nousresearch/one-three-one-rule

1-3-1 Communication Rule

Structured decision-making format for when a task has multiple viable approaches and the user needs a clear recommendation. Produces a concise problem framing, three options with trade-offs, and an actionable plan for the recommended path.

When to Use

• The user explicitly asks for a "1-3-1" response.
• The user says "give me options" or "what are my choices" for a technical decision.
• A task has multiple viable approaches with meaningful trade-offs (architecture, tooling, migration strategy).
• The user needs a proposal they can forward to a team or stakeholder.

Do NOT use for simple questions with one obvious answer, debugging sessions, or tasks where the user has already decided on an approach.

Procedure

1. Problem (one sentence)

   • State the core decision or desired outcome in a single concise sentence.
   • Focus on the what, not the how — no implementation details, tool names, or specific technologies.
   • Keep it tight. If you need "and", you're describing two problems.

2. Options (exactly three)

   • Present three distinct, viable approaches labeled A, B, C.
   • Each option gets a brief description, pros, and cons.
   • Options should represent genuinely different strategies, not minor variations of the same approach.

3. Recommendation (one option)

   • State which option you recommend and why, based on the user's context and priorities.
   • Be direct — this is your professional judgment, not a hedge.

4. Definition of Done

   • List the specific success criteria for the recommended option.
   • These are concrete, verifiable outcomes — not vague aspirations.
   • If the user picks a different option, revise this section to match.

5. Implementation Plan

   • Concrete steps to execute the recommended option.
   • Include specific commands, tools, or actions where applicable.
   • If the user picks a different option, revise this section to match.

Verification

• Response has exactly one Problem sentence.
• Response has exactly three Options (A, B, C) with pros and cons for each.
• Response has a single Recommendation that picks one option with reasoning.
• Definition of Done and Implementation Plan align with the recommended option.
• If the user selects a different option, Recommendation, DoD, and Implementation Plan update accordingly.

Example

User: "Give me a 1-3-1 for adding retry logic to our API client."

Agent:

Problem: API calls to external services fail intermittently under load and need graceful retry handling.

Options:

• Option A: Exponential backoff with jitter in a shared utility. Pros: Simple, well-understood pattern; single implementation shared across all callers. Cons: No circuit-breaking; callers still block during retries.

• Option B: Retry library (tenacity) as a decorator on each endpoint call. Pros: Configurable per-endpoint; built-in support for circuit breakers and rate limits. Cons: Adds a dependency; decorator pattern can obscure control flow.

• Option C: Queue-based async retry with dead letter handling. Pros: Non-blocking; failed calls are tracked and replayable; scales well. Cons: Significant complexity increase; requires a queue backend; overkill for low-volume services.

Recommendation: Option A. The call volume doesn't justify queue infrastructure, and a shared utility with exponential backoff + jitter covers the failure mode cleanly without new dependencies.

Definition of Done:

• api_retry() utility handles transient HTTP errors (429, 502, 503, 504).
• Retries up to 3 times with exponential backoff and jitter.
• All external API calls route through the retry utility.
• Logs each retry attempt with endpoint and attempt number.
• Tests cover retry exhaustion, immediate success, and transient-then-success scenarios.

Implementation Plan:

1. Create utils/api_retry.py with configurable max retries, base delay, and retryable status codes.
2. Add jitter using random.uniform(0, base_delay) to prevent thundering herd.
3. Wrap existing API calls in api_client.py with the retry utility.
4. Add unit tests mocking HTTP responses for each retry scenario.
5. Verify under load with a simple stress test against a flaky endpoint mock.