What Agentic AI Development Means for the SDLC
Agentic AI development is the use of autonomous yet supervised AI agents that coordinate planning, coding, testing, and deployment tasks across the software development lifecycle, turning natural‑language intent into working software through end‑to‑end SDLC automation. This marks a clear break from earlier code assistants that focused mainly on line‑level suggestions. According to Forrester’s report on the state of agentic software development, generative AI is now reshaping how software is planned, built, tested, and delivered, rather than only speeding up coding. Instead of treating the SDLC as a chain of disconnected tools, organizations are adopting AI orchestration agents that work together as peers in enterprise development platforms. The result is a model where humans define intent, constraints, and acceptance criteria, while specialized agents take on more of the execution work across analysis, build, quality assurance, and release.
From Vibe Coding to Orchestrated SDLC Automation
The first wave of generative AI tools centered on “vibe coding,” where developers typed natural‑language prompts and received suggested snippets. LG CNS argues this approach hits limits in large enterprise environments because such tools “often generate code without understanding the structure and operational context of enterprise systems.” Agentic AI development addresses this gap by embedding context into the workflow and coordinating multiple agents across the SDLC. Forrester describes this evolution in three phases: early TuringBots that handled coding and unit tests, expanded tools that added documentation and test generation, and a current stage where agents now cover analysis, planning, design, build, test, and delivery. In this third phase, teams can delegate a feature‑level intent, and a collection of AI orchestration agents decomposes the work, creates artifacts, runs tests, and prepares releases under human oversight.
DevOn AIND: A Glimpse of Enterprise-Grade Agentic Platforms
LG CNS’s DevOn Agentic AI Native Development (AIND) platform shows how agentic AI can support the entire lifecycle of large enterprise IT systems. When a user submits requirements in natural language, specialized agents for requirement analysis and design, coding, and testing and quality verification collaborate to deliver an end‑to‑end solution. For example, if a financial institution requests an automatic transfer savings feature for its core banking platform, the analysis agent designs the architecture while the coding agent generates software that follows the institution’s development standards. Users mainly review and approve outputs, which LG CNS says significantly reduces development time. The platform’s “Knowledge Foundation” organizes enterprise IT information into an ontology that AI can understand, and its spec‑driven development approach aligns design, coding, and verification to predefined specifications, supporting consistent quality and reducing hallucinations in SDLC automation.
Cross-Functional SDLC Orchestration and Legacy Modernization
Agentic AI development is not limited to new code paths. DevOn AIND illustrates how AI orchestration agents can manage cross‑functional SDLC tasks, including legacy modernization and architecture transitions. The platform can automatically convert systems written in older languages such as COBOL into Java, while also updating older Java systems to align with modern architectures and development standards. LG CNS notes that AIND’s COBOL‑to‑Java capability is already being applied to next‑generation projects at major financial institutions. Because the underlying Knowledge Foundation includes security policies, standards, source code, and documentation, the same agentic workflows that handle new features can also guide migration, regression testing, and spec‑driven verification. This positions enterprise development platforms as hubs for SDLC automation, where planning, build pipelines, quality gates, and deployment workflows are coordinated by AI agents rather than configured tool by tool.
How Developer Roles and Practices Must Evolve
As agentic AI spreads, software teams must rethink how they design systems and run projects. Forrester notes that developers will write less code and spend more time reviewing, guiding, and orchestrating coding agents, while testers set quality goals and supervise testing agents instead of scripting every test. Product managers generate specs and prototypes that feed spec‑driven development, and architects focus more on system design, constraints, and context engineering to keep agents within safe boundaries. Testing and governance become more important because autonomous agents can propagate defects quickly if guardrails are weak. Tech leaders are advised to pilot AI across multiple SDLC stages, evolve operating models that define human‑agent collaboration, and treat AI‑generated artifacts with at least the same rigor as human output. In this world, the key skill is giving clear intent, context, and constraints to AI peers.
