Locate the root cause across a microservice mesh, draft the fix as a pull request, and hold the rollback ready.
Checkout p99 latency went from 240ms to 4.1s eleven minutes ago. What happened?
These are exchanges the team published, replayed here. Nothing on this page calls a language model, and nothing claims to.
A prompt library, a node-by-node board, a financial model you can argue with, and the API as it is actually documented.
Each agent below is configured for DevOps and cloud-native operations work, and carries its own measured throughput, accuracy and latency.
Correlates the alert with the deploy, the trace and the log line, proposes the rollback, and waits for a human to press it.
Reviews the diff for the bug rather than the style, and tells you which advisory in the dependency tree actually reaches your code.
Every template is a chain of nodes with a trigger at one end and something the business actually needed at the other.
A page fires; the change history, traces and logs are correlated into one hypothesis with its evidence, and the rollback is prepared and left for a human to press.
Correlated triage with stated confidence, remediation prepared but never executed unattended, and advisories filtered to what your code can reach.
Problem
The first twenty minutes of every incident go on working out what changed.
After
The correlation is waiting in the channel when the responder opens their laptop.
Problem
A scanner reporting 47 vulnerabilities is a scanner nobody reads.
After
Reachability analysis leaves the three that are actually exploitable from your call graph.
Problem
An agent that fixes production on its own is a new outage waiting to happen.
After
It prepares the rollback and stops. A person presses it, every time.
A hypothesis comes with what supports it and what argues against it. Low confidence is reported as low confidence.
Advisories are triaged by whether your code can reach the affected path, with the caveat stated plainly.
Execution is off by default and the setting is auditable. Automation that acts alone is not something this ships with.
The part I did not expect was the dependency triage. We went from a backlog nobody touched to a list of three that we cleared in an afternoon.
A worked example, not a promise. Change the inputs on the full model and the arithmetic changes with them.
repetitive hours a year across 40 people at 8 hours each per week
hours automated at a 62% automation rate
labour value released a year, at $40 an hour fully loaded
Forty-six working weeks a year, before licence cost. Released hours are hours people spend on something else — whether that becomes cash depends on what you redeploy them to, and this figure does not assume you will.
One REST surface, documented request and response for each route, and a token you can revoke.
Routes
{
"data": [
{
"id": "agt_8fk2",
"name": "DataPulse",
"model": "nexus-dense-70b",
"status": "ready",
"quota": { "used": 4120311, "limit": 120000000 }
}
],
"has_more": false
}
Every tier states its own ceilings. Where a limit is not listed, there is not one.
For a team automating its first few processes.
Billed monthly, cancel at any time
For a department running automation as part of how it works.
Billed monthly, cancel at any time
For an organisation with regulators, auditors and a security review.
Billed monthly, cancel at any time
For deployments that cannot touch a public network at all.
Scoped to your deployment
For a team automating its first few processes.
20% below the monthly rate of $299
For a department running automation as part of how it works.
20% below the monthly rate of $1,290
For an organisation with regulators, auditors and a security review.
20% below the monthly rate of $4,900
For deployments that cannot touch a public network at all.
Scoped to your deployment
The dependency triage was the surprise. A backlog nobody touched became a list of three, cleared in an afternoon.
Mean time to restore down 80%
Verified customer, 13 months on the Enterprise plan.