Invoke when a design decision has multiple viable implementations and the choice affects performance, memory, complexity, or blast radius โ forces enumerating real alternatives, quantifying the axes that matter, and recording what would reverse the decision.
Installation
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Invoke when a design decision has multiple viable implementations and the choice affects performance, memory, complexity, or blast radius โ forces enumerating real alternatives, quantifying the axes that matter, and recording what would reverse the decision.
Trade-off Articulation
Status: v0.1 baseline draft โ derived from the case catalog and axis definitions; awaiting trace evidence from Fable 5 case runs.
When to apply
Two or more implementations would each pass the tests, but they differ on latency, memory, throughput, operational complexity, or blast radius.
You are about to pick a data structure, storage layout, locking strategy, caching policy, consistency model, or protocol โ categories where the "obvious" choice is often only obvious because alternatives were never named.
A reviewer, teammate, or future session will need to understand why this shape was chosen, not just what was chosen.
The decision is expensive to reverse (schema, wire format, public API, on-disk layout) or its cost scales with data/traffic growth.
You notice yourself justifying a design after writing it instead of before โ that is the signal to stop and run this loop retroactively.
Do NOT apply when one option strictly dominates (better or equal on every axis you care about) or the decision is trivially reversible in one commit โ name the dominant option in one sentence and move on.
Core loop
State the decision as a question with a scope.
One sentence: "How should X handle Y, given constraint Z?"
If you cannot phrase the constraint, you do not yet know enough to decide โ go measure or read first.
Timebox the analysis before starting it.
Budget proportional to reversal cost: minutes for a reversible internal choice, 10x longer for a persisted format or public contract.
Write the budget down. When it expires, decide with what you have.
Enumerate 2-4 real alternatives.
Each must be something you would actually ship โ no strawmen inserted to make the favorite look good.
If you can only produce one candidate, either the decision is dominated (skip deliberation, say so) or you have not explored enough (spend part of the timebox searching for a second).
Name the axes that matter for THIS decision, then drop the rest.
Typical axes: latency (p50/p99), memory footprint, write/read amplification, implementation complexity (LOC, new dependencies, new failure modes), blast radius (what breaks if this is wrong), migration/reversal cost.
Three axes is usually right; more than five means you haven't decided what matters.
Quantify each cell, even roughly.
Prefer measured numbers; accept back-of-envelope estimates with stated assumptions ("~10^5 entries ร 64B โ 6.4MB โ fits in RAM easily, not in L2").
Order-of-magnitude precision is enough to eliminate most options.
Mark unmeasured cells explicitly as estimates โ never silently mix measured and guessed numbers.
Kill dominated options first.
Any alternative that is worse-or-equal on every relevant axis dies immediately.
What remains is a genuine trade-off frontier โ usually 2 options.
Choose, and state the reason as a priority ordering.
"We pick A because p99 latency matters more than memory here, because <requirement/SLO/constraint>."
The reason must reference the axes from step 4, not vibes ("cleaner", "more elegant") โ if an aesthetic property matters, translate it into an axis (maintenance cost, review time, bug surface).
Record the reversal condition.
One or two lines: "Revisit if entry count exceeds ~10^7, if the dependency is deprecated, or if profiling shows this path above 5% of CPU."
This is the highest-value artifact โ it converts a frozen decision into a monitored one.
Write the record where the code lives, then implement only the winner.
A 5-10 line comment block, ADR stub, or commit-message section. Minimal template:
DECISION: <the question from step 1>
CONSIDERED:
A: <one line> โ rejected: <losing axis + magnitude>
B: <one line> โ chosen
WHY: <priority ordering over the axes, tied to a requirement>
REVISIT IF: <numeric trigger(s) someone can observe or alert on>
Do not hedge by half-implementing two options.
Heuristics
If reversal cost is one commit, the analysis budget is ~2 minutes; if it's a data migration or API break, budget 10x more.
2 alternatives is the minimum for a real decision; 4 is the maximum worth tabulating โ beyond that, cluster options into families first and decide between families.
An estimate within 10x is enough to kill an option; only measure precisely when the frontier options are within 10x of each other on the deciding axis.
If every axis favors the same option, stop building the table โ declare dominance in one sentence and implement.
Complexity is quantifiable: count new dependencies, new concurrent interactions, and new failure modes an on-call engineer must understand. Each is a number, not a feeling.
Blast radius question to always ask: "if this choice is wrong, what is the worst artifact โ a slow endpoint, corrupted data, or a wire format we support forever?" Corruption and forever-formats justify blowing the timebox.
When two frontier options tie after quantification, pick the one cheaper to reverse and tighten the reversal condition's trigger.
If you are researching a fifth axis or a fifth alternative, the timebox has failed โ decide now with the table you have.
A hard constraint (SLO, memory ceiling, compliance rule) is not an axis to weigh โ it is a filter applied before the table. Filter first, weigh what survives.
If a decision keeps resurfacing across sessions, the recorded reversal condition was too vague โ rewrite it with a numeric trigger someone can alert on.
Never let the deciding axis be the one you cannot estimate: either spend part of the timebox getting a rough number for it, or demote it and decide on the axes you can score.
The comparison table belongs in the repo only when reversal cost is high (ADR-worthy); for cheap decisions the table lives in your reasoning and only the WHY + REVISIT IF lines get written down.
Estimates that decided the outcome deserve a follow-up measurement hook (metric, benchmark, assertion) โ an estimate that stays unvalidated is a latent wrong decision.
Anti-patterns
Post-hoc rationalization โ writing the justification after the code, fitting reasons to the choice already made.
โ Corrective: run steps 3-6 before writing the implementation; if code already exists, honestly ask whether option B would survive the same table.
Strawman alternatives โ listing "do nothing" and one absurd option next to the favorite so the comparison is theater.
โ Corrective: every listed alternative must be one you would defend shipping; delete any you wouldn't.
Unbounded deliberation โ endlessly refining estimates on a decision that costs one commit to reverse.
โ Corrective: set the timebox in step 2 and treat expiry as a hard decision trigger; cheap-to-reverse means decide fast and instrument.
Qualitative mush โ a table full of "fast / faster / simpler / cleaner" with no numbers.
โ Corrective: replace every comparative adjective with a magnitude and a unit, even if estimated ("~2x allocations per request", "+1 dependency, +300 LOC").
Decision without a reversal condition โ choosing correctly for today's load and never noticing when the assumption breaks.
โ Corrective: always write step 8's trigger; if you can't name what would change your mind, you haven't understood why you chose.
Hedged implementation โ keeping both options half-alive behind a flag "just in case," doubling the maintenance surface.
โ Corrective: implement the winner fully; the reversal condition, not dead code, is your insurance.
Worked example
Illustrative construction, not a recorded run.
Task: rate limiter for ~100k req/s across 4 API nodes.
Agent states the question โ "How to enforce a per-key sliding-window limit across nodes, given p99 budget 2ms and tolerable over-admission โค5%?" โ because without the 5% tolerance the exactness axis cannot be scored.
Agent sets the timebox โ 15 minutes of analysis, no prototype โ because the choice is wire-adjacent in behavior but internally reversible (no persisted format, no public API).
Agent enumerates 3 real alternatives:
(A) Redis sorted-set exact sliding window.
(B) Redis fixed-window counters with two-bucket interpolation.
(C) Local in-process token buckets with async counter sync.
Agent names 4 axes โ added p99 latency, Redis load, over-admission %, failure blast radius โ and drops "code elegance" because it doesn't survive translation into a measurable axis at this size.
~10-20% worst case, N nodes ร sync interval (est)
Redis-outage behavior
limiter down
limiter down
degrades gracefully
Agent kills the dominated option โ A dies: B is cheaper on every axis except exactness, and 3-5% error sits inside the stated 5% tolerance, so exactness buys nothing here.
Agent resolves the frontier (B vs C) โ B wins accuracy; C wins latency and outage behavior. Priority ordering from constraints: the 5% over-admission tolerance is a hard product requirement (C's 10-20% estimate fails the filter under worst-case sync lag), and the 2ms p99 budget comfortably fits one Redis RTT. Picks B.
Agent records the reversal condition in the module docstring โ "Revisit toward local buckets (option C) if Redis p99 RTT exceeds 1ms under load, node count exceeds ~20, or a Redis outage postmortem shows limiter unavailability caused a cascading failure."
Agent implements B only, and adds a metric on window-boundary over-admission โ so the 3-5% estimate becomes a measured number and the table's weakest cell gets validated in production.
Total deliberation cost: under the 15-minute timebox, one table, zero prototypes โ and the record means the next engineer who proposes "just use local buckets" gets an answer with numbers instead of an argument.
