Agent-Based SDLC: Solving AI Integration Challenges in Industrial Software Development
The saying “English is the new programming language” is becoming reality in enterprise software development. Thanks to natural language processing and AI copilots, traditional coding is shifting to idea-driven and intent-driven workflows. Yet, in this transition, integration has become a major hurdle.
Historically, enterprises managed software platforms with upgrades and complementary tools that advanced performance. But AI-powered tools like copilots and low-code platforms often fail to deliver value because organizations focus on speed without rethinking processes, addressing blockers, upskilling teams, or capturing meaningful telemetry. This leads to disappointing ROI and unmet expectations.
Rather than layering more software on top, Hitachi’s GlobalLogic took a different route—rethinking software development lifecycle (SDLC) orchestration itself. This shift aims to connect AI tools seamlessly and deliver measurable productivity gains, especially in industrial environments.
A New Model for Orchestration
Suhail Khaki, CTO of SDLC at GlobalLogic, compares current orchestration methods to driving a Tesla in manual mode with occasional autopilot assistance. In this analogy, humans manage every handoff between development stages, which causes delays that accumulate and slow down delivery.
By contrast, Tesla’s Full Self-Driving (FSD) mode handles the journey autonomously, alerting drivers only when intervention is needed. This is the vision for AI-driven SDLC: AI orchestrates end-to-end development, involving humans only when necessary. This approach reduces latency and streamlines workflows.
Boosting Velocity with AI-Orchestration
VelocityAI is the platform built around this vision. It uses intelligent agents to manage flows between requirements, development, testing, and deployment. Instead of relying on rigid stage gates, it uses intent-based routing and semantic prioritization to decide what tasks should happen and when, based on user goals.
For example, when you ask VelocityAI to build requirements, it automatically gathers relevant system, domain, and project data to create the backlog. It then orchestrates collaboration between product owners, analysts, quality engineers, architects, and developers to refine the backlog into a ready state.
Central to VelocityAI is a context-aware knowledge engine designed for industries like healthcare, automotive, and telecom. Context matters: without it, even the smartest AI can’t solve complex problems effectively. Instead of a blank canvas, developers get rich, relevant context—like automated proofreading highlighting errors rather than checking every word manually.
AI Integration in Industrial Environments
AI tool adoption faces major challenges: poor integration, shadow AI usage, and weak risk controls. A 2023 report by MIT Sloan Management Review and Boston Consulting Group found that over 55% of AI-related failures stem from third-party AI tools. In industrial sectors such as energy, transportation, and manufacturing, these risks are even more critical due to mission-critical operations.
VelocityAI targets these environments by ensuring software interfaces correctly with physical machinery and sensors while enforcing compliance automatically. When developers write natural language requirements, the platform applies relevant regulations—such as HIPAA or automotive safety standards—without manual intervention. This reduces review effort dramatically; what once took five experts can now be validated by one domain specialist.
Driving Faster Development and Lower Latency
GlobalLogic recently modernized a legacy promotions system for a fleet rental client using VelocityAI components. The old system was built on outdated J2EE and AS400 databases, requiring code changes even for simple promotions. The new system went live in just six months, a pace not achievable with traditional methods.
Beyond promotions, enhancements included repair systems that visually identify vehicle models and guide technicians step-by-step. Video analysis tools autonomously track fleet vehicles and assess damage without extra hardware.
VelocityAI is now expanding into telecom, automotive, and device testing. More than speed, this AI-infused SDLC enables teams to experiment and validate ideas within hours—a capability that was previously impossible. Solving orchestration could unlock AI’s full potential in software development, fostering widespread adoption and innovation at scale.
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