From Code Assistants to Agentic AI Development
Agentic AI development is a model where multiple specialized AI agents collaborate across the full software development lifecycle, turning human intent into coordinated analysis, design, coding, testing, and deployment with far less manual handoff between stages. After years of focusing on coding helpers, generative AI is now reshaping how software itself is planned and delivered. Forrester describes this as the move from TuringBots embedded in single tools to “agentic software development,” where autonomous agents run across analysis, build, test, and release. Instead of issuing one-off prompts like “write this function,” teams issue higher-level intent such as “build this feature,” and agents decompose the work into tasks and artifacts. Humans remain accountable for quality and outcomes, but AI systems take on more execution, allowing productivity gains to compound across the entire software development lifecycle rather than stalling at the coding phase.
Enterprise AI Platforms Now Orchestrate the Entire SDLC
Enterprise AI platforms are shifting from point solutions to orchestrated SDLC agents that cover the whole software development lifecycle. LG CNS’s DevOn Agentic AIND is a clear example: when a user submits requirements in natural language, requirement-analysis and design agents interpret the request, coding agents generate standards-compliant code, and testing agents verify quality. According to LG CNS, these agents “collaborate organically to perform the full development process in an end-to-end manner,” allowing users to focus on review and approval. AIND’s ontology-based Knowledge Foundation structures development standards, security policies, source code, and documentation into a form AI can understand, so the platform can tailor agent behavior to each enterprise environment. This kind of software development lifecycle automation marks a break from isolated AI coding assistants and anchors a new generation of enterprise AI platforms built to coordinate many agents, not just accelerate a single task.
2026 as the Inflection Point for Orchestrated SDLC Agents
Forrester’s research on agentic software development describes 2026 as a threshold year: AI is no longer limited to faster coding, but is reshaping planning, building, testing, and delivery. Earlier waves of TuringBots in 2023 and 2024 focused on coding and unit tests, then expanded into documentation and design support. Now, orchestrated SDLC agents cover analysis, planning, build, test, and release, with platforms coordinating how they work together. This change matters because isolated coding gains of 30–40% can yield less than 10% overall productivity if planning or release management remain manual. When AI agents operate end to end, bottlenecks move less and throughput improves. Organisations adopting agentic AI development are therefore prioritising platforms that orchestrate agents across the lifecycle, rather than doubling down on a single coding tool that leaves the rest of the pipeline unchanged.
Spec-Driven Development and Legacy Modernisation at Scale
Agentic AI platforms are also changing how specifications and legacy systems are handled. LG CNS built DevOn AIND around a spec-driven development model, where agents perform design, coding, and verification according to predefined specifications. This approach aims to produce consistent quality regardless of who initiates the work and to cut hallucinations and errors by tying every artifact back to explicit specs. AIND’s Knowledge Foundation gives agents detailed knowledge of each client’s development standards and security policies, turning specs into operational guardrails. The platform also extends orchestrated SDLC agents into legacy modernisation. It can convert systems written in older languages such as COBOL into Java and modernise existing Java systems to newer architectures and standards, while keeping development workflows aligned with enterprise rules. This shows how agentic AI development is not confined to greenfield projects, but can automate planning and implementation for large-scale modernisation programmes too.
How Developer Roles Evolve in an Agentic SDLC
As orchestrated SDLC agents take over more execution, human roles shift from doing tasks to directing and validating them. Forrester notes that product managers now generate specs and high-level intent that drive spec-driven development, while developers spend more time reviewing, guiding, and orchestrating coding agents than writing code themselves. Testers move from manually scripting tests to setting quality goals and supervising testing agents, including testing AI systems. Architects and senior engineers focus on system design, constraints, and context engineering so that agents operate within clear boundaries. Across all roles, the key skill becomes the ability to express intent, context, and constraints clearly to AI peers. Governance and testing grow more important, not less, as autonomy rises: organisations must establish strong quality gates, audit trails, and accountability before allowing orchestrated SDLC agents to handle critical production workloads at scale.
