Incident Intelligence
Incidents detectedbefore the queue fills.
After agents deploy, GenticFlow learns what normal looks like across your environment. When endpoint signals drift, cluster, or spread, it creates an incident, investigates the affected fleet, and explains likely root cause.
Local resolver cache drift on Finance laptops.
Network AnomalyHealthy endpoints confirm the gateway and NetSuite are reachable. Run the DNS/cache remediation path on the three affected endpoints, then verify HTTP response.
How it works
From endpoint behavior to investigated incident.
The goal is not more alerts. The goal is one operational record that says what changed, where it is spreading, what probably caused it, and what should happen next.
Deploy agents
Agents observe endpoint health, services, processes, events, update state, network behavior, and remediation results.
Learn normal
Baselines form per endpoint, user, role, site, application, service, process, and time window.
Detect drift
Signals fire when behavior leaves the learned range or starts repeating across related endpoints.
Aggregate incidents
Related signals collapse into one incident when they share timing, site, app, service, endpoint group, or fault pattern.
Investigate blast radius
The AI engineer compares healthy and unhealthy endpoints, checks the affected fleet, and finds common factors.
Route the response
Known issues route into playbooks, custom response flows route into workflows, and ambiguous cases escalate with evidence.
Signal use cases
What the agent can detect.
Signals are endpoint-level observations from the deployed agent. They give the server enough evidence to decide whether a single machine is drifting or an issue is spreading.
Application Crash
Detect repeated Outlook, browser, line-of-business app, or agent crashes before users report the same failure.
Firewall Disabled
Flag endpoints whose firewall state moves outside expected policy so the team can investigate drift.
New User Account
Surface unexpected local account creation as a security-sensitive endpoint signal.
Security Event
Detect unusual authentication behavior and preserve the endpoint/user context for investigation.
Service Issue
Catch recurring service crashes or restart loops, such as spooler, VPN, update, or backup services.
Unusual Log Activity
Use Windows event logs to spot update failures, driver faults, service terminations, and app errors.
Unusual Network Activity
Detect DNS, gateway, connection, or reachability patterns that differ from the endpoint baseline.
Unusual Process Activity
Surface processes with unusual CPU, memory, session, or runtime behavior.
Unusual Resource Usage
Detect CPU, memory, disk, uptime, or capacity drift against the learned baseline.
Incident use cases
What the server can create from those signals.
The server aggregates related signals into incidents with affected endpoints, investigation state, likely root cause, and the recommended response path.
Widespread Application Instability
Create one incident when the same app starts failing across a site, client, department, or endpoint group.
Likely Bad Update
Identify when a patch, driver, app version, or update correlates with the affected endpoints.
Emerging Issue
Group early weak signals into an incident before the help desk sees a ticket wave.
Endpoint Health Critical
Create an incident when an endpoint or group crosses a critical health threshold.
Recurring Endpoint Issue
Recognize issues that keep returning after apparent resolution and preserve the history.
Correlated Incident
Aggregate different signal types when they share timing, location, endpoint group, app, or fingerprint.
Temporal Pattern
Detect issues tied to a time window, such as after-hours, startup, backups, or patch cycles.
Predicted Threshold Breach
Warn when the learned trend suggests a metric will cross a problem threshold soon.
Unusual Activity
Create an incident when endpoint behavior moves materially outside its learned normal range.
Baseline first
The agent learns what normal means here.
A busy workstation, a print server, a backup endpoint, and a finance laptop should not share the same thresholds. GenticFlow treats normal as local to the environment.
Incident output
A root-cause packet, not a pile of signals.
When a cluster becomes an incident, the AI engineer collects the details an on-call engineer would ask for first.
Response paths
Detection is connected to resolution.
Incidents should not stop at diagnosis. GenticFlow connects the incident record to the remediation surface that fits the risk and certainty of the case.
Known endpoint issue
Route into a resolution playbook when the incident maps to a supported remediation class.
Customer-specific response
Trigger an automation workflow when the fix needs client, site, app, or policy-specific steps.
Human investigation needed
Open the technician toolkit with the incident context, endpoint evidence, and root-cause hypothesis attached.
FAQ
Incident intelligence questions.
The key distinction: GenticFlow is not trying to become another alert wall. It turns endpoint signals into investigated incidents.
Incidents preserve the evidence chain: signal, aggregation rule, investigation steps, affected endpoints, approvals, actions, and verification.
How is Incident Intelligence different from normal monitoring?
Traditional monitoring produces alerts. GenticFlow learns endpoint behavior, groups related signals into incidents, investigates affected endpoints, and explains likely root cause with evidence and recommended response.
What does the agent baseline?
The agent can baseline endpoint health, services, processes, event patterns, update state, disk pressure, network behavior, application behavior, user-session patterns, and remediation outcomes.
What happens when an issue starts spreading overnight?
GenticFlow aggregates the related signals into an incident, checks every affected endpoint, compares them against healthy machines, identifies common factors, and updates the incident with scope, timeline, likely cause, and next action.
Can incidents trigger remediation?
Yes. Incidents can route into included resolution playbooks, custom automation workflows, or technician investigation depending on confidence, risk, approval policy, and verification requirements.
See what is spreading before users open tickets.
Give the AI engineer enough endpoint context to detect abnormal behavior, investigate it, and route the response before the queue fills.