Operations | Monitoring | ITSM | DevOps | Cloud

Summary: A test hook connects a test or lint command to an event in your AI coding agent’s workflow. When the event fires, the agent runs the command automatically. If it fails, the agent’s action is blocked. You can wire your existing test commands into your agent’s lifecycle hooks to get deterministic local validation before code ever reaches CI. AI coding agents write code at a pace where stopping to manually run tests breaks your flow.

Your AI coding assistant already knows your codebase. Now it can know your production environment too. AppSignal's MCP server gives AI agents and AI code editors direct access to your monitoring data — errors, performance metrics, and more — so they can help you debug, investigate and resolve issues without switching context. And with our new public endpoint, getting started is simpler than ever.

For the first time in a decade, humans are the minority on the open web. In 2025, automated traffic officially crossed the Rubicon to account for 51% of all web activity, while generative AI-driven referrals to retail sites surged by a staggering 693% year-over-year. As we move through 2026, these are no longer just "bot" statistics to be handled by a WAF. They represent a fundamental shift in user behavior. The fastest-growing segment of your audience is now agentic.

The role of AI in observability is evolving rapidly, but the data from our fourth annual Observability Survey makes one thing abundantly clear: the potential is real, and so are the reservations. Practitioners overwhelmingly see value in using AI to help surface anomalies, forecast and spot trends, assist with root cause analysis, and get new users up to speed quicker.

Artificial intelligence recently took a major leap from “saying” to “doing.” Instead of simple back-and-forth chats, we’re now allowing automated AI processes to take action on our behalf—from responding to emails to building and deploying complete applications. This shift from “assistant” to “actor” can make applications more capable, but it also creates additional failure modes.

For years, our annual State of Digital Operations report has been the industry benchmark for understanding how organizations manage incidents, build resilience, and evolve their operational practices. Each year, we survey hundreds of business and operations leaders worldwide to capture the challenges, priorities, and emerging practices shaping digital operations.

This blog is based on the webinar, “AI panel: The next wave of AI technology”. You can watch the full recording by clicking here! The pace of AI innovation is reshaping research, business, and everyday life. However, as breakthroughs in Large Language Models (LLMs) and high-performance computing accelerate, they bring new technical challenges around scale, efficiency, and reliability.