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Aisera's Agentic AI for ITSM

Overview

Aisera's Agentic AI for ITSM represents a breakthrough in autonomous IT service management, delivering intelligent problem-solving capabilities that go beyond traditional scripted automation. Our agentic architecture enables autonomous decision-making, adaptive responses, and continuous learning to transform reactive IT support into predictive, autonomous service delivery.

Key Capabilities

Autonomous Virtual Support Agents

The Aisera Autonomous Virtual Support Agent excels in adaptive, non-scripted incident response by retrieving data from multiple knowledge bases and ITOM systems. The VSA autonomously queries monitoring platforms, executes contextual scripts through Hyperflows, and delivers relevant knowledge without predefined rules. Event Studio enables proactive incident detection, while sophisticated decision-making adapts responses based on real-time data and dynamically selects optimal resolution paths.

Deep Research for Knowledge Generation

Aisera's Deep Research for Knowledge Generation feature autonomously searches external sources to compile comprehensive knowledge articles. The platform compares multiple sources, resolves conflicts through intelligent analysis, and synthesizes content into documentation. Beyond external research, Aisera automatically generates KB articles from resolved tickets and live agent transcripts, extracting resolution patterns. This multi-source approach ensures comprehensive, real-world grounded knowledge.

Integration Interface Negotiation

The Aisera Integration Interface Negotiation feature automatically discovers and negotiates APIs with external systems, eliminating manual configuration. The platform autonomously creates interface connections, generates contextual prompts for interactions, and automates actions without hand-coded workflows. Through MCP support in Hyperflows, Aisera dynamically adapts to system interfaces, understanding available functions and parameters for seamless integration with ITSM tools and applications.

CMDB Discrepancy Analysis and Resolution

Aisera's CMDB Discrepancy Analysis feature autonomously discovers configuration drift and missing CI relationships through monitoring data correlation. The agent analyzes AppDynamics and NewRelic flows to identify undocumented dependencies, validates against ServiceNow CMDB, and proposes corrections. With human validation, it automatically updates CMDB relationships and configurations while maintaining governance, ensuring accurate service impact analysis and improved incident response.

Automated Change Management

The Aisera Automated Change Management system employs agentic AI to assess change impact across services using real-time CMDB and environmental data. The agent autonomously schedules optimal windows, adapts to modifications, and executes changes with continuous monitoring. It detects unauthorized infrastructure changes, performs immediate rollbacks when needed, and maintains governance through automated risk assessment, stakeholder communication, and continuous learning for improved success.

Architecture Overview

Multi-Agent System

Aisera implements a sophisticated multi-agent architecture:

  • Universal Agent: Orchestrates between domain-specific agents

  • Domain Agents: Specialized agents for IT, infrastructure, and service management

  • Task Agents: Execute specific workflows through AI Workflows or Hyperflows

Decision-Making Framework

Agents employ multiple autonomous decision-making methods:

  1. Reinforcement Learning from Human Feedback (RLHF) - Incorporates SME and end-user feedback to optimize decisions

  2. Multi-Criteria Decision Analysis - Evaluates paths based on success probability, execution time, resource requirements

  3. Ensemble Decision Methods - Combines multiple reasoning approaches for consensus decisions

  4. Confidence Scoring with Decision Trees - Uses confidence intervals and branching logic

  5. Historical Pattern Matching - Leverages past interaction data for similar scenarios

  6. Real-time Context Evaluation - Considers system status, resources, and user profile

  7. Policy-based Filtering - Applies compliance requirements and business rules

  8. Continuous Learning Loops - Updates models based on outcome success rates

Demo Scenario: CRM Performance Issue Resolution

Scenario Overview

This comprehensive demo showcases how Aisera's Agentic AI handles a complete ITSM incident lifecycle, from detection through resolution and learning.

Phase 1: Autonomous Incident Detection

ITOM Trigger: AppDynamics sends alert to Aisera Event Studio: "CRM Application - Response Time Threshold Exceeded: Average response time 8.5 seconds (threshold: 2 seconds) - Priority: HIGH"

Event Studio Activation:

  • Event Studio receives ITOM alert and automatically triggers Autonomous VSA

  • VSA proactively reaches out to affected users via Teams: "I've detected performance issues with the CRM application. I'm investigating and will provide updates."

Phase 2: Multi-Source Data Correlation

The VSA autonomously retrieves data from:

  • AppDynamics: Detailed performance metrics and error rates

  • New Relic: Database monitoring showing connection timeouts

  • Network Monitoring: Bandwidth and latency analysis

  • ServiceNow CMDB: Recent deployment logs

  • Knowledge Base: Similar performance degradation patterns

Intelligent Analysis: The agent correlates ITOM data revealing database connection pool exhaustion coinciding with 9:00 AM deployment, identifying root cause through pattern matching with historical incidents.

Phase 3: Autonomous Resolution

Decision-Making Process:

  • Executes diagnostic Hyperflow to confirm database configuration drift

  • Applies temporary fix: increases connection pool from 50 to 150 connections

  • Creates incident ticket with full root cause analysis

  • Updates affected users with resolution timeline

Proactive Monitoring: Continuously monitors metrics for 30 minutes, confirms resolution, and automatically closes incident.

Phase 4: CMDB Discovery and Correction

During analysis, the VSA discovers:

  • Missing Relationship: CRM-APP-01 depends on REDIS-CACHE-03 (undocumented)

  • Configuration Drift: Redis max_connections shows different values in CMDB vs reality

Agentic CMDB Update:

  • Discovers new CI relationships from monitoring data

  • Requests human validation from Service Owner

  • Automatically updates ServiceNow CMDB with correct relationships and configurations

  • Implements preventive monitoring to maintain accuracy

Phase 5: Change Management Integration

Proactive Change Creation: VSA automatically generates change request for permanent configuration fix, schedules optimal maintenance window, and coordinates with stakeholders.

AI Agent Types

Incident Management Agents

1. Autonomous Incident Detection Agent

Title: "Intelligent Multi-Channel Incident Detection & Alert Agent"

This agentic incident management proactively detects incidents from diverse sources including operational monitoring systems, Slack/Teams chat logs, and user communications. It correlates signals across platforms, identifies patterns indicating potential issues, and automatically creates incident records with contextual information. The agent distinguishes between noise and legitimate incidents using intelligent filtering and escalates based on severity and business impact.

2. Impact and Root Cause Analysis Agent

Title: "Autonomous Impact Assessment & Root Cause Analysis Agent"

This agent autonomously analyzes incident impact across business services and infrastructure using real-time CMDB relationships and monitoring data. It performs intelligent root cause analysis by correlating symptoms across multiple systems, executing diagnostic workflows, and identifying failure patterns. The agent dynamically assesses business impact, updates incident priority based on affected services, and provides actionable resolution recommendations.

Service Request Management Agents

1. Intelligent Service Fulfillment Agent

Title: "Autonomous Service Request Processing & Fulfillment Agent"

This comprehensive agentic AI agent manages the complete service request lifecycle from intake to closure, intelligently routing and fulfilling requests through automated workflows and system integrations. The agent analyzes incoming requests using natural language processing to understand user intent, automatically categorizes requests, and determines optimal fulfillment paths based on request complexity, resource availability, and organizational policies.

2. Self-Service Optimization Agent

Title: "Adaptive Self-Service Enhancement & Knowledge Agent"

This intelligent agent focuses on maximizing self-service success rates by continuously analyzing user interaction patterns, identifying service request trends, and proactively improving self-service capabilities. The agent monitors user journeys across self-service portals, identifies points of friction or abandonment, and automatically generates or updates knowledge articles, service catalog descriptions, and workflow guidance.

Creating Agentic Hyperflows

Diagnostic Agent Hyperflow Example

Step 1: Access Hyperflow Studio

Navigate to Aisera Platform → AI Automation & Optimize Flow → Hyperflow Studio

Step 2: Create New Hyperflow

Name: "Multi-Platform Performance Diagnostic Agent"
Type: Diagnostic Agent
Domain: IT Operations

Step 3: Configure Agentic Instructions

Instructions: "You are an autonomous diagnostic agent with access to multiple monitoring platforms. 
When analyzing application issues:

1. Always check multiple data sources for corroborating evidence
2. Look for patterns across application, database, and infrastructure metrics  
3. Prioritize user-impacting metrics (response time, error rate, throughput)
4. Provide specific, actionable recommendations based on discovered anomalies
5. Escalate immediately if critical thresholds are exceeded"

Step 4: Set Guardrails

Guardrails:
- Always query both platforms for data validation
- Focus on user-impacting metrics first  
- Escalate if critical thresholds exceeded (>5s response time, >5% error rate)
- Provide specific next steps, not just data summaries
- Maintain conversation context across multiple diagnostic rounds

Step 5: Configure MCP Tools

The platform automatically discovers and configures available tools:

Auto-Discovered Functions:

  • newrelic_query(app_name, metric_type, duration)

  • appdynamics_analyze(application, component, timeframe)

  • cross_platform_correlation(issue_symptoms)

  • cmdb_relationship_query(ci_name)

  • generate_diagnostic_report(findings)

Step 6: Test and Deploy

Use the integrated Test and Debug feature to validate the Hyperflow with simulated scenarios before production deployment.

Audit and Governance

Tracking Agentic Actions

Aisera provides comprehensive agentic action tracking through:

  • Audit Trail: Logs capture all AI agent decisions with timestamps and reasoning

  • AI Lens: Complete stack trace visibility into agent conversations and decision-making

  • AI Workbench: Analysis of agent performance and unresolved conversations

These tools ensure full transparency, compliance, and accountability for all autonomous AI actions within ITSM workflows, enabling complete audit capabilities.

Decision Transparency

Every autonomous decision includes:

  • Reasoning chain documentation

  • Data sources consulted

  • Confidence scores

  • Alternative options considered

  • Human escalation triggers

Getting Started

Prerequisites

  • Aisera Platform access with ITSM module

  • Configured monitoring system integrations (AppDynamics, NewRelic, etc.)

  • ServiceNow CMDB integration

  • Appropriate user permissions for Hyperflow Studio

Implementation Steps

  1. Configure Data Sources: Set up monitoring system integrations

  2. Define Agent Personas: Create domain-specific agents for your environment

  3. Build Hyperflows: Use Hyperflow Studio to create agentic workflows

  4. Test Scenarios: Validate agent behavior with Test Suite

  5. Deploy Gradually: Start with low-risk scenarios and expand coverage

  6. Monitor Performance: Use AI Lens and Analytics for continuous optimization

Best Practices

  • Start with simple diagnostic scenarios before complex autonomous resolution

  • Maintain human oversight for critical business processes

  • Regularly review agent decisions through audit trails

  • Continuously refine guardrails based on operational experience

  • Leverage feedback loops to improve agent performance

Support and Resources

For additional assistance with Agentic AI for ITSM:

  • Documentation: Complete platform documentation in Aisera GitBook

  • Training: Agentic AI implementation workshops

  • Support: Technical support through standard channels

  • Community: Best practices sharing through Aisera Community

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