From project management to DevSecOps, agentic AI accelerates every stage of the SDLC — so engineering organizations deliver faster, cut cost, and raise quality at the same time.
Three families of work, each with named, production-oriented use cases.
Redefine how software gets built by putting agents behind every SDLC stage:
Future-proof the estate by converting legacy applications into scalable, cloud-native architectures:
Turn scattered application data into actionable intelligence:
One super agent owns the outcome; specialized sub agents each own a step. This is the same anatomy behind every technology use case on the platform.
01
Discovery agents inventory the legacy codebase and build a knowledge base of its structure, dependencies, and behavior.
02
Documentation agents generate current-state architecture views the conversion will be validated against.
03
Conversion agents translate code and configuration in parallel waves, using code-aware retrieval over the source repositories.
04
Quality-engineering agents generate and run tests; validators score the output before anything is promoted.
05
Human gates hold the high-risk changes for engineer approval — then the workflow resumes exactly where it paused.
The orchestration schema supports strangler-fig migration natively — wave-based conversion with coexistence routing, so modernization proceeds without a big-bang cutover.
Speed, cost, and quality — tracked by the platform itself, not by a slide.
FinOps dashboards show per-agent and per-workflow cost against budget. TechOps shows throughput and reliability. AIOps scores output quality on every run — so the modernization program reports progress from live telemetry.
Active engagements at major financial institutions converting legacy platforms and languages to modern, cloud-native stacks.
Pilot programs automating test generation and migrating configuration across enterprise estates.
We'll show you how a conversion super agent takes it apart.
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