Systems | Development | Analytics | API | Testing

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.

Shadow AI detection is the practice of finding and governing unsanctioned AI tools, models, and API integrations that employees deploy without security approval. It has become urgent because these tools route live enterprise data to external models in real time, and traditional security stacks cannot see them. The 2026 Cordyceps disclosure, which exposed identical AI-generated vulnerabilities across 300+ GitHub repositories, showed how fast ungoverned AI can turn into a supply-chain crisis.

For organizations developing regulated products, risk management and testing are closely connected. Every identified risk must be assessed, mitigated, verified, and documented to support compliance requirements and release decisions. Yet these activities often happen in separate systems. Risk assessments may live in dedicated risk management tools or spreadsheets, and testing is usually managed elsewhere.

Monthly active user rates stuck at 23%. A Slack message about another client who can't find the data they need. A support ticket your team has started to joke about: “Can you export this to Excel?” Once upon a time, embedding dashboards inside your product was a differentiator. Today, this is what the feedback looks like when your analytics stop working. AI agents can now respond to complex questions with meaningful insights in seconds.

"Agentic" is the word of the year in software marketing. Every product that sends an automated email is agentic now. Every tool with a chatbot interface is powered by AI agents. The term has been stretched to the point where it tells you almost nothing about what a product actually does.

Lending and credit fintechs sit at the intersection of two hard problems: moving sensitive financial data fast enough to make timely credit decisions, and keeping that data secure enough to satisfy regulators, auditors, and enterprise security teams. The platforms that work for this use case share three traits. They replicate data with latency low enough to feed risk scoring models.

‍Build Cache can meaningfully shorten CI feedback loops, but only if it’s connected to workflows where it’ll actually make a difference. So how do you figure out which workflows will benefit? That’s the part that’s been easy to get wrong — until now. Quick Connect is a new feature that takes the guesswork out of estimating time savings: it looks at your last 30 days of build data and surfaces the workflows that will benefit themost from caching.

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.

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.