Cloud and platform engineering that carries agentic AI at scale

We modernize cloud and platform so agentic AI systems run at enterprise scale, resilient and self-healing as they carry what comes next. The foundation is engineered for AI workloads across AWS, Azure and Google Cloud, sized for where autonomy runs well, with human control kept over the rest.

60%

L2 Effort reduction

90%

Faster MTTR

99.9%

Reliability target

30 to 50%

Infrastructure cost savings

SRE teams

Platform reliability engineering

AIOps teams

Production operations

Foundation for the agentic enterprise

AI-native platform foundations

Architecture shaped for high-throughput inference and agent traffic
Capacity planned by prediction, with idle spend engineered out
Slot utilization raised from under half to close to full on large estates
Cost and performance tuned together from day one

Cloud modernization and migration

Dependency and workload analysis automated before a wave begins
Repeatable migration patterns applied across the estate
Migration run far faster on automated workstreams
Engineering judgment reserved for the decisions that carry risk

Container orchestration and self-healing operations

Container orchestration with automated failover and recovery
Scaling that follows demand without manual intervention
Known failure modes resolved before a human is paged
Recovery measured in minutes on production incidents

Zero-trust security architecture

Identity-based access across services and data
Security controls written into the platform architecture
Real-time anomaly detection across distributed microservices
Threats classified and contained live, including DDoS interception

Compute economics and spend control

Spend tracked as a first-class operational signal
Over-provisioning and under-provisioning removed by predictive allocation
Workloads scheduled across large core and storage estates
Cost held steady while volume climbs

AI-native platform foundations

Architecture shaped for high-throughput inference and agent traffic
Capacity planned by prediction, with idle spend engineered out
Slot utilization raised from under half to close to full on large estates
Cost and performance tuned together from day one

Cloud modernization and migration

Dependency and workload analysis automated before a wave begins
Repeatable migration patterns applied across the estate
Migration run far faster on automated workstreams
Engineering judgment reserved for the decisions that carry risk

Container orchestration and self-healing operations

Container orchestration with automated failover and recovery
Scaling that follows demand without manual intervention
Known failure modes resolved before a human is paged
Recovery measured in minutes on production incidents

Zero-trust security architecture

Identity-based access across services and data
Security controls written into the platform architecture
Real-time anomaly detection across distributed microservices
Threats classified and contained live, including DDoS interception

Compute economics and spend control

Spend tracked as a first-class operational signal
Over-provisioning and under-provisioning removed by predictive allocation
Workloads scheduled across large core and storage estates
Cost held steady while volume climbs

The Amp'd Way

Read the estate

Dependencies, workload patterns and spend are analyzed before any target architecture is drawn.

Design for AI workloads

Capacity is planned by prediction, and security and recovery are placed in the architecture from the start.

Move in waves

Automation carries the repeatable work, and each wave lands with its own rollback and acceptance criteria.

Run it self-healing

Known failure modes resolve automatically, anomalies are contained live, and cost is tuned in runtime.

Client success in production, at scale

See how Agivant engineers real business value for the autonomous enterprise, across several industries

See more client stories

Ecommerce and retail

Multi-brand commerce migration

Cloud & Platform EngineeringAgentic AI & AgentOpsAI & ML EngineeringData Engineering & Advanced Analytics

Deep Dive

Zero revenue-loss downtime across a 10+ brand migration before a platform shutdown

A platform shutdown put a fixed deadline on ten-plus storefronts. Agivant ran the migration with agents handling data movement, validation, SEO mapping and regression testing, then rebuilt the storefronts headless on the new platform.

70% downManual migration effort across ten-plus brands

Technology and internet

ServiceNow launch orchestration

Cloud & Platform EngineeringData Engineering & Advanced Analytics

Deep Dive

50%+ less idle time in product launches, orchestrated on ServiceNow

Launch teams coordinated across systems with no shared view, so work sat idle between handoffs. Agivant orchestrated new product introduction on ServiceNow, with a real-time task hierarchy running across every connected system.

50%+Less idle time in the launch workflow

Semiconductor and EDA

Million-core compute utilization

Cloud & Platform EngineeringAI & ML Engineering

Deep Dive

Slot utilization up to 90% across a million-core compute farm

Slot utilization on a massive compute farm ran from under 50% to 75%, with downtime found after the fact. Agivant raised utilization to as much as 90% and cut downtime 20% through proactive issue detection, across eight million jobs a day.

Up to 90%Slot utilization, from a range of under 50% to 75%

Semiconductor and EDA

Agentic server image builds

Cloud & Platform EngineeringAgentic AI & AgentOps

Deep Dive

Immutable, security-scanned server images from one plain-English request

Platform teams shipped server images across clouds, teams and deployment zones by hand. Agivant built an agentic workflow where one plain-English request produces an immutable, security-scanned image, with two human approval gates on every build.

Zero driftImmutable, idempotent builds every time

Ecommerce and retail

Zero revenue-loss downtime across a 10+ brand migration before a platform shutdown

A platform shutdown put a fixed deadline on ten-plus storefronts. Agivant ran the migration with agents handling data movement, validation, SEO mapping and regression testing, then rebuilt the storefronts headless on the new platform.

70% downManual migration effort across ten-plus brands

Technology and internet

50%+ less idle time in product launches, orchestrated on ServiceNow

Launch teams coordinated across systems with no shared view, so work sat idle between handoffs. Agivant orchestrated new product introduction on ServiceNow, with a real-time task hierarchy running across every connected system.

50%+Less idle time in the launch workflow

Semiconductor and EDA

Slot utilization up to 90% across a million-core compute farm

Slot utilization on a massive compute farm ran from under 50% to 75%, with downtime found after the fact. Agivant raised utilization to as much as 90% and cut downtime 20% through proactive issue detection, across eight million jobs a day.

Up to 90%Slot utilization, from a range of under 50% to 75%

Semiconductor and EDA

Immutable, security-scanned server images from one plain-English request

Platform teams shipped server images across clouds, teams and deployment zones by hand. Agivant built an agentic workflow where one plain-English request produces an immutable, security-scanned image, with two human approval gates on every build.

Zero driftImmutable, idempotent builds every time