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.
The questions engineering, operations, and security teams ask most before evaluating ARGUS.
ARGUS is an AI-powered SRE and incident investigation platform designed to help engineering and operations teams investigate incidents faster and reduce manual troubleshooting effort.
ARGUS analyzes operational signals from existing monitoring, observability, cloud, DevOps, and ITSM tools and correlates them to build an understanding of what happened, what may have caused the incident, and what actions engineers should consider next.
ARGUS is designed to augment SRE and operations teams rather than replace them.
During a production incident, engineers often need to manually investigate multiple systems before they can understand the root cause. ARGUS reduces this manual effort by bringing relevant information together and using AI-driven reasoning to:
The objective is to reduce investigation time and help teams reach a resolution faster.
No. ARGUS is designed to work on top of and alongside existing monitoring and observability investments.
Traditional monitoring and observability platforms collect and visualize telemetry and generate alerts. ARGUS uses those signals as input for AI-powered investigation and reasoning.
Monitoring tells you that something is wrong. ARGUS helps investigate why it is wrong and what to investigate next.
ARGUS follows a structured investigation approach. It can:
The underlying approach uses structured, multi-step AI reasoning rather than relying on a single AI prompt.
ARGUS is designed to integrate with the tools that already form part of your operational ecosystem. Examples include:
The exact integrations available depend on the target deployment and customer environment.
ARGUS is designed primarily as an advisory and decision-support platform. It can recommend remediation actions, but production changes remain subject to customer-defined approval and governance processes.
This human-in-the-loop approach is particularly important for critical production environments.
ARGUS is designed with enterprise security principles including:
The current implementation uses HTTPS/TLS for communication, encrypted database storage, and controlled secrets management. The exact security architecture depends on the selected deployment model.
No. ARGUS is designed to augment engineers. It automates repetitive investigation activities so engineers can spend more time on:
The goal is to increase what existing teams can handle, not to reduce headcount.
These platforms address different parts of the operational lifecycle.
ARGUS works alongside these platforms rather than requiring them to be replaced.
We recommend starting with a focused use case rather than connecting the entire environment immediately. A typical approach is:
Use-case selection → Integration → Pilot → Validation against real incidents → Measure results → Production rollout
This allows you to validate the value of ARGUS against real operational scenarios before expanding the scope.
Browse the full FAQ covering integrations, deployment models, security, GDPR, AI governance, and implementation.
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