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Mature experimentation programs eventually have to report the cumulative impact of their shipped changes. The request might come as an ROI story for leadership, a revenue update for finance, or a gut check on the quarter’s progress. The tempting shortcut is to sum the observed lift from each winning experiment and report the total. That naive sum almost always overstates the truth because of a statistical artifact called the winner’s curse.

An agent that only speaks when spoken to is a tool you have to remember to use. Olly has run on a schedule for a while now, working a saved prompt hourly, daily, weekly, or monthly and writing its findings into a chat with its own run history. Those scheduled tasks are now wired into the Coralogix Notification Center, so Olly delivers that output itself, allowing Olly to reach out to Slack or email, out of the box.

How context engineering creates production-ready agentic AI What if the AI strategy you spent the past year building is already being measured by a completely different set of rules? I recently joined Amy Machado, senior research manager at IDC and Jim Malone, senior contributing editor at CIO Marketing Services, for a webinar where we explored how buyer expectations, architectural requirements, and evaluation criteria are shifting as enterprises move from search-driven experiences to agentic AI.

Most build-versus-buy debates assume you're choosing once, at the start, and living with it. Agentic engineering doesn't work that way. The decision shows up at every layer of the stack, and the teams getting it right aren't the ones who picked "build" or "buy" as a philosophy. They're the ones who know which layer is which.

The latest SIGNL4 features make it even easier to keep your stakeholders informed and communicate with them directly. At the same time, you can fine-tune your alerting to better fit your teams and workflows.