Code shows what the system does. It rarely preserves why a team chose one boundary, provider, data model, or failure policy over another. Without that context, future engineers repeat research or remove constraints they do not know exist.
An architecture decision record is a small durable document for a material choice.
Record decisions with consequences
Capture the context, decision, considered options, evidence, tradeoffs, status, and consequences. Link to experiments, diagrams, incidents, and requirements rather than copying them.
Write at the moment of commitment
Draft the record while the decision is being reviewed. A retrospective document often replaces uncertainty with false confidence.
Use revisit triggers
State what would invalidate the decision: volume, latency, provider change, new regulation, team capacity, or a failed assumption. Supersede records rather than rewriting history.
Keep the system close to the code
Store records in the repository, review them with changes, assign owners, and make them searchable. The practice should reduce coordination cost, not create an approval board for every implementation detail.
Record the choice at the moment it matters
An ADR should state context, decision, status, alternatives considered, consequences, and evidence. Keep it concise enough to update during delivery. The goal is not a perfect history; it is to preserve why a consequential choice was reasonable under the information available.
Write ADRs for decisions that shape multiple components, create costly reversal, establish a standard, or resolve recurring disagreement. Avoid recording every implementation detail. Link decisions to code, diagrams, incidents, and measurements so future teams can inspect the basis.
Mark superseded decisions rather than rewriting history. A changed decision is valuable evidence about how requirements or technology evolved.
Use ADRs as an operating tool
Review relevant decisions during onboarding, architecture change, incident analysis, and major dependency upgrades. Add review triggers where an assumption has a measurable boundary, such as traffic volume, provider capability, or regulatory scope.
Ownership matters more than format. Store records beside the system, use searchable identifiers, and include them in review practice. A large architecture wiki that engineers do not encounter during change provides little protection.
Executive decision record
The decision is which consequential technical choice should be preserved with its context so future teams can change it intelligently. Write that decision before selecting a model, vendor, framework, or implementation pattern. A written boundary keeps technical exploration connected to the operating outcome and makes it possible to explain why the organization advanced, revised, or stopped the work.
Approval should depend on a concise decision, considered alternatives, consequences, linked evidence, status, owner, and measurable review trigger. The evidence does not need to remove every uncertainty, but it should address the uncertainty capable of changing value, architecture, risk, or ownership. Record the baseline, assumptions, unresolved questions, and the person accepting the next stage.
Failure boundary and operating ownership
The central failure to guard against is creating a documentation archive detached from code and change practice or rewriting history when a decision is superseded. Treat that condition as a testable scenario. Define how the system detects it, what users experience, which action is prevented or reversed, and what evidence reaches the person responsible for recovery.
Long-term accountability sits with the team responsible for the affected architecture, with reviewers who can challenge consequences before adoption. Supporting specialists can provide platforms, research, review, or delivery capacity, but they cannot substitute for an owner who controls policy and operating change. Name that owner before production and include the ownership path in release evidence and incident procedure.
A practical 90-day application plan
During the first 30 days, convert a concise decision, considered alternatives, consequences, linked evidence, status, owner, and measurable review trigger into a bounded evidence plan. Assign each artifact to a named contributor, identify the representative inputs required, and agree on the comparison baseline before implementation expands. The objective of this period is to expose the assumption most likely to invalidate the work while the cost of changing direction is still low.
During days 31 through 60, build or instrument the smallest complete workflow that can support the decision about which consequential technical choice should be preserved with its context so future teams can change it intelligently. Include the real data and authorization path where feasible, record exceptions, and review difficult cases with the people who own the underlying process. Resist adding breadth until the team can explain the measured behavior of this narrow slice.
During days 61 through 90, test the boundary represented by creating a documentation archive detached from code and change practice or rewriting history when a decision is superseded. Exercise degraded dependencies, ambiguous inputs, recovery, and handoff rather than demonstrating only successful cases. End the period with a written advance, revise, or stop decision that cites evidence, residual exposure, expected operating cost, and the next authority boundary.
The review should be accepted by the team responsible for the affected architecture, with reviewers who can challenge consequences before adoption. That group should confirm not only that the system can work, but that ownership, support capacity, monitoring, and change control are credible. If those conditions are absent, the responsible outcome is another bounded learning stage rather than an unsupported production commitment.
Practical checklist
- Material and durable decision
- Context and constraints
- Options and evidence
- Decision and consequences
- Owner and date
- Revisit triggers
- Links to implementation and tests
Continue reading
- [Software discovery phase](/software-discovery-phase-guide)
- [Production readiness checklist](/production-readiness-checklist-software)
Sources and further reading
- [Architecture Decision Record community](https://adr.github.io/)