Systems | Development | Analytics | API | Testing

Enterprise customer support has reached an inflection point. The AI customer service market now exceeds $15 billion, and Gartner predicts agentic AI will autonomously resolve 80% of common customer service issues by 2029. Yet many organizations still struggle with platforms that deflect rather than resolve, require months of implementation, or lack the compliance depth needed for regulated industries. The difference between success and failure often comes down to choosing the right AI agent platform.

If you've spent any time researching accounts payable automation, you've probably run into the term RPA — robotic process automation. It's one of the oldest and most widely deployed forms of business automation, and it still plays a real role in AP departments today. But it's also frequently confused with newer, AI-powered approaches to invoice processing. This guide breaks down what RPA actually does in accounts payable, where it holds up, and where it runs out of road.

You're preparing for a release readiness meeting. The Test Plan contains hundreds of Tests. Development continued until late yesterday, several Defects were resolved overnight, and only a few hours remain before stakeholders need an update. There is enough time to execute part of the Test suite, but not all of it. The question isn't whether testing should continue. It's which Tests should be executed first. Every release forces QA teams to make prioritization decisions.

The journey to AgentSpot didn't start with a traditional product roadmap or a speculative “what if” from our R&D labs. Instead, it was born out of a growing friction within our own walls and became a "frontier R&D project" fueled by engineers exploring the internal potential of generative AI. When we first launched SpotGPT, our internal genAI application (similar to ChatGPT, but trained on internal resources) we saw immediate and massive adoption.

AI agents make thousands of decisions per day, at volumes no human-centered governance model can realistically supervise. According to IBM's 2026 Tech Leader Study of 2,000 CIOs/CTOs in 33 geographies across 19 industries: The study concludes that organizations face a trap: prioritize speed, and governance falls behind; or prioritize safety, and deployment stalls, weakening the organization’s competitive position.

If you use the Internet, you have surely come across the term Markdown. Markdown is a lightweight markup language that makes it very easy to write formatted content. It was created by John Gruber and Aaron Swartz in 2004. It uses very easy-to-remember syntax and is therefore used by many bloggers and content writers around the world. Even this blog that you are reading is written and formatted using Markdown.

Getting an answer from your data has never been faster. Turning that answer into something you can share still takes hours. Genie, our AI Analyst, made it possible for anyone to answer questions about performance. Ask “Why did conversions drop last month?” or “Which marketing channels drove the most pipeline?” and you’ll get a clear answer in seconds, with the charts to back it up. But some answers are worth more than a reply in a chat.

TL;DR: Fiserv platforms generate fixed-width.rpt files where every field sits at an exact character position. Astera ReportMiner maps those positions through a visual template editor, extracts the data, validates it against your business rules, and writes it directly to Excel, CSV, or 200+ other destinations. One template handles every future instance of the same report type, and the full pipeline runs unattended on a schedule.

Mainframe reports are one of the oldest and most persistent data extraction challenges in enterprise IT. They are generated by COBOL programs, printed by JES2/JES3 spoolers, and exported as fixed-width text files from IBM i Series, z/OS, AS/400, and similar systems. They power critical operations in banking, insurance, government, healthcare, and manufacturing. They also look nothing like the documents that modern AI extraction tools are designed for.

Flaky tests erode trust in the pipeline one rerun at a time. Learn why tests flake, how to tell a flaky test from a real regression, and how to fix the root cause instead of just hitting rerun.