Engineers still make the final judgment call. ARGUS collects the intelligence to make sure the call is informed, fast, and consistent, whether it's 2pm or 2am. It's the best way to improve your UpTime and reduce your MTTR.
Most teams lose 20 to 45 minutes per incident getting from the alert to a first hypothesis. Argus gets there in about 41 seconds. Move the sliders to see what the gap costs you, and what Argus gives back.
Default example: 40 incidents per month, 30 minutes of diagnosis time, 2 engineers, and a $90 hourly engineer cost equal 480 engineer-hours and $43,200 per year before remediation work begins.
Argus reads from your monitoring, deployment, logging, and topology tools simultaneously — correlates the signals, and delivers a single structured briefing your team can act on immediately.
Every investigation returns ranked hypotheses at confidence levels, backed by numbered evidence chains. Not a wall of logs – a structured argument your engineer can act on or challenge immediately.
Datadog and GitHub called simultaneously. Root cause identified. Notifications fired to Slack, Jira, Teams, and ServiceNow — all in a single investigation flow. Your team is briefed before they open a laptop.
The On-Call Briefing synthesises investigation history, recurring patterns, and deploy activity into one screen. Shift handover goes from a 30-minute verbal to a 5-minute review. Every time.
This isn’t a benchmark. These are the before-and-after numbers from real operations teams who deployed Argus into their incident workflows.
| Scenario | Today | With Argus |
|---|---|---|
| Time to first hypothesis | 20–45 min | ~2 min |
| NOC escalation decision | Gut feel or wait | Topology + impact in 90s |
| Deployment correlation | Manual GitHub / Jenkins | Automatic, every investigation |
| Recurring issue recognition | “I think this happened before...” | Structured history with root cause |
| Shift handover catch-up | 20–30 min | 5 min investigation log review |
| Cross-service impact | Multi-tool navigation | Blast radius on topology, instant |
Average MTTR improvement: 40–70% reduction in diagnosis time. Shift handover and NOC escalation accuracy gains alone justify the deployment.
This isn’t a benchmark. These are the before-and-after numbers from real operations teams who deployed Argus into their incident workflows.
Know immediately if an alert is a blip or a P0 incident — before you wake anyone up.
Multiple dashboards, gut feel, waiting for pattern recognition.
Service topology + blast radius + deployment correlation in 90 seconds.
One question instead of five dashboards. One answer instead of five context switches.
Datadog → GitHub → Jaeger → Slack → manual correlation. 40+ minutes.
All tools queried simultaneously. Ranked hypothesis with evidence in 41 seconds.
Direction in 90 seconds instead of 45 minutes of log archaeology.
Engineer wakes up. Opens 6 tabs. Pieces together context from scratch. 45 minutes lost.
Briefing is pre-generated. Root cause, blast radius, suggested action — ready before the call starts.
Full overnight context in 5 minutes, not 30. Every shift, every time.
20–30 minute verbal handover. Important context missed. Recurring issues not flagged.
5-minute investigation log review. Recurring patterns highlighted. Nothing falls through.
In one 30-day period: 40 investigations automated, 27 minutes of total AI processing time, equivalent to 20 hours of manual engineer time. Efficiency ratio: 44×.
And the cost of running those 40 investigations? $0.0105 per investigation. Full cost transparency, built into the platform — AI gateway spend tracked by virtual key, per agent, per day.
Argus sits on top of your existing tools. It reads, correlates, and surfaces intelligence – it never replaces the systems your team depends on.
Argus Panoptes was the all-seeing giant of Greek mythology — 100 eyes, never sleeping, never missing a thing. Appointed to watch over what mattered most, he saw everything simultaneously, at all times. We named our incident response agent after him because that’s exactly what your infrastructure deserves: something that watches your entire stack, all the time, and never looks away — so that when something breaks at 2am, the answer is already waiting for your engineer.
We start by mapping your incident workflow, service topology, and monitoring stack. Then we show you exactly where Argus cuts diagnosis time and improves escalation accuracy. No commitment required.
Practical answers about Argus, AI-powered incident response, root-cause analysis, MTTR reduction, tool integrations, and on-call engineering workflows.
Argus is IWConnect’s incident response agent. It helps engineering, DevOps, SRE, NOC, and managed services teams investigate incidents faster by correlating monitoring, deployment, logging, tracing, topology, and notification tools into one evidence-backed root-cause briefing.
Argus reduces mean time to resolution by collecting incident context automatically, checking multiple tools in parallel, ranking likely root-cause hypotheses, and giving on-call engineers a structured briefing with evidence instead of forcing them to investigate each system manually.
No. Argus works with the monitoring, logging, tracing, deployment, incident, and notification tools teams already use. It acts as an intelligence layer on top of those systems, helping teams understand what happened, where to look, and what evidence supports each possible root cause.
Argus is designed to support human decision-making, not remove it. It gathers evidence, prepares briefings, ranks likely causes, and can notify teams or connect to workflows, while engineers remain in control of production decisions and remediation actions.
Argus can connect to common monitoring, deployment, logging, tracing, incident, and notification tools such as Datadog, Grafana, Prometheus, Elastic, GitHub, Jenkins, GitLab, ArgoCD, Jaeger, Splunk, Loki, ServiceNow, Jira, Slack, and Microsoft Teams.
Argus reviews signals from monitoring, logs, deployments, traces, topology, and incident history, then correlates the evidence to produce ranked root-cause hypotheses. Each hypothesis should be supported by the signals that led to it, so engineers can evaluate the reasoning quickly.
Argus helps on-call engineers by turning alerts into structured incident briefings. Instead of jumping between dashboards, logs, deployments, and tickets, engineers receive a summarized view of likely causes, supporting evidence, related changes, recurring patterns, and recommended next steps.
Investigation cost depends on the connected systems, incident volume, AI processing requirements, and workflow design. The Argus page includes a calculator to estimate manual investigation cost and the recoverable time that AI-assisted incident response can help reduce.
The first step is to review your current incident response process, monitoring stack, deployment sources, logging and tracing tools, notification channels, incident history, and escalation workflows. From there, IWConnect can identify where Argus can reduce diagnosis time and support faster root-cause analysis.
✕
By signing up for the waiting list now, you'll secure your spot for early access and claim these valuable benefits.
