Operations | Monitoring | ITSM | DevOps | Cloud

We just added two new AI features to our app: natural-language translation for Error search and Insights queries. Honeybadger has two query languages: Error search speaks a simple token syntax in the spirit of Solr or a basic Elasticsearch query, while Insights runs on BadgerQL (BQL), our own language for digging into your event data, designed to feel familiar to CloudWatch Insights and Splunk users. Both are powerful, but sometimes you just want something that works without having to open up the docs.

How long would it take you to get one new line of code into the container running your payment service? In a lot of organizations, the answer runs to weeks, because the change has to clear a build owner, a test cycle, and a release window that nobody wants to open early. That timeline is why so much monitoring advice fails on contact. Most of it opens by telling you to add a library, rebuild the image, and push a new version. If you could do that this afternoon, you would have done it already.

If you run Starlette or FastAPI in production with an APM tool, you should keep an eye on PR. It adds native OpenTelemetry HTTP server spans directly into the framework. No external instrumentor, no monkeypatching. Just spans emitted from the router itself. At Scout Monitoring, we instrument Starlette and FastAPI through our Python agent. A change like this touches how every APM tool in the Python ecosystem works with these frameworks, ours included.

Every investigation, detection, dashboard, and AI-assisted workflow depends on one thing: data that teams can trust. But as environments grow more distributed, the data behind those experiences gets harder to manage. New applications, cloud services, security tools, infrastructure, and network devices constantly generate machine data, and each new source can introduce new formats, missing fields, inconsistent mappings, and pipeline changes that require expert attention.

Vibe coding made building software feel almost too accessible. You describe what you want, an AI assistant scaffolds it, and a few hours later, something is running. So, it was only a matter of time before developers started asking the obvious question: why should I pay for a monitoring tool when I can just build my own? In all fairness, the DIY instinct is a healthy one. But monitoring is one of the last corners you’d want to cut.

AWS Lambda durable functions let you build long-running, multi-step workflows for use cases such as payment processing, order fulfillment, and AI workflows with human approval. A single durable execution can pause for a wait or callback, retry failed work, and resume in a fresh Lambda invocation without losing its state. The strong resilience provided by durable executions, however, creates an observability challenge because each invocation produces its own telemetry data.

Teams often track operational work across spreadsheets, Slack threads, Jira tickets, and whatever system generated the original alert or signal. This fragmentation makes it difficult to maintain a consistent record of what needs attention, who or what is addressing the issue, and what has already happened. As AI agents take on more responsibility for investigations, triage, and code changes, the number of handoffs grows, making ownership, status, and history even harder to preserve.

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.