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Stop Firefighting. Start Engineering Resilience.
High AMS cost and limited operational scalability are engineering problems, not support problems. When L1–L3 operations consume engineering bandwidth and reactive incidents drive cost, the fix isn’t more headcount, it’s autonomous operations. Xoriant replaces traditional AMS with AI-led operations that resolve, predict, and prevent with human engineers governing outcomes, not responding to alerts.
Making Operations a Competitive Advantage
The true cost of reactive operations rarely appears on a dashboard. It lives in the engineering hours consumed by L1 tickets, the delivery velocity lost to unplanned incidents, and the cumulative reliability erosion that compounds with every release. Xoriant replaces that model with Autonomous Operations: AI-led L1–L3 resolution, proactive AI SRE, and agent governance frameworks that shift teams from reactive firefighting to continuous, measurable resilience.
Guided by Human Ingenuity and AI, our Autonomous Operations practice combines deep AMS expertise with AI-native operational models continuously monitoring, predicting, and resolving issues at machine speed, with human engineers governing outcomes. The result is not just fewer incidents. It is an operational model that gets more capable, more precise, and more cost-efficient with every cycle.
Overarching philosophy of HI/AI
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Capabilities That Build New and Modernise What Already Exists
Autonomous AMS
AI SRE
Agent Operations
Intelligent Remediation and Self-Service
Observability and Predictive Maintenance
Hyper Automation in Build and Release
Autonomous AMS
Autonomous AMS
Replace traditional application managed services with AI-led L1–L3 operations. AI agents resolve known issue patterns autonomously, with human engineers maintaining oversight at defined escalation gates.
AI SRE
AI SRE
Proactive site reliability engineering powered by AI. Telemetry monitoring, predictive failure detection, and automated remediation prevent issues before they reach production or impact users.
Agent Operations
Agent Operations
Build the governance framework for running AI agents in production. Covers agent monitoring, cost optimisation, performance benchmarking, and lifecycle management across multi-agent environments.
Intelligent Remediation and Self-Service
Intelligent Remediation and Self-Service
AI agents resolve L1 issues from historical incident patterns. Log intelligence detects anomalies early and automated code review improves patch quality before deployment.
Observability and Predictive Maintenance
Observability and Predictive Maintenance
AI assistants monitor system health and surface risks early. Autonomous bots resolve issues end to end while telemetry data surfaces improvement opportunities across the platform.
Hyper Automation in Build and Release
Hyper Automation in Build and Release
AI accelerates patch creation with built-in quality checks. Autonomous testing covers every release and release documentation is auto-generated to keep knowledge bases current.
Case Study
Fortifying Application Security for a B2B Data Provider Using the SSDLC Approach
Robust app security results in secured, compliant and risk-free apps.
Case Study
Fortifying the Banking Frontlines with Improved Network and Security
Automated device hardening from weeks to minutes without errors.
Case Study
Faster Big Data Testing and Migration with Metadata-driven Framework
60% Improved Delivery Efficiency Using Test Automation During Migration From SQL Server to Snowflake.
Accelerators
Built to Accelerate What Matters Most
Differentiators
Engineering at the Xoriant Scale
Human-on-Loop at Scale
AI handles routine L1–L3 operations autonomously. Engineers govern outcomes, handle edge cases, and focus on reliability improvements, not alert triage or manual ticket resolution.
Predictive Before Reactive
AI SRE models detect and resolve failure patterns before they impact users. Xoriant’s operations shift from reactive MTTR reduction to proactive prevention at every stage.
Agent Operations Governance
As AI agents become operational infrastructure, Xoriant provides the governance layer monitoring performance, optimising cost, and managing agent lifecycles in production environments.
Aligned Commercial Models
Outcome-based operational models align Xoriant’s incentives to uptime, cost efficiency, and resolution rates not effort hours or ticket volume that grows with incident frequency.
Keeping You Updated
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FAQ
Autonomous Operations
What is Autonomous Operations and how is it different from traditional AMS?
Autonomous Operations replaces traditional reactive AMS with AI-led L1–L3 resolution, proactive AI SRE, and agent governance — reducing operational cost, improving scalability, and shifting engineering teams from alert response to outcome governance.
How does AI-led L1–L3 operations work in practice?
AI agents classify incoming incidents, match them against historical resolution patterns, and execute remediation autonomously for known issue types. Human engineers operate in an oversight role, reviewing outcomes and handling incidents that require engineering judgment.
What is AI SRE and how does it improve platform reliability?
AI SRE applies machine learning to telemetry, log, and performance data to detect failure signatures before they manifest as incidents. Automated remediation acts on those signals in real time, preventing outages and reducing MTTR across the platform estate.
What does Agent Operations governance include?
Agent Operations governance covers monitoring, performance benchmarking, cost optimisation, and lifecycle management of AI agents running in production ensuring agents perform as designed, stay within cost parameters, and are updated as requirements evolve.
How does Autonomous Operations reduce AMS cost?
By automating L1–L3 resolution and shifting to predictive operations, Xoriant reduces the headcount required to maintain operational SLAs lowering cost per ticket, reducing incident volume, and compressing resolution time without compromising coverage.
How does Xoriant handle the transition from traditional support to Autonomous Operations?
Xoriant manages the transition in structured phases conducting discovery and knowledge capture, deploying AI monitoring and classification layers, automating high-volume resolution patterns, and progressively expanding autonomous coverage with full engineering governance.
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