Is Agile Dead? Could Waterfall Come Back as AI Coding Transforms Software Development in 2025?
The last few years have brought immense change to software development. AI coding assistants can draft functions, generate tests, and refactor at a pace that would’ve sounded like science fiction a decade ago. Even back in February 2023, studies were showing that developers with AI “pair programmers” completed tasks nearly 56% faster than a control group — and AI coding has drastically improved since then.
This has led to a very blunt question for leaders: “If AI can write code so quickly, is Agile now an unnecessary complexity, and is Waterfall becoming more effective?”
The Rise and Warping of Agile
Before we answer this question, we need to (quickly, we promise) understand where Agile came from and how it has changed since its inception. Agile started as a counter to traditional, heavy, big-batch planning, and its advantages resulted in widespread adoption. This even extended to enterprise, driving the growth of SAFe (Scaled Agile Framework) for agility at larger teams.
While this did solve real problems, SAFe also started to warp and distort Agile. It brought back aspects like large coordination batches and heavyweight cadence planning. This has swung the pendulum away from Agile’s “responding to change” and back toward Waterfall’s “plan and push.” From what we’ve seen so far, AI seems to only accelerate this more. For example, if code shows up faster than requirements learning and design decisions, large planning batches become even riskier — it means you can ship the wrong thing even sooner.
So, Is AI Killing Agile?
That being said, no. Agile is not dead (or even dying). Although AI cuts down coding time, it doesn’t eliminate product discovery, systems design, governance, or safe delivery. The teams that continue to outperform still optimize their flow metrics like deployment frequency, lead time for changes, Change Failure Rate, and MTTR.
But could Waterfall be “more feasible” now? Yes, for projects where requirements are stable and risk is well-bounded, faster code generation can reduce the headaches of long upfront design. However, most modern product work (especially projects that many of our partners work on) is dominated by requirements uncertainty and feedback-driven adaptation.
Why Does This Matter for Software Developers?
Why should you or your dev team care? You’ve likely found a methodology and workflow that performs well for you and your projects; why change what isn’t broken? The answer is that AI’s value does warrant a shift in most developers’ workflows and even team structures.
Your core values will likely shift up the stack as developers focus less on manual boilerplate code and more on architecture, API shape, testing strategy, threat modeling, and operability. AI has also made it easier than ever to generate “plausible” code — meaning it’s more valuable than ever to choose the right solution and catch subtle risks.
As regulators and auditors are increasingly asking how AI was used to create applications, what data it saw, and how you validated its outputs, documentation and traceability gain significant importance. This is true for all industries, but especially governed industries like healthcare, finance, aerospace, and more. This shift will likely change team dynamics as well, as seniors become pseudo “editor-in-chiefs” while juniors focus on prompt engineering, verification, and measurement.
Agile Isn’t Dying, It’s Evolving
Our view on Agile software development is not that it’s dead or dying, but that it’s continuing to evolve as the environment around it changes. Agile’s promise was never to “write code slowly” — it promised to “optimize for learning.” As a result, AI (when used properly) can support this and compress the iteration loop for more efficient Agile development. The actual changes will be the implementation details, such as rituals, roles, and risk controls, to match a landscape where the timeline from idea to code is minutes instead of days.
A New AI-Driven Agile Manifesto?
With these changes to Agile, it may even warrant a review of the Agile Manifesto to see how it might be updated for 2025. Most people simply memorize the ordered pairs of Agile values (“individuals and interactions over processes and tools,” “working software over comprehensive documentation,” etc.) without really understanding the premise that anchors the Manifesto. An updated version could help resolve this, especially because the core ethos of “learning through doing” is more relevant than ever.
In fact, our own Dr. Sriram Rajagopalan came up with a concept for a refreshed Agile Manifesto that re-centers it around values that stand firm in an AI-driven world. This new structure would still value traditional facets, but it would shift priorities towards:
- Automated Quality Control OVER Software Testing
- Comprehensive Documentation OVER Writing Code
- Risk Management OVER Burndown Charts
- Architectural Governance OVER Code Reviews
Practical Implications for Software Development Teams
We’ve talked broadly about implications and impact so far, but what about tangible impact on specific roles and aspects of software development?
For Senior Devs
We expect senior team members to transition from authors to editors, reviewing more and writing less. They’ll handle the upper stack priorities mentioned earlier, such as architecture seams, invariants, and anti-corruption layers. Senior devs will also probably be the ones to own AI guardrails, lead the way on SSDF-aligned practices, and champion LLM threat models (e.g. prompt injection, data leakage, etc.).
For Junior Devs
We anticipate that more junior team members will manage AI outputs, essentially treating AI tools like bright (but error-prone) interns. They’ll focus on proving correctness via tests, logs, and debuggers to verify outputs.
Shift in Roles & Skillsets
All developers, regardless of seniority, will need to learn new skills and adapt to new roles. This includes building reliable and repeatable prompts, enhanced regression checks, and more to evolve with AI’s growing role in the pipeline. Developers will also need to be ready to collaborate more closely with PM/UX to iterate on problem framing before code is minted.
Tool Fluency
In addition to skillsets, developers should be fluent across the stack of tools. Chat in your IDE, your repo, and your test toolchain. Optimize your platform for AI-assisted code, tests, and docs. “Platform engineering” will likely be a major differentiator when it comes to the next frontier of productivity, so invest in this area wisely.
Quality Trade-Offs
Although AI can produce secure, well-structured code, it can also generate passable nonsense. Dev teams will need to plan security reviews that are aligned with OWASP’s Top 10 and gate risky changes behind stronger tests. This will balance quality with speed, hopefully mitigating the trade-offs for speed that AI tools tend to make.
Governance, Risk, & Security
Last but not least, governance is becoming a serious concern when it comes to AI code generation. Development teams should adopt some form of AI management system like NIST AI RMF and/or ISO 42001 to organize policies, roles, risk registers, model inventory, and audits. We also recommend that developers keep an eye on regulation changes to stay ahead of any adjustments that might need to be made in the future.
Actionable Ways Agile Teams Can Use AI-Driven Development Tools
There are several best practices and ways that Agile teams specifically can leverage AI tools so they don’t fall behind:
- Amplify, Don’t Replace: Use AI to accelerate learning loops, but keep humans accountable for correctness and ethics.
- Evolve Agile Rituals: Capture acceptance criteria as executable checks that the AI can target, shorten the planning horizon, and add a “provenance & AI-use” checklist (what was AI-generated and how was it verified?).
- Implement Guardrails: Pre-commit hooks to flag secrets, licenses, unsafe patterns in the IDE, OWASP LLM mitigations in the app, etc.
- Invest in Training & Reskilling: Not only training seniors on security/architecture and juniors on debugging and test design, but consider a team-owned prompt library tied to your architecture and coding standards.
- Build Governance From Day 1: Align your program to NIST AI RMF for risk practices and ISO 42001 for management-system guidelines. For international teams, we also recommend mapping controls to emerging EU AI Act obligations for GPAI (where relevant).
Takeaway: Invest in Flexible Tools Like SpiraTeam
To reiterate, AI isn’t killing Agile — it’s forcing the framework to continue to evolve. This is the promise of Agile, to remain flexible and optimize for learning. Unfortunately, Agile teams can usually only adapt as much as their tools can. This means that it’s crucial to invest in versatile development platforms that can adjust to shifting Agile approaches (or even adopt Waterfall/Hybrid aspects).
SpiraTeam provides this flexibility for your AI-driven software development, including AI-powered features of its own. From its methodology-agnostic design to its scalable pricing structure, Spira enhances your development pipeline and sets your team up for success. Instead of relying on old, out-of-date tools that haven’t kept pace with the Agile changes we’ve discussed above, make the switch to an industry-leading platform that revolutionizes how teams manage and plan their projects. Get started with a free 30-day trial by clicking the button below!
