# Success Signals — Architecture Experiment 17 > This is a design document only. Do not implement yet. > Record observations about what success looks like across the investigation turn cycle. --- ## Signal 1 — Narrative Becoming Simpler Over Time ### Observation Early turns produce dense, broad narratives. As the investigation progresses, the narrative should become *simpler* — fewer active unknowns, tighter understanding, more resolved items. If narrative complexity increases as the investigation continues, that is a failure signal. ### What It Looks Like - Early turn: "We know some things about X. We don't know Y, Z, or W. There are several possibilities for each." - Later turn: "We have narrowed it to two scenarios. The key question is whether A or B applies." - Final turn: "The evidence points to one scenario with high confidence. Two areas remain untested and do not affect the conclusion." ### Why It Matters Real investigations simplify. A real expert helps you see less, not more, as understanding deepens. If the narrative gets more complex over time, the investigation is spiralling rather than converging. --- ## Signal 2 — Uncertainty Becomes Targeted Rather Than Diffuse ### Observation Early uncertainty is broad ("I don't know much about this situation"). Successful investigations narrow uncertainty to specific, high-value questions. The user should be able to articulate exactly what remains unknown and why it matters. ### What It Looks Like - Early: "There are a lot of things I'm not sure about." - Middle: "I need to figure out whether the revenue model is sustainable or if this is just temporary." - Late: "We've established the cost structure. The remaining uncertainty is about customer retention, which affects the bottom line but not the current viability." ### Why It Matters Diffuse uncertainty paralyzes decision-making. Targeted uncertainty enables action. The investigation's value increases as uncertainty narrows, even if total uncertainty count remains high (one deeply uncertain critical question is more valuable than ten vaguely uncertain peripheral ones). --- ## Signal 3 — Questions Become Narrower and More Precise ### Observation Early questions are broad and exploratory ("Tell me about the situation"). Successful investigations produce progressively narrower questions. The user should find themselves answering increasingly specific prompts rather than restating what they already know. ### What It Looks Like - Turn 2: "What have you noticed about the customer base?" - Turn 5: "Of the three segments you identified, which has the highest lifetime value and why?" - Turn 8: "You said segment A has higher retention. Is that due to switching costs or product differentiation?" ### Why It Matters Broad questions indicate the investigation is still in orienting mode. Precise questions indicate it has moved through exploring and focusing into deepening. The narrowing trajectory *is* progress — even if nothing has been conclusively resolved yet. --- ## Signal 4 — User Provides Richer Observations Over Time ### Observation As the investigation continues, user contributions should become richer in structure, not just quantity. The user should begin providing evidence, distinguishing facts from assumptions, and offering connections between topics without being asked. ### What It Looks Like - Turn 1: "Business is struggling." - Turn 4: "Revenue dropped 20% but costs stayed flat. I think the issue is customer churn, not acquisition." - Turn 7: "I've checked the data — churn is up 15% in Q3. The correlation with the pricing change is clear, but I haven't looked at whether it's price sensitivity or product quality." ### Why It Matters This signal indicates the user is thinking *with* the facilitator, not just *for* it. The investigation has shifted from information collection to shared reasoning. This is the strongest signal that the behaviour model is working — the user is adopting investigative patterns they did not start with. --- ## Signal 5 — Behaviour Requires Fewer Clarifications ### Observation Early turns require frequent clarification of behaviour intent ("Why are you asking me this?" "What are we trying to find out?"). Successful investigations reduce these meta-comments as the user understands the pattern of interaction. ### What It Looks Like - Early: "Wait, why are you focusing on X when Y seems more important?" - Middle: Occasional "How does that relate?" but mostly continuing without reorienting. - Late: No meta-comments. The user answers directly and sometimes anticipates the next question. ### Why It Matters Meta-comments indicate the user is trying to understand the *process* rather than engage with the *content*. When the process becomes transparent, meta-comments disappear naturally. This is a signal that the investigation rhythm feels natural, not mechanical. --- ## Signal 6 — Shared Understanding Increases Measurably ### Observation The gap between what the user knows and what the system represents should shrink over time. The user should frequently recognise the workspace as an accurate reflection of their thinking. ### What It Looks Like - "That's exactly how I see it." - "You just said what I was trying to figure out." - Periodic corrections that are minor ("Well, not exactly X — more like Y"). - User starts referencing the workspace in conversation ("Looking at what we know, it seems like..."). ### Why It Matters Shared understanding is the core objective of the investigation. If the user treats the workspace as an externalisation of their own thinking rather than a separate system's analysis, the architecture is working as intended. The facilitator becomes a mirror, not an interrogator. --- ## Signal 7 — Investigation Reaches Appropriate Termination Without Force ### Observation A successful investigation ends when understanding is sufficient for the user's purpose, not when every unknown is resolved. The user should signal readiness to conclude, and the facilitator should recognise and validate that readiness without pushing further. ### What It Looks Like - User states they have what they need. - Facilitator acknowledges completion without introducing new lines of enquiry. - Narrative presents a coherent summary rather than a list of remaining gaps. - User reports feeling confident in their understanding, even with residual uncertainty. ### Why It Matters An investigation that cannot end is worse than one that ends early. The ability to know when enough is enough — and to present the findings clearly — is arguably more important than finding every last answer. This signal validates that the architecture supports closure as a first-class state, not an afterthought. --- ## Signal 8 — Investigation Feels Like Conversation Rather Than Questionnaire ### Observation The user should lose awareness of the turn structure. They should not feel like they are answering questions in a process but thinking through a situation with someone who helps them see more clearly. ### What It Looks Like - User forgets to answer all parts of a question because the conversation moved on naturally. - Conversation contains acknowledgments, pauses, and synthesising alongside questions. - The facilitator sometimes says "That's interesting" without immediately asking another question. - Turns feel organic in length — some are short exchanges, some are long explorations. ### Why It Matters This is the ultimate validation of the behavioural model (Experiment 15). If the investigation feels like a questionnaire, the behaviour selection is mechanical regardless of how sophisticated the assessment logic becomes. The architecture succeeds when the user experiences facilitated thinking, not automated investigation. --- ## Signal 9 — Multiple Narrative Projections Serve Different Users Equally Well ### Observation The same investigation should produce useful projections for different audiences: a concise summary for executives, a detailed trace for technical reviewers, and a facilitator view for the active investigator. Each projection should be accurate but optimised for its audience's needs. ### What It Looks Like - Executive sees: "Three scenarios evaluated. Recommendation: Scenario B. Confidence: substantial. Remaining uncertainty affects timeline only." - Technical reviewer sees: Full trace from findings back to original observations, with epistemic labels on each link. - Active investigator sees: Current understanding, active unknowns, confidence signals, and next action — all in one glance. ### Why It Matters This validates the narrative-as-intermediate-layer architecture (Experiment 14). If a single narrative can be projected into multiple accurate but audience-appropriate views without re-implementing translation logic per view, the layer separation is architecturally sound. --- ## Signal 10 — Investigation Self-Corrects Without User Intervention ### Observation When the user corrects the facilitator ("That's not right" or "I didn't mean X"), the investigation should recover gracefully within one or two turns. The system should acknowledge the correction, update its understanding, and reorient without requiring the user to explain themselves multiple times. ### What It Looks Like - User: "Actually, that's not quite right." - Facilitator (next turn): "You're right — I misinterpreted your earlier point. The situation is closer to Y than X. Let me adjust our understanding accordingly." - Investigation continues from the corrected state without re-explaining what was already established. ### Why It Matters Robustness of correction is a measure of architectural health. If every mistake requires restarting or extensive re-explanation, the layers are too tightly coupled or the graph representation lacks sufficient expressiveness. Graceful self-correction indicates healthy layer boundaries. --- ## Recording Note These success signals are observations about what effective facilitated investigation looks like from the user's perspective. They describe outcomes at the human-computer interface boundary — where the architecture either succeeds or fails in its purpose. Which of these actually emerge during implementation will only be known through experimentation. Some may require redefinition. All should guide evaluation of future working implementations.