Key Takeaways
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Spec drift is the gap between what stakeholders believe the system does, what documentation claims, and what it actually does
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Drift emerges across three layers: Spec ↔ Ticket, Ticket ↔ Code, and Code ↔ Runtime
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Spec drift is not a people problem—it’s a systems problem
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The solution is continuous verification against intent, not better documentation
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Best practice: build systems that detect and make drift visible early
The Problem: Specifications Don’t Stay Fresh
Most teams don’t lose time because they write bad specifications.
They lose time because specifications decay.
The moment a spec is written, reality starts moving: code changes, tickets get re-scoped, edge cases appear, and “temporary” decisions become permanent. Six months later, the spec is no longer a description of the system—it’s a historical artifact.
I’ve seen this pattern repeat across teams and organizations. Not because people don’t care, but because the system moves faster than any manual documentation process can keep up.
This phenomenon is spec drift, and it quietly compounds into real delivery cost.
The Current Moment in Software Delivery
Over the past two years, software delivery has been flooded with AI tooling—code assistants, test generators, autonomous agents, and “self-healing” automation.
I’m genuinely excited about this wave. It removes friction in places that used to feel immovable.
But it also accelerates a dangerous pattern: we can now ship changes faster than we can validate intent.
If the reference point (specification) is already drifting, then faster execution simply produces faster divergence.
What Exactly Is Spec Drift?
Spec drift is the gap between:
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what stakeholders believe the system does,
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what documentation/specifications claim it does,
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and what the system actually does.
It’s rarely a single large mismatch. It’s thousands of small ones:
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A requirement that was partially implemented
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A “quick fix” that changed behavior
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A ticket that captured the outcome but not the underlying decision
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An edge case that became normal usage
Over time, teams stop trusting specifications, and they fall back to the only source of truth they have: tribal knowledge + production behavior.
That’s not a moral failing. It’s a survival mechanism.
How Drift Actually Forms: A Practical Model
In practice, drift emerges across three layers.
Layer 1: Spec ↔ Ticket Drift
The backlog evolves faster than the spec. Teams negotiate scope inside tickets and comments, not inside the canonical specification.
Layer 2: Ticket ↔ Code Drift
Tickets describe “what” at a business level; the code encodes “how” with implicit decisions.
Many of those decisions never make it back to the ticket.
Layer 3: Code ↔ Runtime Drift
Even if code matches intent at merge time, production reality changes:
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Configuration differs per environment
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Data shape changes
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Integrations behave differently
This is why post-hoc documentation always loses.
Where It Hurts (More Than People Admit)
Spec drift shows up as:
Acceptance Fights
When people disagree on what was promised, acceptance becomes subjective. Subjective acceptance is political.
Unpredictable Delivery Cost
The more drift exists, the more time teams spend rediscovering intent.
That time is not planned, not estimated, and rarely measured.
Test Effort That Scales Poorly
Manual test design becomes a translation exercise from outdated specs into current behavior.
Onboarding Drag
New engineers can’t trust the documentation. They learn by reading code and asking the same questions repeatedly.
Compliance and Audit Risk
When you need evidence that reality matches intent, drift becomes a governance problem, not just a delivery one.
What makes this hard is that it usually doesn’t feel like a single big issue. It feels like constant low-level friction. A few hours here, a few days there. Death by a thousand cuts.
Why “More Documentation” Doesn’t Fix It
The common reaction is: “We need to update our docs.”
That’s not wrong. It’s just not scalable.
Documentation is a lagging indicator. The most diligent teams still can’t keep up because the system changes daily.
Spec drift is not a people problem. It’s a systems problem.
What Actually Works: Continuous Verification Against Intent
The structural fix is to treat alignment as a continuous process.
Spec ↔ Tickets ↔ Code should be verified repeatedly, not audited occasionally.
In practical terms, that means:
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Detecting gaps—where a requirement has no coverage, or coverage no longer matches
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Keeping scenarios tied to intent—not only to UI clicks
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Producing objective evidence continuously—not relying on memory
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Keeping delivery economics visible—variance between estimate and reality
Best Practices (If You Want to Start Small)
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Pick one project with recurring acceptance friction.
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Identify 5–10 intent statements that stakeholders care about.
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Translate those into stable scenarios (Gherkin works well).
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Run them continuously (CI), and treat failures as drift signals.
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Track variance: how often did intent change, and when did it become explicit?
A Personal Takeaway
I don’t think teams will ever “discipline” their way out of spec drift.
The only sustainable path I’ve found is to accept that drift is natural, then build systems that detect it early and make it visible.
If you’re feeling the friction—acceptance debates, unpredictable delivery, documentation nobody trusts—you’re not alone. It’s a common pattern.
The interesting question isn’t “how do we write better specs?”
It’s: how do we keep intent and reality aligned as a living process?