Agent Skills: Aitesis Protocol

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, resolves memory-internal contradictions through evidence, routes cross-domain concerns to other epistemic protocols, 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) → [if off-diagonal] Qc → (Uᵣ', Uₑ, Uᵢ'', Uₙ) →
  Q(classify_result + Uₑ + Uᵢ'', priority) → A → X' → (loop until informed)
-- Uₙ (non-actionable: CrossDomain coherence + detect-only dimensions): shown in classify summary with routing target
-- Uᵢ'' (factual/user-dependent or coherence/MemoryInternal/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)
  → reclassify(MemoryInternal → Factual)  -- Coherence/MemoryInternal enters Factual resolution path
  → 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}
Ac         = User coherence classification ∈ CoherenceType     -- Phase 1 Qc gate answer type
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 and Coherence/MemoryInternal fibers — fibration structure)
Verifiability ∈ {ReadOnlyVerifiable, EmpiricallyObservable, UserDependent}
CoherenceType ∈ {MemoryInternal, CrossDomain}
               -- 2D: Scope(Same/Cross) × Resolution(Evidence/Structure); off-diagonal → Gate
Scope      ∈ {Same, Cross}
Resolution ∈ {Evidence, Structure}
off_diagonal(s, r) = ¬((s = Same ∧ r = Evidence) ∨ (s = Cross ∧ r = Structure))
classify   = Uᵢ' → Σ(d: Dimension). Fiber(d)
             where Fiber(Factual)       = Verifiability
                   Fiber(Coherence)     = CoherenceType
                   Fiber(Relevance)     = Unit    -- detect only
                   Fiber(Emergent(_))   = Unit    -- detect only (default; refinable per discovered dimension)
             -- 2-layer model = Grothendieck fibration: Layer 2 exists over Factual fiber;
             -- Coherence fiber classifies into CoherenceType, where MemoryInternal instances enter the Factual resolution path
             -- CrossDomain/Relevance/Emergent → 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 [includes reclassified Coherence/MemoryInternal]; Phase 2 question
Uₙ         = Non-actionable detected uncertainties  -- Fiber(Coherence) = CrossDomain or Fiber(d) = Unit; shown in classify summary with routing target
Action     = Tool call sequence (Write, Bash)
EscapeCondition ∈ {EnvironmentMutation, BoundExceeded, RiskElevated}
                    -- maps to Rule 20 (a)-(c) 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-actionable (classify summary routing)
         [if off-diagonal(scope, resolution)] Qc(scope_assessment, resolution_assessment) → Stop → user_classification  -- Coherence 2D gate [Tool]
         -- evaluation order: Qc resolves before Uₑ_candidates computation; reclassified MemoryInternal/EmpiricallyObservable enters Uₑ_candidates
         [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) + Fiber(Coherence)=MemoryInternal uncertainties
informed(X') = remaining = ∅                                -- non_factual_detected does not block convergence
progress(Λ) = 1 - |remaining| / |actionable(Λ)|            -- denominator excludes non-actionable (CrossDomain + detect-only dimensions)
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 Qc      (gate)        → present (conditional: Coherence 2D off-diagonal gate; fires only when scope ≠ resolution assessment)
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 1 Qc (coherence 2D)  → conditional: fires only when scope ≠ resolution assessment
                              always_gated when fired (gated: user classifies coherence type as MemoryInternal or CrossDomain)
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(Coherence) = CrossDomain or 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)-(c)

── COMPOSITION ──
*: product — (D₁ × D₂) → (R₁ × R₂). graph.json edges preserved. Dimension resolution emergent via session context.

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. Corollary: partial evidence covering a subset of the claim scope is inference for the uncovered portion — evidence-claim alignment must be verified before treating evidence as resolution.

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 coherence, the AI classifies by scope and resolution method — memory-internal contradictions are resolved through factual reclassification, while cross-domain contradictions are routed to downstream protocols via deficit-matched routing. For 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, resolving memory-internal contradictions through evidence, and routing cross-domain concerns to other epistemic protocols.

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/CrossDomain and relevance dimensions are detected but routed to downstream protocols. Coherence/MemoryInternal contradictions are evidence-resolvable and follow the factual resolution path. 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

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) | | Prior-decision implication | Prospect references or rests on a decision made in an earlier session (rationale, commitment, convention) not present in current conversation context |

Default-scan trigger (resolves the bootstrapping asymmetry of the Prior-decision implication signal): When the prospect scope touches architecture decisions, API or protocol design, persisted state schemas, or user-facing behavior commitments, the Prior-decision signal fires by default — Phase 1 Ctx begins with a bounded scan over persistent memory (MEMORY.md, prior-decision logs in .insights/) regardless of explicit user mention. Without this default, detection would require the AI to already suspect prior involvement, which is the condition the signal is meant to surface. The default-scan is scope-triggered (not blanket), bounded to Phase 1 Ctx (no gate), and subject to the memory staleness rule in Phase 1 Step 1 (verify against current state before treating as resolved).

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
• Cross-session state consultation is in-scope: When a prospect implicates prior decisions, rationale, or committed state from earlier sessions, Read/Grep over persistent memory files (MEMORY.md, .insights/, prior decision logs) is part of Phase 1 Ctx — not a separate protocol. Consulting past state at the current decision moment is context collection, and evidence from memory is tagged source: "memory:{path}" for traceability. Memory staleness guard: memory evidence is temporally decoupled from the current environment — the referenced decision may have been superseded, the referenced file may have been renamed, the convention may have been revised. Memory evidence is therefore NOT eligible for the Factual/ReadOnly direct-resolve path. Classify memory-sourced evidence as Factual/EmpiricallyObservable (verify against current codebase/environment state before treating as resolved) or escalate to user confirmation via the classify summary with explicit staleness:unverified tag. Codebase evidence remains eligible for ReadOnly direct resolution because it is inherently current. This is discovery of what the AI already committed to, not pattern discovery across sessions.

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 — Observability sub-modes guide classification):
  • ReadOnlyVerifiable: fact exists in environment (StaticObservation or BeliefVerification) and is observable with current tools, AND evidence scope ⊇ claim scope → resolve directly via extended context lookup
    • When evidence scope ⊊ claim scope: split — covered portion proceeds to Step 3 (ReadOnly resolution), uncovered portion is classified separately and enters the appropriate verifiability path
  • 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
• Coherence classification (Layer 2, 2D model: Scope × Resolution):
  • Pre-filter: cross-scope + rule-resolvable (existing scope hierarchy, established precedence) → coexistence (exit Coherence; not a contradiction)
  • Same scope + evidence-resolvable → MemoryInternal → factual reclassification (ReadOnlyVerifiable / EmpiricallyObservable / UserDependent) → follows Factual resolution path (Step 3, Step 4, or Phase 2)
  • Cross scope + structure-requiring → CrossDomain → deficit-matched routing: MappingUncertain→/ground, BoundaryUndefined→/bound, GoalIndeterminate→/goal, FrameworkAbsent→/frame, GapUnnoticed→/gap, IntentMisarticulated→/clarify
  • Off-diagonal (Scope ≠ Resolution): present both assessments with evidence via conditional gate; user classifies as MemoryInternal or CrossDomain
    • (Same, Structure): same-scope contradiction where factual verification is insufficient — resolution requires understanding structural relationships within the scope
    • (Cross, Evidence): cross-scope contradiction where evidence comparison can determine which scope's claim is current — despite scope difference, factual verification suffices
  • MemoryInternal → actionable (proceeds to resolution); CrossDomain → record as Uₙ (non_factual_detected) with deficit-matched routing target
• Other non-actionable dimensions: Relevance and Emergent → detect and record as Uₙ (non_factual_detected); shown with routing target in classify summary, not Phase 2 question
  • 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)
• Scope re-verification: if targeted lookup reveals evidence scope ⊊ claim scope not detected at Step 2 (subtle gap), apply the same split — covered portion resolved, uncovered portion reclassified separately
• Resolved: mark as Uᵣ' (read_only_resolved), skip Phase 2

Step 4 — Empirical observation: For EmpiricallyObservable uncertainties:

• Pre-observation escape check: If Rule 20 (a)-(c) applies BEFORE execution (environmental constraint prevents bounded, non-destructive observation), reclassify as UserDependent and log (uncertainty, condition, rationale) to Λ.observation_skips — the skip rationale must cite the triggering escape condition and the empirical basis that established it. Skip remaining Step 4 execution for this uncertainty.
• Execution (if no pre-observation escape): Construct ObservationSpec: { setup, execute, observe, cleanup }; execute lifecycle (setup → execute → observe → cleanup → record)
• Outcome — differentiating evidence found: Attach evidence to Uₑ → proceeds to Phase 2 with positive evidence
• Outcome — no differentiating signal: Observation executed but no state change distinguishes candidate answers. Attach the null-signal finding as negative evidence to Uₑ → proceeds to Phase 2 with observation record. This is NOT an escape or reclassification; the observation output itself is the evidence. Phase 2 surfaces the observed null-signal to the user for judgment, not AI-internal categorical dismissal.

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]")
  • U2c: Factual/partial (evidence scope ⊊ claim scope: covers [scope A], claim requires [scope B] — uncovered portion classified separately)
  • U3a: Coherence/MemoryInternal (basis: evidence summary — same-scope, evidence-resolvable) → factual reclassification
  • U3b: Coherence/CrossDomain (basis: evidence summary — structure-requiring) → deficit-matched routing
  • 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-actionable 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 + Coherence/MemoryInternal) 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 over codebase and persistent memory — persistent memory here means prior-committed decisions and rationale (architecture choices already merged, conventions documented in rules, deliberate trade-offs recorded in memory files), NOT inductive patterns loaded per-session (pattern loading carries a pollution risk per architectural-principles.md Cross-Session Knowledge Composition and is outside Phase 1 Ctx scope); memory-sourced evidence is tagged memory:{path} and subject to the staleness guard in Phase 1 Step 1 (NOT eligible for Factual/ReadOnly direct-resolve; verify against current state or escalate to user confirmation), (b) classify uncertainties by dimension (Factual/Coherence/Relevance) and verifiability, (c) show classification transparently in Phase 2, (d) for Factual/ReadOnly: resolve directly (codebase evidence only), (e) for Factual/EmpiricallyObservable: run empirical observation to attach evidence (memory evidence lands here when staleness is unverified), (f) for Coherence/MemoryInternal: resolve through factual reclassification (ReadOnlyVerifiable / EmpiricallyObservable / UserDependent), (g) for Coherence/CrossDomain and 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). Evidence-claim alignment: partial evidence covering a subset of the claim scope is inference for the uncovered portion, not evidence — verify evidence scope ⊇ claim scope before treating as resolved, and classify the uncovered portion separately
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 (non-actionable: CrossDomain coherence + detect-only dimensions, 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 execute observation. Observation has exactly two outcomes, both of which proceed to Phase 2 via Uₑ: (i) differentiating evidence found → attach evidence to Uₑ → Phase 2; (ii) no differentiating signal found → the null-signal finding is itself an observation output — attach as negative evidence to Uₑ → Phase 2. Post-observation null-signal findings are NOT reclassifications to UserDependent; they are Uₑ items with observation-grounded negative evidence. Reclassification to UserDependent is legitimate only under empirically verifiable escape conditions evaluated BEFORE observation execution: (a) observation requires persistent environment mutation beyond instrument setup, (b) execution exceeds 30s bound, (c) risk gate triggers (elevated-risk observation). Pre-observation categorical classification reinstates the inference-before-observation shortcut this rule guards against — classification must arise from observation output, not substitute for it.
21. No pre-filter rationalization: Pre-filter (coexistence exit) applies only when an explicit, named scope hierarchy rule or documented precedence ordering resolves the apparent contradiction without epistemic protocol intervention. Classifying a genuine cross-domain structural contradiction as "rule-resolvable" to avoid routing = pre-filter misuse (analogous to Rule 20's observation avoidance guard)