AIMS Head of Customer Success, Technical Evangelist and former BizTalk Premier Field Engineer Ricardo Torre presents on the present and future of AI-powered integration monitoring.

1. AI-powered performance
monitoring for integration:
Present and future

2. 2
• AIMS Monitoring using ML today
• Demo: Application instrumented
• What the future holds
Agenda

3. AIMS is the only intelligent performance monitoring and
analytics solution that’s deeply focused on integrations:
the beating heart of digital business.
Prevent downtime in critical business processes ● Reduce time wasted on
troubleshooting / monitoring ● Shift focus to building business value
Enable IT teams as
business drivers
Configure & troubleshoot
less. Innovate more.
Real-time insight &
predictive performance
management
Deep integration
intelligence automated by
ML and AI

4. 4
Deep integration intelligence – fully automated by machine learning
AIMS combines the latest machine learning
technology with deep insights into transaction
flow through on-premise, cloud and hybrid
integrations.
• Fully automated solution
• No manual configuration required
• Deep transaction tracing
• Scaleable to the world's largest integrations
• Near-zero performance impact

5. 5
Real-time insight and predictive performance management
Topographical map screenshot
AIMS generates a dynamic 360° map of your
entire integration environment and data and
detects and warns you about actual and potential
anomalies in real-time.
• Big data analytics powered by ML
• Real-time, dynamic mapping
• Predictive anomaly detection
• Map and monitor unknown areas of IT system

6. 6
Configure and troubleshoot less. Innovate more.
Anomaly overview screenshot
AIMS eliminates most of the time you spend on
manual configuration and troubleshooting false
alerts and makes sure you can take proactive
action early on the alerts that really matter.
• No manual configuration required
• Dynamic thresholds for cyclical business
• Proactively identify root cause of issues
• Fewer false-positive alerts

7. 7
Enable IT as business drivers
Dashboards screenshot
AIMS enables IT teams to impact business
outcomes by making it easier to communicate IT
system insights and performance data with non-
technical business stakeholders in a language they
can understand.
• Extract business intelligence from IT systems
• Real-time data on transaction flows
• Present complex data using dashboards
• Bridge gap between IT and business

8. 8
ServiceBus
Current Technology Agents
LogicApps
IIS
File Count Monitoring
HTTP Endpoint
On Premise / VM Azure Open Source

9. 10
Steps to implement the AIMS SDK
Agent Definition
1. Define Agent Metadata
2. Add SDK to Solution
3. Authenticate with API
4. Create System and get authentication token
5. Gather Nodes, Links, Stats and Events
6. Send with API

10. 11
Steps to implement the AIMS SDK
Agent Definition
Define your agent metadata in the UI or through
the API (using a JSON config file)
• Nodes Types
• Event Types
• Property Types
• Stat Types
• Status
• Stat Types
• Aggregation
• Unit
• Name
{
"id": "aims.logsrv",
"companyId": 22,
"name": "Logging Service",
"version": "v1.0",
"metadata": {
"eventTypes": [
{
"hasDuration": false,
"id": "fa04f393-8a04-4f9b-b0fe-c282bb4912f9",
"name": "aims.logsrv.message",
"displayName": "Log Messages"
}
],
"nodePropertyTypes": [
{
"id": "d074bf93-9b8f-45f4-b507-afaff126ec18",
"name": "aims.logsrv.type",
"displayName": "Type"
},

11. 12
Demo
Instrumenting an application with AIMS
• Using an docker hosted API
• Using the AIMS .NET SDK
• Instrumenting the application
• Ingest
• Metadata
• Metrics
• Events

13. What the future holds

14. Machine learning is among us!
New (old) algorithms
New people

15. What can we
improve?
And why?
We want to give you a even more scientific insight on your

16. AIMS will become better in classifying different
situations.

17. Message count example:
• Every Wednesday at 8 o’clock we get 100 messages through one node.
• Sometimes at the same hour we get 1000 messages through the same
node, but we know that this is a perfect normal behaviour, because
is linked to an activity that take place some Wednesdays, usually
at 8.
• These are two normal behaviour, one is more frequent than the
other, so it will contribute to our main baseline (the everyday
shopping that we do for dinner)
• Both a message count of 10000 and of 250 could be anomalous.

19. Other new features
• Prediction
• Correlations: variable x has 70%
probability to be correlated to
variable y

20. Thank
you