ERROR INVESTIGATION

Plain language in.
The right answer out.

Your engineers shouldn't memorize which monitoring tool owns which service. Argus knows. Ask in plain language. Get a synthesized answer across Datadog, Grafana, Elastic, and Jaeger. No dashboard hopping.

See Argus on one of your services → See the example
argus query
LIVE
> Billing errors, last 2h
routed to Datadog
147 events scanned
pattern detected
TIME
ERROR
COUNT
01:58
NullPointer
147
02:01
Timeout
89
02:14
NullPointer
203
02:23
Timeout
112
› same NullPointer signature as 04 NOV incident
Datadog Jaeger
FROM SIX TABS TO ONE QUESTION

Your engineer doesn't have a missing-data problem. They have a missing-time problem.

The data already exists. Today, finding it takes the engineer through tools nobody designed to talk to each other.

TODAY

Six tools. One question. Twenty minutes.

Datadog
Open the Billing service dashboard.
+0 min
Grafana
Cross-reference upstream service metrics.
+4 min
Elastic
Search logs for the error pattern.
+9 min
Jaeger
Pull traces for the affected requests.
+13 min
Jira
Check if anyone solved this before.
+18 min
Outcome: 20 minutes of context-gathering before the engineer has a hypothesis. Multiplied by every incident, every shift.
WITH ARGUS

One question. The right tool. The right answer.

What errors has the Billing service had in the last 2 hours?
→ Routes to Datadog for Billing service metrics
→ Pulls trace context from Jaeger
→ Cross-references Jira for prior incidents
Synthesized answer in 12 seconds. Pattern flagged. Sources cited.
Outcome: the engineer skips the scavenger hunt. Goes straight to the fix.
A REAL INVESTIGATION

Three questions. Three minutes. One root cause.

An engineer notices an error spike on the Billing service. They have a vague hunch but no time to chase it through six dashboards.

So they ask Argus. Then they ask Argus a follow-up. Then one more.

Each question routes to the right tool. Each answer comes back with sources cited. The engineer doesn't open a single dashboard. Three minutes later, they have the root cause.

This is what good tooling feels like. Get out of the way. Show the data. No ceremony.

argus · investigation session · 02:31
Q1 What errors has the Billing service had in the last 2 hours?
SOURCES: Datadog Jaeger
551 errors total. Two patterns: NullPointer (350) and Timeout (201). NullPointer signature matches the 04 NOV incident. Same root: missing null-check in the customer-lookup path.
Q2 What deployments hit Billing in the last 24 hours?
SOURCES: GitHub Jenkins
One deployment at 23:41 UTC. PR #4127 by @m.ristevska. Touches the customer-lookup module. Likely culprit.
Q3 Did the same fix work last time?
SOURCES: Jira INC-2847
Yes. 04 NOV incident resolved by adding null-check at line 142 of CustomerLookupService. Suggested action: rollback PR #4127 or apply the same fix.
WHAT THIS MEANS FOR YOUR TEAM

One layer. Three engineers, faster.

FOR ENGINEERS

Stop being a query interface for six tools.

Your engineers were hired to solve problems, not to remember which dashboard owns which service. Argus does the routing. They do the engineering.

FOR SRE LEADS

Diagnosis time, halved or better.

The 20 minutes of cross-tool correlation before any real work begins becomes 2 minutes of structured answers. Multiplied across every incident, every engineer.

FOR ENGINEERING LEADERSHIP

Tool sprawl stops being a tax.

Heterogeneous monitoring stacks are now a feature, not a liability. New tools, new acquisitions, new teams. Argus reads from all of them.

SEE IT ON ONE OF YOUR SERVICES

30 minutes. One real investigation. You will know.

Pick a service that's given your team trouble lately. We bring the demo. You'll see Argus query your stack and surface the answer in the format your engineers would receive.

Book a 30-minute walkthrough → Download the executive brief