From Code Autocomplete to Agentic Software Development
Agentic software development is a model in which networks of specialized AI agents collaborate across the entire software development lifecycle, automatically handling planning, coding, testing, documentation, and deployment tasks from a single expression of intent by a human. Instead of focusing on AI that speeds up line-by-line coding, organizations are now delegating whole features or workflows to autonomous agents. Forrester describes this shift as a move from isolated “TuringBots” to orchestrated SDLC agents that operate across analysis, design, build, test, and delivery. This changes the frame from local productivity gains to automated software delivery at system scale. Teams still stay accountable, but AI agents take over a growing share of execution, creating an AI-native API platform and delivery stack where the main bottleneck is orchestration, not typing speed.
APIs as the Playground for AI-Native Platforms
The API layer is where AI agents are first running the full lifecycle, not only assisting with code. Postman’s AI Engineer is a cloud-native agent built into its AI-native API platform, designed to handle API development, testing, documentation, exploration, and CI/CD integration as an always-on teammate. It is powered by a context graph that stores the institutional memory of how each API was built, changed, and governed over time, which allows the agent to take reliable action instead of generating disconnected snippets. This matters because, according to Postman, most APIs today are untested, undocumented, and disconnected from governance workflows, leading to growing context debt and brittle systems. By offloading routine API work to AI agents, teams can restore quality and consistency without scaling headcount, while keeping human owners focused on business intent and policy.
Endava’s Agent Networks and Automated Software Delivery
Endava shows how AI agents software development goes beyond single tools toward modular networks of specialized agents. Rather than giving developers one assistant, Endava is building a library of agents that each own a distinct slice of the agentic software development lifecycle. One agent turns raw business requirements into user stories and functional specs. Another spins up boilerplate logic, executes unit tests, and generates documentation from those specs. A separate “silent reviewer” agent scans pull requests for vulnerabilities, errors, or formatting issues before humans are asked to review. Teams can chain these blocks into custom workflows for web applications, data pipelines, or compliance tasks, creating highly tailored automated software delivery paths. This model makes agents the primary executors of work, while engineers focus on picking, combining, and governing the right sequence of agents for each project.
Engineers Shift to Agent Experience and Orchestration
As agents take over more execution, the engineer’s role is shifting toward agent experience engineering and orchestration. Netlify CTO Dana Lawson argues that writing code was always less than a quarter of an engineer’s job and is now the least strategic part. Engineers become “shepherds of production,” responsible for understanding complex systems, routes to production, and business context so that human–agent collaboration works reliably. Agent experience (AX) blends developer experience and user experience, focusing on where humans and agents intersect in the software delivery lifecycle, not only on making API calls agent-friendly. Platforms like Netlify are being rebuilt to speak both to traditional developers and to AI agents and new “builders” who may not know git or classic tooling. Success depends on designing workflows where signals are pushed to developers by agents, rather than pulled manually from scattered systems.
Crossing the Threshold: What an Agentic SDLC Looks Like
Forrester’s view of the state of agentic software development lifecycle shows a clear threshold: generative AI is now reshaping planning, building, testing, and delivery, not only accelerating code. Earlier waves improved coding speed by 30–40%, but overall team productivity barely moved when planning, testing, and release stayed manual. In an agentic SDLC, teams express intent such as “build this feature,” and agents decompose work, generate artifacts, run tests, and prepare releases. Automated software delivery becomes the norm, while humans stay in the loop for oversight, trade-offs, and approval. This end-to-end approach reduces bottlenecks that appear when AI is applied in only one phase. It also demands new governance, context graphs, and orchestration patterns so that multiple agents can collaborate safely across the full lifecycle without increasing operational risk.
