In early 2026, Capgemini executive Steve Jones declared that AI had "killed" the Agile Manifesto — sparking one of the fiercest debates in the software industry this year. His argument: agentic AI systems that write code, run tests, and deploy features fundamentally contradict the manifesto's core values. But here is the thing — teams that actually understand the manifesto for agile software development know that its principles were never about specific tools or technologies. They were about how humans organize around uncertainty. And in 2026, uncertainty has never been greater.
The Agile Manifesto was written in 2001 by 17 software developers at a ski lodge in Snowbird, Utah. What they produced — four values and twelve principles — became the foundation for how modern software teams work. Twenty-five years later, frameworks like Scrum, Kanban, SAFe, and LeSS have layered processes and certifications on top of those original ideas. Some of those layers help. Many have become the very bureaucracy the manifesto was written to replace.
This article breaks down every value and principle in the manifesto for agile software development, explains which ones have aged well, and shows you how to reinterpret the ones that need updating — especially if your teams are integrating AI into their workflows.
What is the Agile Manifesto?
The Agile Manifesto is a one-page document with four values and twelve supporting principles. It was created on February 11–13, 2001, by a group that included Kent Beck, Ken Schwaber, Jeff Sutherland, Martin Fowler, and thirteen other software practitioners. They shared a common frustration: heavyweight development processes — waterfall, BDUF (Big Design Up Front), rigid phase gates — were slowing down delivery and ignoring what customers actually needed.
The manifesto for agile software development does not prescribe specific practices. It does not mention Scrum, Kanban, sprints, or standups. It establishes a mindset — a set of priorities for teams building software in complex, unpredictable environments. Every Agile framework in use today, from Scrum to Disciplined Agile to SAFe, traces its philosophical roots to this single page.
The 4 Agile values explained
The manifesto states four core values, each framed as a preference — not an absolute. The phrase "while there is value in the items on the right, we value the items on the left more" is often forgotten, but it is essential. The manifesto never says processes, documentation, contracts, or plans are worthless. It says they should serve people, not the other way around.
1. Individuals and interactions over processes and tools
This value remains the most important — and the most violated — principle in modern Agile. Organizations invest heavily in Jira configurations, workflow automations, and reporting dashboards while neglecting the quality of conversations between team members.
In 2026, this value takes on a new dimension. AI tools are now active participants in development workflows. Code assistants generate pull requests. AI agents handle testing and documentation. The question is no longer just "are we prioritizing people over tools?" but "are we clear about which decisions require human judgment and which can be delegated to AI?"
The principle still holds. Teams that over-rely on tools — whether Jira or AI — without strong human interaction consistently underperform. A Scrum.org survey found that the top predictor of Scrum team success was not tooling or process maturity, but the quality of daily collaboration between developers, Scrum Masters, and Product Owners.
What this means for your team: Audit where your team spends its time. If more than 30% of collaboration happens through tool-mediated updates rather than direct conversation, you have a problem that no AI tool will fix.
2. Working software over comprehensive documentation
The original intent was clear: stop writing 200-page specification documents before building anything. Ship working software early and let it speak for itself.
In the AI era, this value is more relevant than ever. AI can generate documentation, test plans, and technical specs in minutes. The bottleneck is no longer documentation creation — it is deciding what is worth building. Teams that still spend weeks on upfront specification before shipping anything testable are wasting their greatest advantage: the ability to learn from real users, fast.
The modern interpretation: working software validated by users is the measure of progress. Not story points completed. Not velocity charts trending upward. Not AI-generated documentation that no one reads.
3. Customer collaboration over contract negotiation
This value was written for an era when software was built under fixed-scope contracts with long change-request cycles. It argued for ongoing dialogue with customers instead of treating a signed requirements document as sacred.
Today, most product teams have internalized this — at least in theory. But a subtler version of the problem has emerged: teams that collaborate with stakeholders but not with actual users. Product Owners gather requirements from business leaders and proxy them to the team, but no one on the team has spoken to a real customer in months. This is contract negotiation wearing a customer collaboration mask.
What this means in practice: If your team's primary feedback loop runs through internal stakeholders rather than end users, you are violating this value regardless of how Agile your ceremonies look.
4. Responding to change over following a plan
The most frequently cited — and most frequently misunderstood — Agile value. "Responding to change" does not mean "no planning." It means building plans that are designed to evolve as you learn.
In 2026, AI makes this value even more powerful. As Mountain Goat Software's Mike Cohn argues, AI makes experimentation so cheap that teams should not merely respond to change — they should create it. AI gives teams the ability to test hypotheses, prototype alternatives, and validate ideas in hours rather than weeks. But this only works when the team has the collaborative discipline to evaluate results honestly and pivot when the data says so.
The teams that struggle most with this value are those locked into quarterly roadmaps that treat features as commitments rather than hypotheses. If your roadmap looks the same in March as it did in January, you are following a plan, not responding to change.
The 12 principles: which still work and which need reinterpretation
The twelve principles behind the manifesto for agile software development provide more specific guidance than the four values. Here is how each holds up in 2026.
Principles that aged perfectly
Principle 1: "Our highest priority is to satisfy the customer through early and continuous delivery of valuable software." This is timeless. The emphasis on valuable — not just working — software prevents teams from shipping features nobody needs. If anything, AI amplifies this principle. When generating code is cheap, the cost of building the wrong thing drops, but the cost of maintaining it does not.
Principle 2: "Welcome changing requirements, even late in development." Still essential. The meaning of "late in development" has shifted, though. With continuous deployment and AI-assisted refactoring, "late" is less risky than it was in 2001. Teams can afford to welcome change more aggressively — if their architecture supports it.
Principle 3: "Deliver working software frequently, from a couple of weeks to a couple of months, with a preference to the shorter timescale." The time frames have compressed dramatically. Understanding what iteration means in practice is more important than ever — many teams now deliver multiple times per day. The principle's spirit — shorter feedback loops beat longer ones — is as true now as it was twenty-five years ago.
Principle 10: "Simplicity — the art of maximizing the amount of work not done — is essential." This is arguably the most underrated principle and the one AI makes most powerful. AI can handle routine coding, testing, and documentation. The human role increasingly is deciding what not to build. Teams that use AI to do more are missing the point. The best teams use AI to focus on less — shipping fewer, higher-impact features with greater confidence.
Principle 12: "At regular intervals, the team reflects on how to become more effective, then tunes and adjusts its behavior accordingly." Retrospectives remain the single most important Agile ceremony. The teams that skip retros or treat them as a checkbox are the same teams that complain Agile does not work. Regular reflection is the engine of continuous improvement, and no AI tool replaces it.
Principles that need reinterpretation for 2026
Principle 6: "The most efficient and effective method of conveying information to and within a development team is face-to-face conversation." This was written before distributed teams became the norm and before AI agents became active participants in team workflows. The underlying truth — high-bandwidth communication beats low-bandwidth communication — still holds. But "face-to-face" now includes video calls, and teams need new protocols for how AI-generated information enters the conversation. When an AI agent surfaces a code review finding or a test failure, someone on the team needs to own communicating the meaning of impediment — what is actually blocking progress — to the rest of the group.
Principle 5: "Build projects around motivated individuals. Give them the environment and support they need, and trust them to get the job done." The principle is sound, but "environment and support" in 2026 includes AI tooling, access to relevant data, and organizational clarity about what decisions AI makes versus what decisions humans make. Teams where AI is introduced without clear boundaries — where developers are unsure whether to trust AI-generated code or review it line by line — are not environments that motivate. They are environments that confuse.
Principle 11: "The best architectures, requirements, and designs emerge from self-organizing teams." The Scrum Guide has already updated this to "self-managing" teams, which is more accurate. In practice, truly self-organizing teams are rare. Most successful teams operate with clear boundaries and authority — self-managing within defined constraints. The AI dimension adds another layer: teams need to self-manage not only their own work but also how they integrate AI-generated outputs into their workflow.
Principle 8: "Agile processes promote sustainable development. The sponsors, developers, and users should be able to maintain a constant pace indefinitely." This principle is under pressure. AI tools create the illusion that teams can go faster indefinitely. Leaders see AI generating code at 3x speed and expect 3x output. This is a recipe for burnout. Sustainable pace must account for the cognitive load of reviewing AI-generated work, making judgment calls AI cannot make, and managing the increased rate of change AI enables.
Principles that most teams ignore (but shouldn't)
Principle 4: "Business people and developers must work together daily throughout the project." The word "daily" is not aspirational — it is prescriptive. Yet most organizations still operate with weekly syncs between business stakeholders and development teams. The result: developers build what was requested last week while business priorities shifted three days ago.
Principle 7: "Working software is the primary measure of progress." Most organizations still measure progress in story points, velocity, or sprint burndown charts. None of these are working software. The principle is blunt: if you cannot demonstrate working software, you have not made progress. Story points completed is not progress. Lines of code written — whether by humans or AI — is not progress.
Principle 9: "Continuous attention to technical excellence and good design enhances agility." This is the principle that separates teams that sustain agility from teams that lose it. Technical debt is the silent killer of Agile transformations. AI-generated code introduces a new risk vector: code that works but is poorly designed, hard to maintain, or opaque in its logic. Teams that accept AI output without rigorous review accumulate technical debt faster than any human developer could.
Why going back to the manifesto fixes broken Agile
Here is a pattern that repeats across organizations: a team adopts Scrum. It works well for a while. Then ceremonies accumulate, processes calcify, and Agile becomes the bureaucracy it was supposed to replace. Standups become status reports. Retrospectives become complaint sessions. Sprint planning becomes a negotiation between Product Owners and developers about how much work to commit to.
The fix is almost always the same: go back to the manifesto. Strip away the process layers and ask four questions:
Are we prioritizing people and conversations over our tools and processes?
Are we delivering working software that users can validate?
Are we collaborating with real customers, not just internal stakeholders?
Are we genuinely responding to what we learn, or defending a plan?
If the answer to any of these is "no," no framework adjustment — no new Scrum ceremony, no SAFe configuration, no Kanban board redesign — will fix the problem. The issue is not the framework. The issue is the values underneath it.
This is especially true for organizations integrating AI into Agile workflows. AI does not fix broken Agile. It accelerates whatever dynamic already exists. If a team delivers value effectively, AI helps them deliver more. If a team is stuck in ceremonial theater, AI just generates more artifacts that no one uses.
What the Agile Manifesto means for AI-augmented teams
The debate about whether AI "kills" the Agile Manifesto misses the point entirely. The manifesto was written to be technology-agnostic. Its authors were not prescribing specific tools — they were describing how humans work best under uncertainty.
AI changes what tasks humans perform, but it does not change the dynamics of uncertainty, collaboration, and learning that the manifesto addresses. If anything, AI makes the manifesto more urgent:
More change, faster — requiring teams that genuinely respond to change rather than follow plans
More output, less signal — requiring teams that focus on valuable working software, not volume
More automation, more judgment calls — requiring teams that prioritize human interaction for the decisions that matter
Organizations that invest in agility training for their teams — not just tool training, but genuine capability building around collaboration, iteration, and continuous improvement — are the ones positioned to thrive. This is exactly what FixAgile, an Agile training and implementation framework designed for the age of AI, is built to deliver. FixAgile helps teams go beyond mechanical Agile adoption and develop the judgment, collaboration skills, and AI-integration practices that the manifesto's values demand.
Key takeaways
The manifesto for agile software development is 25 years old, and its core values are more relevant than they were when they were written. The principles that aged best are the ones focused on human judgment, customer value, simplicity, and continuous improvement. The principles that need updating are the ones tied to specific practices — face-to-face communication, self-organization — that need reinterpretation for distributed, AI-augmented teams.
If your Agile transformation has stalled, if your ceremonies feel like theater, or if your teams are struggling to integrate AI into their workflows without losing the human collaboration that makes Agile work — go back to the manifesto. The answers are still there. And if you need structured support to put those principles into practice, FixAgile's training programs are designed to bridge the gap between Agile theory and the reality of working with AI in 2026.
