Operations | Monitoring | ITSM | DevOps | Cloud

AI Agent Security: Applying the OWASP Top 10 Across the Software Development Lifecycle

Learn how to secure AI agents across development, testing, deployment, and runtime using the OWASP Top 10 for Agentic Applications. A single poisoned web page can be enough to hijack an AI agent. As organizations move from AI assistants that generate responses to autonomous agents that take action, the security risks are changing. An agent can access sensitive data, execute code, modify configurations, and interact with external systems.

4 Signs Your IT Operations Are Ready for AI Agents

Is your IT operations model keeping up with growing complexity? From rising incident volumes to costly outsourcing and unnecessary escalations, here are 4 signs it may be time to rethink how your IT operations run. Discover why enterprises are turning to AI agents to reduce manual work and scale operations.

Property-based testing: How to find bugs your unit tests miss

Property-based testing is a software testing technique that checks whether a program satisfies general rules, called properties, across many automatically generated inputs. Instead of writing tests for individual examples, developers define how their code should behave and let a testing framework search for inputs that break those rules.

Introducing CircleCI Functions: Reusable CI steps built for coding agents

CircleCI Functions are now available in beta, giving developers and coding agents a faster, more reliable way to set up CI pipelines using reusable, tested steps. Functions are the next evolution of CircleCI orbs. They make common CI tasks, like installing programming languages, configuring AWS credentials, and setting up Docker, available as ready-to-use programs that agents can discover, configure, and validate before running a pipeline.

The Evaluation Factory: How We Make Agents Measurably Better, Run After Run

Ask a team running an agent in production whether last week’s change made it better. Most can’t say. They swapped a model or rewrote a skill, eyeballed a few runs, and moved on. That works for a demo. It doesn’t work for an agent that touches production, where customers want two things that pull against each other: precision where a mistake is expensive, and low cost where the work is routine. A root cause someone acts on has to be right.