Checkly runs synthetic monitoring checks against APIs, websites, and user workflows, and connecting it through Neotask lets an agent set up and review those checks in plain conversation. Ask the agent to add a check for a specific endpoint or user flow, and it configures the monitor through Checkly rather than you clicking through the setup screens one at a time. Once checks are running, you can ask for the current status, pull up a dashboard summary, or review traces from a specific run. The more distinctive part of this connection is what happens when something fails: the agent can pull in AI-assisted root cause analysis for a failed check or an ongoing incident, giving a starting explanation for what broke instead of leaving you to read raw logs and traces on your own at an inconvenient hour. That combination of setup, review, and failure analysis in one place is what makes the connection worth using day to day, rather than treating monitoring as a separate tool you only open when something has already gone wrong.
| Check creation | Sets up a new synthetic monitoring check for an API, site, or workflow |
| Status page review | Pulls up the current state of a status page tied to monitored services |
| Dashboard summary | Reports a summary of check results and dashboard metrics on request |
| Trace inspection | Retrieves trace data for a specific check run |
| Root cause analysis | Runs AI-assisted analysis on a failed check or incident to suggest a likely cause |
| Historical check summary | Reports how a specific check has performed over a recent stretch of time |
After a developer ships a new API endpoint, they ask their Neotask agent to set up a synthetic check that hits it every few minutes. The agent creates the check in Checkly, confirms it's running, and the developer moves on without opening the Checkly dashboard to configure it manually or remembering to circle back to it later that day. A week later, the developer asks the agent for a quick summary of how that check has performed since it went live, and gets a short answer instead of pulling up the dashboard to look.
An on-call engineer gets paged when a checkout flow check starts failing and asks the agent what happened. The agent pulls the trace for the failed run along with Checkly's root cause analysis and reports a likely cause, giving the engineer a starting point before they even open a laptop to dig through logs at that hour. With a probable cause already in hand, the engineer can go straight to confirming and fixing the issue instead of spending the first several minutes just figuring out where to look.
Both. It can set up new checks for APIs, websites, or user workflows and also review status pages, dashboards, and traces for checks already running, so setup and ongoing review use the same connection.
The agent can bring in AI-assisted root cause analysis for the failure, giving an explanation of what likely went wrong rather than just reporting that the check failed at some point, which is more useful when responding under time pressure.
It gives you a faster starting point by pulling trace data into the conversation, though you can still dig into the full trace if the summary isn't detailed enough for your purposes.
Yes, you can ask for a general status or dashboard summary and get a condensed answer rather than the raw underlying data, useful for a quick check between other tasks.
Yes, the root cause analysis feature is aimed at exactly that: giving a quicker read on an active incident or failed check as it happens, before someone has manually pulled up logs or traces themselves.
No, the agent can create the initial check for you, so this works whether you're starting fresh or reviewing checks that already exist in your account, without a separate onboarding step.