Diagnose feedback loops to predict how complex systems behave and find where intervention actually works.
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Coding
Cynefin
Try itDiagnose which of five decision domains your situation belongs to, then match the right approach — before committing to a plan.
What it does
Cynefin is Dave Snowden's sense-making framework (IBM, 1999) that helps teams identify which of five domains — Clear, Complicated, Complex, Chaotic, or Confused — their situation falls into before choosing a response method. The framework provides a four-step diagnostic process: describe the situation, diagnose the domain, match an approach (S-C-R, S-A-R, P-S-R, or A-S-R), and check for boundary shifts. The most common costly mistake: treating Complex problems as Complicated. Output includes a named domain with evidence, matched decision method, and boundary monitoring signals. Activates when best practices stop working, experts disagree, or your existing playbook doesn't fit.
When to use it
- Best practices from another team keep failing
- Domain experts disagree on the right approach
- Planning a new initiative in unclear territory
- Deciding how to allocate AI investments across different risk levels
The skill document
Cynefin
Overview
Cynefin (pronounced "kuh-NEV-in"; Welsh for "habitat") is a sense-making framework by Dave Snowden (IBM, 1999). Its claim: the right decision approach depends on which of five domains the situation falls into — Clear (obvious cause-effect, use SOP), Complicated (knowable with expertise, use analysis), Complex (emergent, probe first), Chaotic (absent cause-effect, act first), Confused (unknown domain, decompose first). The most common and costly error: treating Complex problems as Complicated.
Composes with ooda-loop, feedback-loops, antifragile, first-principles.
When to Use
- A familiar approach has stopped working and you can't articulate why
- Experts disagree on the right answer — a crisis unfolding where the previous playbook doesn't apply
- "Best practices from X" imported without checking if the domain matches
- A team is over-planning something emergent, or "let's get more data" when data won't come without action
- Allocating AI capex or racing AI-native competition: deciding which AI bets are engineering (Complicated), emergent agent/adoption experiments (Complex), or live incidents (Chaotic)
Not when: domain is unambiguously Clear (execution only); small-stakes one-shot; specialized framework already fits.
Coaching Novices (Adaptive Front Door)
Engine mode: concrete case → run The Process. Coach mode: unfamiliar → guide step by step. In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- Classify the situation (Clear/Complicated/Complex/Chaotic) and match decision method to domain.
- Check fit: if unambiguously routine (Clear), skip framework.
- Elicit their real case — decision, current method, cause-effect structure.
[WAIT — do not advance until user responds]
- Are cause-effect relationships obvious, knowable, emergent, or absent? Is current method matched?
[WAIT — do not advance until user responds]
- Close: named domain + matched decision method + boundary watch.
[WAIT — do not advance until user responds]
The Process
Step 1 — Describe: Decision/situation: | Current approach: | What worked/not: | Stakeholders:
Step 2: Diagnose the domain
Obvious to everyone? (Clear) | Knowable with expertise? (Complicated)
Only retrospective? (Complex) | Absent/in flux? (Chaotic) | Unknown? (Confused)
Diagnostics: 5 experts converge? (Yes → Complicated; No → Complex). Standard best practice works? (Yes → Clear/Complicated; No → Complex/Chaotic). Interventions predictable? (Yes → Clear/Complicated; No → Complex/Chaotic).
Step 3: Match approach to domain
Clear: Sense→Categorize→Respond (SOP/automate) | Complicated: Sense→Analyze→Respond (experts)
Complex: Probe→Sense→Respond (safe-to-fail experiments, amplify wins)
Chaotic: Act→Sense→Respond (establish order, then re-classify) | Confused: decompose, classify each part
Step 4: Check boundary movement + choose intervention
Domain shifted? (Complicated→Complex from disruption? Clear-Chaotic cliff approaching?)
Clear: deploy SOP; monitor. Complicated: experts; pick defensible alternative.
Complex: parallel safe-to-fail probes; amplify wins. Chaotic: decisive action; re-diagnose.
Boundary watch: shift signals | who monitors | re-diagnosis schedule
Output template:
Cynefin Diagnosis:
Domain: [Clear/Complicated/Complex/Chaotic/Confused] | Evidence: [cause-effect, expert agreement]
Method: [S-C-R / S-A-R / P-S-R / A-S-R] | Actions: | Mismatch cost (if any):
Boundary watch: [shift signals | monitoring owner | re-diagnosis schedule]
→ Method in Action: Snowden at IBM (1999) and the HBR Synthesis (2007) · Apollo 13 Mission Response (1970) → 2026 lens: Sorting AI Decisions by Domain (2024–2026)
Pack: Cynefin Domain Patterns
| Domain | Examples | Method | Mistake |
|---|---|---|---|
| Clear | Routine compliance; manufacturing QC | S→Categorize→R; SOP | Over-analysis |
| Complicated | Engineering design; surgery; M&A | S→Analyze→R; experts | Analysis paralysis |
| Complex | Startup PMF; org culture; new market entry | Probe→S→R; safe-to-fail | Over-planning |
| Chaotic | Crisis first 24h; security breach | Act→S→R; decisive action | Deliberating |
| Confused | New market; leadership transition | Decompose; classify each | Defaulting to home domain |
→ Primary sources: references/sources.md
Common Rationalizations
[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.
| Fake move | Reality |
|---|---|
| [D] "We just need a better plan" | Often the issue is Complex — no plan works; probes and adaptation required. |
| [D] "Get me an expert" | Right for Complicated. Wrong for Complex: experts disagree because cause-effect is emergent. |
| [D] "Do what worked last time" | Right for Clear. Dangerous near Clear-Chaotic boundary — produces the cliff fall. |
| [D] "We need more data" | Often a deflection in Complex/Chaotic where data only emerges from probes/action. |
| [D] "The plan is right; execution is the problem" | Classic post-mortem rationalization when Complicated-domain plan failed on Complex-domain problem. |
| [D] "Best practices from industry X" | Only transfers if industry X has the same domain structure. Complex ≠ Complicated. |
| [D] "We need more analysis / more decisiveness" | Analysis: right for Complicated, wrong for Complex/Chaotic. Decisiveness: right for Chaotic, wrong for Complex. |
| → Add [O] entries here after each real use — paste the actual failure pattern | What went wrong and why |
Red Flags
- Repeated failure always blamed on "execution" — "Best practices" imported without domain check
- Experts disagree on the right answer (Complex signal) — Crisis response dominated by analysis
- Complex situation managed with a single plan, not a probe portfolio
- Team waiting for clarity in a domain where clarity only comes from acting
Verification
- Domain explicitly named with diagnostic evidence
- Decision method matched to domain (S-C-R / S-A-R / P-S-R / A-S-R)
- If current approach mismatches: mismatch cost named
- Boundary signals identified; re-diagnosis schedule set
- If Complex: ≥3 safe-to-fail probes designed
- If Chaotic: order-establishing action specified
Part of deciqAI Knowledge Skills — 227 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/c/cynefin · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.
Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/cynefin.json
Questions people ask
- When should I use Cynefin vs. other decision frameworks?
- Use Cynefin when your situation feels ambiguous and familiar approaches aren't working. The skill explicitly says not to activate when the domain is unambiguously Clear (execution only) or when a specialized framework like OODA or expected value analysis already fits.
- What does the skill actually produce?
- The output is a structured diagnosis: the named domain (e.g., Complex) with supporting evidence, the matched decision method (e.g., Probe-Sense-Respond), any mismatch cost if your current approach is wrong for the domain, and boundary watch signals for re-diagnosis.
- What's the most important mistake Cynefin helps avoid?
- Treating Complex problems as Complicated. In Complex domains, cause-effect is only knowable in retrospect — analysis and expert consensus won't find the answer. You need safe-to-fail probes instead. The framework gives you diagnostic questions to distinguish these domains.
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