From Fullstack Teams to AI Agents: Redefining Software Delivery
AI agents in software delivery are specialized, collaborative systems that divide the end-to-end lifecycle into focused tasks, automating requirements analysis, coding, testing, deployment, and monitoring while keeping humans in charge of orchestration and final approval. This shift goes far beyond code autocomplete tools. Companies are moving toward an automated software pipeline where AI agents handle repetitive and structured work, leaving developers to frame problems, tune workflows, and validate outcomes. Endava and LG CNS are early examples of this transition, each building an AI-native delivery model around multi-agent architectures. Their approaches point to a new enterprise AI automation pattern: instead of a single “fullstack developer” owning every layer, a network of agent specialists covers the pipeline, reducing manual handoffs and accelerating delivery. For large organizations, this is less about novelty and more about making complex, legacy-heavy environments faster, safer, and more consistent.
Endava’s Network of Specialized Agents and the AI-Native Mindset
Endava is replacing the classic fullstack workflow with a modular network of AI agents that each own a distinct part of software delivery. Built on a unified platform that integrates ChatGPT Enterprise with Codex models, these agents cover tasks such as turning raw business requirements into user stories, generating boilerplate logic, executing unit tests, and drafting documentation. Another agent acts as an early-stage reviewer, scanning pull requests for vulnerabilities and formatting issues before humans step in. The goal is a reusable library of agents that teams can compose into custom workflows for web apps, data pipelines, or compliance-heavy systems. Developers increasingly focus on defining problems, selecting the right agent chain, and verifying outputs, rather than writing routine code. According to Endava’s approach, the value of AI agents in software delivery lies in rethinking the workflow itself, not merely speeding up line-by-line coding.
LG CNS DevOn Agentic AIND: An Agentic AI Development Platform
LG CNS is pushing the same direction with DevOn Agentic AI Native Development (AIND), an agentic AI development platform built for large enterprise IT systems. AIND was created to overcome the limits of natural language “vibe coding”, where code is generated without a strong grasp of system structure or operational context. In AIND, requirement analysis, design, coding, testing, and quality verification agents collaborate end-to-end once a user enters requirements in plain language. For example, when a financial institution asks for a new automatic transfer savings service, an analysis and design agent first plans the architecture, then a coding agent generates software that follows the client’s standards. LG CNS reports that users mainly review and approve results, cutting development time while keeping control. A key feature is support for legacy modernization, including automatic COBOL-to-Java conversion aligned to modern architectures and development guidelines.
Multi-Agent Architectures and Knowledge Foundations in Enterprise Pipelines
Both Endava and LG CNS show how multi-agent architectures enable a division of labor across the automated software pipeline. Instead of one general-purpose coding assistant, multiple agents operate in parallel on requirements, architecture, code generation, testing, documentation, and review. In LG CNS’s case, this is strengthened by its Knowledge Foundation, an ontology-based database that encodes development standards, security policies, source code, and documentation in machine-readable form. This allows AIND to perform spec-driven development, where design, coding, and verification follow predefined specifications, improving consistency and reducing hallucinations. Endava, meanwhile, is building its own catalog of reusable agents so teams can assemble workflows fit for specific domains like frontend, APIs, data pipelines, or accessibility. Both models rely on strong guardrails: automated scanning of machine-generated code, human sign-off for critical components, and clear data policies to protect intellectual property and meet security expectations.
The New Enterprise Development Model: AI-Augmented, Workflow-First
The rise of agentic AI development platforms signals a shift away from traditional fullstack roles toward AI-augmented development models. Human engineers move up a level: they define business problems, shape specifications, compose agent workflows, and judge final outputs. AI agents handle much of the standard coding, testing, and documentation work in the background. Endava is backing this with organization-wide training so engineers think in terms of agent-assisted systems instead of individual tasks. LG CNS is embedding enterprise knowledge and specs directly into AIND so quality and compliance are baked in, not bolted on. For enterprises, this means fewer manual handoffs between roles, faster iteration on large and legacy systems, and more consistent adherence to standards. As AI agents in software delivery mature, the competitive advantage will come from who can turn these tools into dependable, end-to-end, workflow-first pipelines rather than isolated coding helpers.
