff In Confluent
A customer writes in asking where their order is. The AI support agent checks the account, sees the order marked shipped, and sends a reply. The order was cancelled forty minutes ago. The agent wasn't wrong about anything it could see. It was reasoning over stale data that refreshes every six hours. This is why AI projects stall. Not because the models aren't capable, but because they lack AI-ready data and a reliable view of the current state of the business.
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confluent

Confluent Cloud for Apache Flink: Engine for Mission-Critical, Real-Time Operational Systems and dbt/SQL-Native Home for Data Science and AI

Aug 18, 2026 By Yashwanth Dasari In Confluent
Organizations today are under immense pressure to deliver on two critical fronts: building mission-critical, real-time operational systems and powering the next generation data science and artificial intelligence (AI) workflows with analytics-ready data. Historically, achieving both meant navigating a divided, complex architecture.
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5 Ways Automation Is Improving Food Manufacturing Quality and Safety

5 Ways Automation Is Improving Food Manufacturing Quality and Safety

Aug 18, 2026 By SystemsDigest In SystemsDigest
Food manufacturers have always operated under intense pressure to deliver products that are both consistent and safe. A single contamination event or a batch of mislabeled allergens can trigger recalls, damage brand trust, and put consumers at risk. As production volumes grow and supply chains stretch across borders, manual inspection processes are struggling to keep pace. That's where automation is stepping in, transforming how food is monitored, tested, and cleared for shelves. Continue reading to learn more about how automation improves food manufacturing.
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speedscale

Chaos Monkey Won't Find Your Bug

Aug 17, 2026 By Matt LeRay In Speedscale
We shipped a chaos feature that never caused any chaos. Our mock server has had a fault-injection effect for years with a straightforward job: withhold the response entirely and see whether the caller copes. Last week I audited it against the actual code path. It had never withheld anything. The handler returned early without writing a response. Go’s net/http then did what it is designed to do, which is synthesize a 200 OK and flush the recorded body.
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zymr

Top Challenges of Interoperability in Healthcare and How AI Is Helping Solve Them

Aug 17, 2026 By Yogesh Karachiwala In Zymr
Healthcare interoperability enables clinical and administrative systems to exchange usable patient information. However, connectivity alone does not ensure accurate interpretation or workflow compatibility. Many of the challenges with interoperability in healthcare have less to do with moving data and more to do with whether the receiving system understands what that data means. FHIR standardizes healthcare data exchange through structured resources and implementation frameworks.
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smartbear

SmartBear MCP for Zephyr: Connect your testing system of record to your AI tools

Aug 17, 2026 By Alisha Siddhartha In SmartBear
Your SmartBear Zephyr test data holds the answers you need before you ship: what’s covered, what passed, where the risk sits. That data has always lived one context switch away, behind the Jira UI. The SmartBear MCP Server changes that. It brings your Zephyr test data into any MCP-compatible AI client, so quality keeps pace with how fast your team builds. This guide covers where testing sits in the AI age, what MCP is, and how it unifies data visibility within your Zephyr workflow.
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oril

Where AI Delivers Real ROI in Brokerage and Listing Platforms

Aug 17, 2026 By ORIL Team In ORIL
Two AI features can cost the same to build and land on opposite sides of the P&L. Add a chatbot to a listing page, and you get a support-deflection number that plateaus in a quarter. Rework search ranking so a buyer who types “quiet street, near a school, room for an office” gets the right ten homes instead of 400 filtered results, and you move search-to-contact conversion, which sits at the top of every revenue metric downstream.
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perforce

Why Determinism & Realism Are So Critical in Enterprise Synthetic Data

Aug 17, 2026 By Perforce In Perforce
Generating synthetic data is one thing. Trusting it is another. As enterprises adopt synthetic data for development and testing, important questions quickly emerge: Can it be reproduced consistently? Does it accurately reflect real-world business scenarios? In fact, our survey of enterprise leaders found that “consistent, high-quality test data to reduce defects” was their priority in test data automation at 43%.
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