Agility
Agility is a way of organizing work characterized by overlapping the execution phases—analysis, design, production, testing, and integration—rather than chaining them together sequentially. It makes it possible to build or modify a product continuously through the delivery of functional, verifiable increments. Unlike concurrent engineering, the quality of the result rests on people's knowledge and collaboration, not on the processes or tools used.
Origins and foundations
Agility as a formal movement emerged in 2001 with the publication of the Agile Manifesto, signed by seventeen software development professionals who shared a dissatisfaction with the prevailing management models. Its four values and twelve principles remain the field's conceptual reference, even though how they're applied has evolved considerably since then.
Agility wasn't born from theory but from practice: the frameworks that underpin it—Scrum, XP, Crystal, Kanban—were developed by teams looking for more effective ways of working in highly uncertain environments.
Agility versus predictive management
The fundamental distinction between agility and predictive management isn't about speed or tools, but about how uncertainty is managed:
- Predictive management assumes it's possible to define the complete product at the start of the project and that deviations from the plan are errors to be corrected.
- Evolutionary management—the basis of agility—assumes that knowledge about the product evolves as it's developed, and that adapting the plan to that knowledge is a healthy practice, not a sign of poor planning.
This difference has consequences for how teams are organized, how decisions are made, how progress is measured, and how the team relates to the customer.
Agility and AI: a two-way relationship
Generative AI is affecting agility in two directions at once:
- AI as a tool within agile teams. Code assistants, documentation generators, and feedback analyzers are changing the speed and nature of the work. Agility, with its emphasis on continuous adaptation, is the framework best prepared to absorb this shift: it lets the team review its practices at every retrospective as the tools evolve.
- Agility as a framework for developing AI products. Building systems based on language models involves an uncertainty about the product's behavior that predictive management can't handle. Iterative cycles, early value delivery, and continuous review with the customer are especially valuable when the product itself learns and changes.
Common mistake
Confusing agility with using Scrum. Scrum is a specific framework for implementing agile principles, but it's neither the only one nor a definition of agility on its own. A team can use Scrum mechanically and not be agile at all, and a team can be highly agile without following any formal framework. Agility is a way of thinking about work, not a set of ceremonies.
Resources
📄 Scrum Master v.4.0Free download · Scrum Manager
References
- Beck, Kent et al. (2001). Manifesto for Agile Software Development. agilemanifesto.org.
- Highsmith, Jim. (2002). Agile Software Development Ecosystems. Addison-Wesley.
See also
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