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Decision-less Agility

A Failure Pattern of Decision Avoidance, Context Erosion, and Measurement Gap

Summary​

Decision-less Agility is a Failure Pattern in which, in Agile development, "not deciding" is rationalized as flexibility, and a vacuum in decision-making becomes structurally fixed.

This Pattern describes the process by which decision-making capability is lost in both software and organization as a result of overlapping dynamics such as decision avoidance, absence of measurement, and lack of context. What is addressed here is not errors in specific methodologies or tools, but the mechanism by which well-intentioned choices converge toward a recurring structure of failure.

Context​

This Pattern appears in environments where Agile development has matured as an option and simultaneously, software lifespan has become long-term. In situations where uncertainty is high and all requirements and specifications cannot be determined in advance, Agile is chosen, but there are cases where it proceeds without differentiating between "not deciding the future" and "the minimum that must be decided now." In environments such as contract development or lab-style outsourced development where multiple stakeholders exist, decision-making responsibility is dispersed, and matters that cannot be decided on the spot tend to be structurally deferred. In recent years, AI-assisted development has become common, and the ability to proceed with work while completing ambiguous assumptions and undecided matters further accelerates this dynamic.

When development continues in such situations, implementation proceeds in a form that compensates for undecided assumptions, and the "Source of Truth" for specifications is gradually embedded in code. In software that has been operated for a long time, PO replacements and team turnover occur, leaving only fragmentary documentation and current code. As a result, a state where no one can clearly explain "why it is that way" or "how far it can be changed" becomes normalized.

In such context-deficient environments, AI-assisted development is partially introduced. AI performs reasonable completion from given information, but when assumptions and constraints are not explicit, it makes judgments based on implicit assumptions, just like humans. Therefore, when combined with Agile development where decision-making remains ambiguous, this Pattern tends to manifest.

Forces​

The main dynamics that generate this Pattern are as follows:

  • Uncertainty is high and the future cannot be decided
    Future specifications and behavior cannot be determined, and the choice to "adjust later" is easily rationalized.

  • Risk of holding decision responsibility
    Decisions accompany accountability for future changes and failures, and in multi-stakeholder environments they are easily postponed.

  • Pressure to move quickly
    "The fact of progress" is valued as safety over consensus-building, and implementation proceeds easily while matters remain undecided.

  • Cost of writing documents
    The cost of aligning understanding and updating does not appear in short-term results, and implementation-first is easily rationalized over documenting decisions.

  • Temptation that AI immediately produces "plausible answers"
    Work proceeds even when assumptions and constraints are ambiguous, making undecided matters less likely to surface.

  • Absence of measurement and lack of decision material
    Impact cannot be compared, and deferral is iteratively treated as an implicit optimal solution, accumulating a vacuum in decision-making.

Failure Mode​

  • The "Source of Truth" for specifications disperses into code fragments
    Implementation proceeds while carrying undetermined assumptions, so decisions are locally embedded in the form of functions and configuration values. Intentions and boundaries do not consolidate in one place, and the basis of behavior becomes unreconstructible due to code scattering.

  • Vacuums in decisions accumulate and become fixed as implicit assumptions
    Behavior used tentatively continues and becomes an implicit standard, and no one can specify the changeable range. Changes carry uncertainty of "might break," and as a result, deferral is reproduced.

  • AI completion strengthens assumptions and contradictions become latent
    Work proceeds even without explicit assumptions, so local assumptions are directly confirmed in implementation. Even when different assumptions coexist, they function in the short term, making lack of consistency hard to detect.

  • Absence of measurement disconnects the feedback path
    The magnitude of impact and effectiveness cannot be observed, and decision-making for improvement or correction does not hold. Discussions lean toward opinion conflicts, and decision-making becomes fixed as a habit of postponement.

  • Documentation becomes post-hoc, and Source of Truth becomes absent
    Descriptions are partially updated after the fact and cannot indicate where the latest basis lies. New entrants rely on reverse-engineering code, and the cost and risk of change increase.

  • Boundaries become unstable and dependencies between modules become unpredictable
    Undefined specifications seep into interfaces, and both callers and providers drift toward mutual local optimization. Consistency as a whole becomes hard to maintain, and the impact scope of changes tends to expand.

Consequences​

  • Maintenance cost increases cumulatively
    (Part I: What Breaks — Responsibility / Boundary)
  • A state where "no one understands the whole" becomes fixed
    (Part I: What Breaks — Responsibility / Boundary)
  • AI support can only be utilized in a limited way
    It depends on some personnel who can supplement assumptions and constraints themselves, and does not function as decision support for the organization as a whole.
    (Part II: Why It Breaks — Context Erosion)
  • Agile loses trust
    The gap between the expectation of "supposed to be flexible" and reality cannot be explained, and the process itself becomes questioned.
    (Part II: Why It Breaks — Decision Avoidance)
  • Organizational learning stops
    The learning loop does not hold due to absence of measurement, and improvement is not converted into reproducible knowledge.
    (Part II: Why It Breaks — Broken Learning Loop)

Countermeasures​

The following are not a list of solutions, but counter-patterns for changing dynamics with minimal intervention against Failure Mode.

  • Establish a minimal decision gate
    Differentiate among immutable, tentative, and undecided to prevent deferral from accumulating into "implicit specifications."

  • Introduce a single observational indicator
    Prioritize comparability over precision, creating a state where decisions connect to deltas rather than opinions.

  • Fix boundaries (contracts) for a short period and secure internal degrees of freedom
    Localize external impact and maintain the assumption that iteration can "proceed safely."

  • Unify the Source of Truth to one point
    Protect consistency of the reference point rather than completeness, preventing the current location of specifications from getting lost.

  • Leave decision intent with minimal labels
    Leave immutable/tentative/deadline in adjacent locations, creating a decision history that can be verified later.

  • Fix assumption templates when using AI
    Present assumptions, constraints, and immutables first, and adopt/reject generated output by boundaries and indicators.

  • Normalize clearing of deferred items
    Choose among "decide/discard/send forward," and do not make postponement a stable state.

Resulting Context​

Uncertainty continues to remain, but assumptions for decisions are made visible, and deferral becomes less likely to accumulate.

With a single indicator, effectiveness becomes comparable, and changes are discussed as deltas rather than opinions.

As boundaries and reference points stabilize, changes are treated as verifiable judgments rather than "speculation that might break."

AI-generated output likewise can be adopted/rejected in light of assumptions, boundaries, and indicators.

These do not eliminate uncertainty, but arrange it into a form where decisions can be taken on.

See also​

  • Responsibility Diffusion
    A derived structure in which the dynamic of avoiding decisions makes the decision-making subject itself ambiguous.

  • Specification-by-Absence
    A derived pattern in which, as a result of proceeding without deciding, undefined matters become fixed as de facto specifications.

Appendix: Conceptual References​

Appendix: References​

  • James O. Coplien, Organizational Patterns of Agile Software Development, 2004.
  • W. Edwards Deming, Out of the Crisis, 1982.
  • Stafford Beer, Cybernetics and Management, 1959.
  • David L. Parnas, On the Criteria To Be Used in Decomposing Systems into Modules, 1972.
Last updated on by Hiroshi Kurabayashi