Aitesis Protocol

Infer context insufficiency before execution through AI-guided inquiry. Type: (ContextInsufficient, AI, INQUIRE, Prospect) → InformedExecution.

Definition

Aitesis (αἴτησις): A dialogical act of proactively inferring context sufficiency before execution, where AI identifies uncertainties across multiple dimensions (factual, coherence, relevance), collects contextual evidence via codebase exploration, classifies each uncertainty by dimension and verifiability, and inquires about remaining uncertainties through information-gain prioritized mini-choices for user resolution.

── FLOW ──
Aitesis(X) → Scan(X, dimensions) → Uᵢ → Ctx(Uᵢ) → (Uᵢ', Uᵣ) →
  classify(Uᵢ', dimension) → (Uᵣ', Uₑ, Uᵢ'', Uₙ) →
  Q(classify_result + Uₑ + Uᵢ'', priority) → A → X' → (loop until informed)
-- Uₙ (non-factual): shown in classify summary with routing target
-- Uᵢ'' (factual/user-dependent): Phase 2 question candidates

── MORPHISM ──
Prospect
  → scan(prospect, context, dimensions)  -- infer context insufficiency (multi-dimension)
  → collect(uncertainties, codebase)     -- enrich via evidence collection
  → classify(enrichable, dimension)      -- epistemic classification (core act)
  → observe(empirically_observable, environment) -- dynamic evidence gathering (factual only)
  → surface(classify_result + observed + remaining, as_inquiry)
  → integrate(answer, prospect)
  → InformedExecution
requires: uncertain(sufficiency(X))      -- runtime gate (Phase 0)
deficit:  ContextInsufficient            -- activation precondition (Layer 1/2)
preserves: task_identity(X)              -- task intent invariant; prospect context mutated (X → X')
invariant: Evidence over Inference over Detection

── TYPES ──
X        = Prospect for action (source-agnostic: task execution, analysis, investigation, or any purposeful action requiring context)
             -- Input type: morphism processes X uniformly; enumeration scopes the definition, not behavioral dispatch
Scan     = Context sufficiency scan: X → Set(Uncertainty)
Uncertainty = { domain: String, description: String, context: Set(Evidence) }
Evidence = { source: String, content: String }                -- collected during Ctx
Priority ∈ {Critical, Significant, Marginal}
Uᵢ       = Identified uncertainties from Scan(X)
Ctx      = Context collection: Uᵢ → (Uᵢ', Uᵣ)
Uᵢ'      = Enriched uncertainties (evidence added, not resolved)
Uᵣ       = Context-resolved uncertainties (resolved during collection)
Q        = Inquiry (gate interaction), ordered by information gain
A        = User answer ∈ {Provide(context), Point(location), Dismiss}
X'       = Updated prospect (context-enriched)
InformedExecution = X' where remaining = ∅ ∨ user_esc
-- Layer 1 (epistemic)
Dimension    ∈ {Factual, Coherence, Relevance} ∪ Emergent(Dimension)
               -- open set; external human communication excluded
Observability ∈ {StaticObservation, DynamicObservation, BeliefVerification}
               -- exists(fact, env) sub-modes
-- Layer 2 (tool implementation, Factual fiber only — fibration structure)
Verifiability ∈ {ReadOnlyVerifiable, EmpiricallyObservable, UserDependent}
classify   = Uᵢ' → Σ(d: Dimension). Fiber(d)
             where Fiber(Factual)       = Verifiability
                   Fiber(Coherence)     = Unit    -- detect only
                   Fiber(Relevance)     = Unit    -- detect only
                   Fiber(Emergent(_))   = Unit    -- detect only (default; refinable per discovered dimension)
             -- 2-layer model = Grothendieck fibration: Layer 2 exists only over Factual fiber
             -- Coherence/Relevance → detect + show routing target in classify summary
ObservationSpec = { setup: Action, execute: Action, observe: Predicate, cleanup: Action }
EmpiricalObservation = (Uᵢ', ObservationSpec) → Uₑ  -- dynamic evidence gathering
Uᵣ'        = Read-only verified uncertainties    -- resolved (no Phase 2)
Uₑ_candidates = { u ∈ Uᵢ' : classify(u) = (Factual, EmpiricallyObservable) }  -- Phase 1 observation gate
Uₑ         = Empirically observed uncertainties    -- evidence attached, proceeds to Phase 2
Uᵢ''       = Remaining user-dependent uncertainties  -- Fiber(Factual) = UserDependent; Phase 2 question
Uₙ         = Non-factual detected uncertainties     -- Fiber(d) = Unit; shown in classify summary with routing target
Action     = Tool call sequence (Write, Bash)
EscapeCondition ∈ {EnvironmentMutation, BoundExceeded, RiskElevated, StructuralUncertainty}
                    -- maps to Rule 20 (a)-(d) escape hatches; logged in observation_skips

── PHASE TRANSITIONS ──
Phase 0: X → Scan(X, dimensions) → Uᵢ?                        -- context sufficiency gate (silent)
Phase 1: Uᵢ → Ctx(Uᵢ) → (Uᵢ', Uᵣ) →                         -- context collection [Tool]
         classify(Uᵢ', dimension) → (Uᵣ', Uₑ, Uᵢ'', Uₙ) →     -- epistemic classification (core act); Uₙ = non-factual (classify summary routing)
         [if Uₑ_candidates ≠ ∅] EmpiricalObservation(Uₑ_candidates) → Uₑ  -- dynamic evidence gathering [Tool]
Phase 2: Qs(classify_result + Uₑ + Uᵢ''[max_gain], progress) → Stop → A           -- uncertainty surfacing [Tool]
Phase 3: A → integrate(A, X) → X'                               -- prospect update (sense)

── LOOP ──
After Phase 3: re-scan X' for remaining or newly emerged uncertainties.
New uncertainties accumulate into uncertainties (cumulative, never replace).
If Uᵢ' remains: return to Phase 1 (collect context for new uncertainties).
If remaining = ∅: proceed with execution.
User can exit at Phase 2 (early_exit).
Continue until: informed(X') OR user ESC.
Convergence evidence: At remaining = ∅, present transformation trace — for each u ∈ (Λ.context_resolved ∪ Λ.read_only_resolved ∪ Λ.empirically_observed ∪ Λ.user_responded), show (ContextInsufficient(u) → resolution(u)). Convergence is demonstrated, not asserted.

── CONVERGENCE ──
actionable(Λ) = uncertainties \ non_factual_detected       -- Fiber(Factual) uncertainties only
informed(X') = remaining = ∅                                -- non_factual_detected does not block convergence
progress(Λ) = 1 - |remaining| / |actionable(Λ)|            -- denominator excludes routed non-factual
narrowing(Q, A) = |remaining(after)| < |remaining(before)| ∨ context(remaining(after)) ⊃ context(remaining(before))
early_exit = user_declares_sufficient

── TOOL GROUNDING ──
-- Realization: gate → TextPresent+Stop; relay → TextPresent+Proceed
Phase 0 Scan    (sense)       → Internal analysis (no external tool)
Phase 1 Ctx     (observe)     → Read, Grep (stored knowledge extraction: codebase, memory, references); WebSearch (conditional: environmental dependency)
Phase 1 Classify (observe)    → Internal analysis (multi-dimension assessment); Read, Grep (stored knowledge cross-reference analysis)
Phase 1 Observe (transform)   → Write, Bash, Read (dynamic evidence gathering, Factual only); cleanup via Bash
Phase 2 Qs      (gate)        → present (mandatory: classify result + uncertainty surfacing; Esc key → loop termination at LOOP level, not an Answer)
Phase 3         (track)       → Internal state update
converge     (relay)       → TextPresent+Proceed (convergence evidence trace; proceed with informed execution)

── ELIDABLE CHECKPOINTS ──
-- Axis: relay/gated = interaction kind; always_gated/elidable = regret profile
Phase 2 Qs (transparent)   → always_gated (gated: user provides context judgment on insufficiency)

── MODE STATE ──
Λ = { phase: Phase, X: Prospect, uncertainties: Set(Uncertainty),
      dimensions_detected: Set(Dimension),                           -- π₁ image of classify_results
      classify_results: Map(Uncertainty, Σ(d: Dimension). Fiber(d)), -- fibration-typed classification
      context_resolved: Set(Uncertainty),  -- Uᵣ from TYPES
      read_only_resolved: Set(Uncertainty), -- Uᵣ' from TYPES
      empirically_observed: Set(Uncertainty), -- Uₑ from TYPES
      non_factual_detected: Set(Uncertainty), -- Uₙ from TYPES; Fiber(d) = Unit, classify summary routing
      user_responded: Set(Uncertainty),
      remaining: Set(Uncertainty), dismissed: Set(Uncertainty),
      history: List<(Uncertainty, A)>, observation_history: List<(ObservationSpec, Result, Evidence)>,
      observation_skips: List<(Uncertainty, EscapeCondition, String)>,  -- audit trail for Rule 20 escape hatches
      active: Bool,
      cause_tag: String }
-- Invariant: uncertainties = context_resolved ∪ read_only_resolved ∪ empirically_observed ∪ non_factual_detected ∪ user_responded ∪ remaining ∪ dismissed (pairwise disjoint)
-- Note: observation_skips is an audit log orthogonal to the partition — logged when EmpiricallyObservable is reclassified to UserDependent via Rule 20 (a)-(d)

Core Principle

Evidence over Inference over Detection: Aitesis operates on an epistemic hierarchy with two boundaries. The lower boundary (Inference > Detection): infer context insufficiency from requirements rather than detecting via fixed taxonomy — the protocol dynamically identifies what context is missing, not mechanically checking against a preset list. The upper boundary (Evidence > Inference): gather evidence through direct environmental observation rather than substituting inference from reasoning alone — when a fact is observable, observe it.

Within this hierarchy, the AI first collects contextual evidence via codebase exploration to enrich question quality, then classifies each uncertainty by dimension and verifiability — classification is the protocol's core epistemic act, not a routing sub-step. For factual uncertainties, the AI resolves read-only verifiable facts directly and empirically observes dynamically accessible ones with direct evidence before asking. For non-factual dimensions (coherence, relevance), the AI detects and shows routing targets in the classify summary. The purpose is multi-dimensional context sufficiency sensing — asking better questions for what requires human judgment, self-resolving what can be observed, and routing what belongs to other epistemic concerns.

Write is authorized for observation instrument setup (temporary test artifacts with mandatory cleanup). Rule 20 is the structural expression of the upper boundary — the adversarial guard against stopping at Inference when Evidence is achievable.

Distinction from Other Protocols

Protocol	Initiator	Deficit → Resolution	Focus
Prothesis	AI-guided	FrameworkAbsent → FramedInquiry	Perspective selection
Syneidesis	AI-guided	GapUnnoticed → AuditedDecision	Decision-point gaps
Hermeneia	Hybrid	IntentMisarticulated → ClarifiedIntent	Expression clarification
Telos	AI-guided	GoalIndeterminate → DefinedEndState	Goal co-construction
Horismos	AI-guided	BoundaryUndefined → DefinedBoundary	Epistemic boundary definition
Aitesis	AI-guided	ContextInsufficient → InformedExecution	Context sufficiency sensing
Analogia	AI-guided	MappingUncertain → ValidatedMapping	Abstract-concrete mapping validation
Prosoche	User-initiated	ExecutionBlind → SituatedExecution	Risk-assessed execution
Epharmoge	AI-guided	ApplicationDecontextualized → ContextualizedExecution	Post-execution applicability
Katalepsis	User-initiated	ResultUngrasped → VerifiedUnderstanding	Comprehension verification

Key differences:

• Syneidesis surfaces gaps at decision points for the user to judge (information flows AI→user) — Aitesis infers context the AI lacks before execution (information flows user→AI)
• Telos co-constructs goals when intent is indeterminate — Aitesis operates when goals exist but execution context is insufficient
• Hermeneia extracts intent the user already has (user signal) or detects expression ambiguity (AI-detected, requires confirmation) — Aitesis infers what context the system lacks

Heterocognitive distinction: Aitesis monitors the AI's own context sufficiency (heterocognitive — "do I have enough context to execute?"), while Syneidesis monitors the user's decision quality (metacognitive — "has the user considered all angles?"). The operational test: if the information gap would be filled by the user providing context, it's Aitesis; if it would be filled by the user reconsidering their decision, it's Syneidesis.

Factual vs evaluative: Aitesis uncertainties span multiple dimensions — factual (objectively correct answers discoverable from the environment), coherence (consistency among collected facts), and relevance (whether collected facts are relevant to the execution goal). Syneidesis gaps are evaluative — they require judgment about trade-offs and consequences. Phase 1 context collection exists because factual uncertainties may be partially resolved or enriched from the codebase. Coherence and relevance dimensions are detected but routed to downstream protocols. Evaluative gaps cannot be self-resolved.

Mode Activation

Activation

AI infers context insufficiency before execution OR user calls /inquire. Inference is silent (Phase 0); surfacing always requires user interaction via gate interaction (Phase 2).

Activation layers:

• Layer 1 (User-invocable): /inquire slash command or description-matching input. Always available.
• Layer 2 (AI-guided): Context insufficiency inferred before execution via in-protocol heuristics. Inference is silent (Phase 0).

Context insufficient = the prospect contains requirements not available in the current context and not trivially inferrable. Context insufficiency spans multiple dimensions: missing facts, incoherent facts, and facts not relevant to the prospect's goals. Sufficiency encompasses both executability (can the action proceed?) and analysis confidence (is the context adequate for reliable judgment?).

Gate predicate:

uncertain(sufficiency(X)) ≡ ∃ requirement(r, X) : ¬available(r, context) ∧ ¬trivially_inferrable(r)

Priority

<system-reminder> When Aitesis is active:

Supersedes: Direct execution patterns in loaded instructions (Context must be verified before any execution begins)

Retained: Safety boundaries, tool restrictions, user explicit instructions

Action: At Phase 2, present highest information-gain uncertainty candidate with classify results via gate interaction and yield turn. </system-reminder>

• Aitesis completes before execution proceeds
• Loaded instructions resume after context is resolved or dismissed

Protocol precedence: Activation order position 4/9 (graph.json is authoritative source for information flow). Concern cluster: Planning.

Advisory relationships: Receives from Horismos (advisory: BoundaryMap narrows context inference scope), Prothesis (advisory: perspective simulation provides context inference recommendations), Hermeneia (advisory: background gaps suggest context insufficiency), Syneidesis (suppression: same scope suppression). Provides to Prosoche (advisory: inferred context narrows execution risk assessment), Analogia (advisory: inferred context narrows mapping domain identification); Epharmoge (suppression: pre+post stacking prevention). Katalepsis is structurally last.

Trigger Signals

Heuristic signals for context insufficiency inference (not hard gates):

Signal	Inference
Novel domain	Knowledge area not previously addressed in session
Implicit requirements	Task carries unstated assumptions
Ambiguous scope	Multiple valid interpretations exist and AI cannot determine intended approach from available context
Environmental dependency	Relies on external state (configs, APIs, versions)

Cross-session enrichment: Domain knowledge accumulated through prior Reflexion cycles may narrow the Phase 0 uncertainty scan — known domain patterns reduce the scope of novel-domain signals. This is a heuristic input that may bias detection toward previously observed patterns; gate judgment remains with the user.

Revision threshold: When accumulated observation_skips entries across 3+ sessions cluster around a specific EscapeCondition with consistent rationale, the Verifiability classification boundary warrants revision — the escape is systematic, not exceptional. When accumulated Emergent dimension detections across 3+ sessions reveal a recurring non-factual uncertainty pattern, the Layer 1 dimension set warrants a new fiber in the fibration structure — promoted fibers default to Unit (detect-only) unless the pattern exhibits internal classification structure requiring a structured fiber type.

Skip:

• Execution context is fully specified in current message
• User explicitly says "just do it" or "proceed"
• Same (domain, description) pair was dismissed in current session (session immunity)
• Phase 1 context collection resolves all identified uncertainties
• Read-only / exploratory task — no prospect to verify

Mode Deactivation

Trigger	Effect
All uncertainties resolved (context, read-only, observed, or user)	Proceed with updated prospect
All remaining uncertainties dismissed	Proceed with original prospect + defaults
User Esc key	Return to normal operation

Uncertainty Identification

Uncertainties are identified dynamically per task — no fixed taxonomy. Each uncertainty is characterized by:

• domain: The knowledge area where context is missing (e.g., "deployment config", "API versioning", "user auth model")
• description: What specifically is missing or uncertain
• context: Evidence collected during Phase 1 that enriches question quality

Priority

Priority reflects information gain — how much resolving this uncertainty would narrow the remaining uncertainty space.

Level	Criterion	Action
Critical	Resolution maximally narrows remaining uncertainty space	Must resolve before execution
Significant	Resolution narrows uncertainty but alternatives partially compensate	Surface to user for context
Marginal	Reasonable default exists; resolution provides incremental improvement	Surface with pre-selected Dismiss option

Priority is relational, not intrinsic: the same uncertainty may be Critical in one context and Marginal in another, depending on what other uncertainties exist and what context is already available.

When multiple uncertainties are identified, surface in priority order (Critical → Significant → Marginal). Only one uncertainty surfaced per Phase 2 cycle.

Protocol

Phase 0: Context Sufficiency Gate (Silent)

Analyze prospect requirements against available context across multiple dimensions. This phase is silent — no user interaction.

1. Scan prospect X for required context: domain knowledge, environmental state, configuration details, user preferences, constraints
2. Check availability: For each requirement, assess whether it is available in conversation, files, or environment
3. Dimension assessment: Identify which dimensions are potentially insufficient — factual (missing information), coherence (conflicting information), relevance (information not relevant to goal)
4. If all requirements satisfied: present sufficiency finding per Rule 17 before proceeding (Aitesis not activated)
5. If uncertainties identified: record Uᵢ with domain, description — proceed to Phase 1

Scan scope: Current prospect context, conversation history, observable environment. Does NOT modify files or call external services.

Phase 1: Context Collection + Classification + Empirical Observation

Collect contextual evidence, classify each uncertainty by dimension and verifiability, and empirically observe accessible uncertainties with direct evidence.

Step 1 — Context collection: For each uncertainty in Uᵢ:

• Call Read/Grep to search for relevant information in codebase, configs, documentation
• If definitive answer found: mark as context-resolved (Uᵣ), integrate into execution context
• If partial evidence found: enrich uncertainty with collected evidence (Uᵢ'), retain for classification
• If conflicting evidence found: enrich uncertainty with conflicting findings (Uᵢ'), retain for classification
• If no evidence found: retain in Uᵢ' with empty context

Step 2 — Epistemic classification (core act): For each remaining uncertainty in Uᵢ':

• Dimension assessment (Layer 1): Is this factual, coherence, or relevance?
  • Factual: a fact is missing from context and required for execution
  • Coherence: collected facts are mutually inconsistent
  • Relevance: collected facts are not relevant to the execution goal
• Verifiability assessment (Layer 2, Factual dimension only — Observability sub-modes guide classification):
  • ReadOnlyVerifiable: fact exists in environment (StaticObservation or BeliefVerification) and is observable with current tools → resolve directly via extended context lookup
  • EmpiricallyObservable: fact requires DynamicObservation — does not exist statically but is observable through non-destructive execution, reversible, and bounded (< 30s) → empirical observation
  • UserDependent: neither read-only verifiable nor empirically observable → Phase 2 directly
• Non-factual dimensions: Coherence and Relevance → detect and record as Uₙ (non_factual_detected); shown with routing target in classify summary, not Phase 2 question
  • Coherence → /ground (structural mapping may reveal inconsistency source)
  • Relevance → deficit-matched: GoalIndeterminate→/goal, GapUnnoticed→/gap, BoundaryUndefined→/bound, IntentMisarticulated→/clarify
  • Emergent(_) → match observed deficit condition against candidate protocol deficit conditions
• Store all results in Λ.classify_results

Step 3 — Read-only verification: For ReadOnlyVerifiable uncertainties:

• Targeted context lookup via Read/Grep — classification narrows search scope to specific files/locations that Step 1's broad sweep did not cover (e.g., spec files, config schemas identified by classify)
• Resolved: mark as Uᵣ' (read_only_resolved), skip Phase 2

Step 4 — Empirical observation: For EmpiricallyObservable uncertainties:

• Construct ObservationSpec: { setup, execute, observe, cleanup }
• Execute observation lifecycle (setup → execute → observe → cleanup → record)
• Observation evidence attached to Uₑ → proceeds to Phase 2 with evidence
• Escape hatch: If Rule 20 (a)-(d) applies, reclassify as UserDependent and log (uncertainty, condition, rationale) to Λ.observation_skips — the skip rationale must be specific enough to audit (e.g., "StructuralUncertainty: naming preference, no observable state differentiates options")

If all uncertainties context-resolved or read-only-resolved (no observed or user-dependent remaining): proceed with execution (no user interruption). If observed or user-dependent uncertainties remain: proceed to Phase 2.

Web context (conditional): When uncertainty carries an environmental dependency signal (external API versions, library maintenance status, breaking changes) and the information is not available in the codebase, extend context collection to web search. Web evidence is tagged with source: "web:{url}" for traceability.

Scope restriction:

• Context collection: Read-only investigation (Read, Grep, WebSearch). — core preserved
• Read-only verification: Extended context lookup for verifiable facts (Read, Grep). — resolves directly
• Empirical observation: Non-destructive observation via Bash execution, with optional Write for observation instrument setup (temp test artifacts). Observation artifacts must be created in temp locations and cleaned up after observation. Observation must not modify existing project files. Observation results are evidence for Phase 2, not resolution — evidence gathering, not replacement.

Observation design constraints:

1. Minimal: Create the smallest possible observation instrument
2. Reversible: All observation artifacts must be cleaned up after observation
3. Sandboxed: Observation must not modify existing project files
4. Transparent: Log observation lifecycle in Λ.observation_history
5. Bounded: 30-second timeout → fall back to user inquiry
6. Risk-aware: Elevated-risk observation → reclassify as UserDependent

Phase 2: Uncertainty Surfacing

Present the highest-priority remaining uncertainty with classify results via gate interaction.

Classification transparency (Always show): Phase 2 always includes classify results for remaining uncertainties. This is informational — no approval required. Users can override classification by stating objection. When only one uncertainty remains, inline the classification with the uncertainty description rather than showing a separate summary block.

Surfacing format:

Present the classification results, uncertainty description, and evidence as text output:

• Classification summary:
  • U1: Factual/ReadOnly (basis: evidence summary)
  • U2: Factual/EmpiricallyObservable (basis: evidence summary)
  • U2b: Factual/EmpiricallyObservable → UserDependent (escape: [condition] — "[rationale]")
  • U3: Coherence (basis: evidence summary) → /ground
  • U4: Relevance (basis: evidence summary) → /goal
  • Any classification to revise?
• [Specific uncertainty description — highest priority]
• Evidence: [Evidence collected during context collection and observation, if any]
• Progress: [N resolved / M actionable uncertainties] (excludes non-factual routed)

Then present:

How would you like to resolve this uncertainty?

Options:
1. **[Provide X]** — [what this context enables]
2. **[Point me to...]** — tell me where to find this information
3. **Dismiss** — proceed with [stated default/assumption]

Design principles:

• Classification transparent: Show classify results (dimension + verifiability) for all uncertainties — "visible by default, ask only on exception"
• Context collection transparent: Show what evidence was collected and what remains uncertain
• Progress visible: Display resolution progress across all identified uncertainties
• Actionable options: Each option leads to a concrete next step
• Dismiss with default: Always state what assumption will be used if dismissed

Selection criterion: Choose the uncertainty whose resolution would maximally narrow the remaining uncertainty space (information gain). When priority is equal, prefer the uncertainty with richer collected context (more evidence to present).

Phase 3: Prospect Update

After user response:

1. Provide(context): Integrate user-provided context into prospect X'
2. Point(location): Record location, resolve via next Phase 1 iteration
3. Dismiss: Mark uncertainty as dismissed, note default assumption used

After integration:

• Re-scan X' for remaining or newly emerged uncertainties
• If uncertainties remain: return to Phase 1 (collect context for new uncertainties first)
• If all resolved/dismissed: proceed with execution
• Log (Uncertainty, A) to history

Intensity

Level	When	Format
Light	Marginal priority uncertainties only	Gate interaction with Dismiss as default option
Medium	Significant priority uncertainties, context collection partially resolved	Structured gate interaction with progress
Heavy	Critical priority, multiple unresolved uncertainties	Detailed evidence + collection results + classify results + resolution paths

UX Safeguards

Rule	Structure	Effect
Gate specificity	activate(Aitesis) only if ∃ requirement(r) : ¬available(r) ∧ ¬trivially_inferrable(r)	Prevents false activation on clear tasks
Context collection first	Phase 1 before Phase 2	Enriches question quality before asking
Uncertainty cap	One uncertainty per Phase 2 cycle, priority order	Prevents question overload
Session immunity	Dismissed (domain, description) → skip for session	Respects user's dismissal
Progress visibility	[N resolved / M actionable] in Phase 2	User sees progress on actionable (Factual) uncertainties
Narrowing signal	Signal when narrowing(Q, A) shows diminishing returns	User can exit when remaining uncertainties are marginal
Early exit	User can declare sufficient at any Phase 2	Full control over inquiry depth
Cross-protocol fatigue	Syneidesis triggered → suppress Aitesis for same task scope	Prevents protocol stacking (asymmetric: Aitesis context uncertainties ≠ Syneidesis decision gaps, so reverse suppression not needed)
Classify transparency	Always show classify results (dimension + verifiability) in Phase 2 surfacing format	User sees AI's reasoning and resolution path per uncertainty
Routing transparency	Uₙ items show → /protocol in Phase 2 classify summary	User sees routing destination at detection time
Observation transparency	Log observation lifecycle in Λ.observation_history	User can audit what was tested
Skip transparency	Log escape hatch usage in Λ.observation_skips with condition + rationale	User can audit why observation was skipped
Observation cleanup	All test artifacts removed after observation	No residual files
Observation timeout	30s limit → fall back to user inquiry	Prevents hanging
Observation risk gate	Elevated-risk observation → reclassify as UserDependent	Safety preserved

Rules

1. AI-guided, user-resolved: AI infers context insufficiency; resolution requires user choice via gate interaction (Phase 2)
2. Recognition over Recall: Present structured options via gate interaction and yield turn — structured content must reach the user with response opportunity. Bypassing the gate (presenting content without yielding turn) = protocol violation
3. Context collection first, epistemic classification second: Before asking the user, (a) collect contextual evidence through Read/Grep, (b) classify uncertainties by dimension (Factual/Coherence/Relevance) and verifiability, (c) show classification transparently in Phase 2, (d) for Factual/ReadOnly: resolve directly, (e) for Factual/EmpiricallyObservable: run empirical observation to attach evidence, (f) for Coherence/Relevance: detect and show routing target in classify summary
4. Evidence over Inference over Detection: When context is insufficient, infer the highest-gain question rather than detect via fixed checklist (lower boundary). When a factual uncertainty is empirically observable, observe directly rather than infer from reasoning alone (upper boundary — Rule 20 is the structural guard)
5. Open scan: No fixed uncertainty taxonomy — identify uncertainties dynamically based on prospect requirements
6. Evidence-grounded: Every surfaced uncertainty must cite specific observable evidence or collection results, not speculation
7. One at a time: Surface one uncertainty per Phase 2 cycle; do not bundle multiple uncertainties
8. Dismiss respected: User dismissal is final for that uncertainty domain in the current session
9. Convergence persistence: Mode active until all identified uncertainties are resolved or dismissed
10. Progress visibility: Every Phase 2 surfacing includes progress indicator [N resolved / M actionable] — actionable excludes non_factual_detected (routed, not resolved)
11. Early exit honored: When user declares context sufficient, accept immediately regardless of remaining uncertainties
12. Cross-protocol awareness: Defer to Syneidesis when gap surfacing is already active for the same task scope
13. Evidence before inquiry: User inquiry is for judgment — not for facts the AI can discover
14. Always show: User can override classification without explicit approval step. Visible by default, ask only on exception
15. Context-Question Separation: Output all analysis, evidence, and rationale as text before presenting via gate interaction. The question contains only the essential question; options contain only option-specific differential implications. Embedding context in question fields = protocol violation
16. No premature convergence: Do not declare remaining = ∅ without presenting convergence evidence trace. "All uncertainties resolved" as assertion without per-uncertainty evidence = protocol violation
17. No silent sufficiency assumption: If Phase 0 scan detects no uncertainties, present this finding with reasoning to user for confirmation before proceeding — do not silently declare context sufficient
18. Option-set relay test: Before presenting gate options, apply the relay test to the option set: if AI analysis converges to a single dominant option (option-level entropy→0), the interaction is relay — present the finding directly instead of wrapping it in false options. Each gate option must be genuinely viable under different user value weightings
19. Gate integrity: Do not inject options not in the definition, delete defined options, or substitute defined options with different ones (gate mutation). Type-preserving materialization — specializing a generic option into a concrete term while preserving the TYPES coproduct structure — is permitted and distinct from mutation
20. No observation avoidance: When a Factual uncertainty is classifiable as EmpiricallyObservable (observable through bounded, non-destructive Bash execution), classifying it as UserDependent to avoid empirical observation = protocol violation. The AI must attempt observation before falling back to user inquiry. Reclassification to UserDependent is legitimate only when: (a) observation requires persistent environment mutation beyond instrument setup, (b) execution exceeds 30s bound, (c) risk gate triggers (elevated-risk observation), or (d) the uncertainty is structural rather than empirical — no observable state change would differentiate between possible answers (e.g., design trade-offs, naming choices, preference questions)