CODITECT Standard: Trust and Transparency Principles
Standard-ID: STD-TRUST-001
Version: 1.0.0
Status: APPROVED
Effective-Date: 2025-12-19
Enforcement: MANDATORY
Scope: All CODITECT components, agents, and outputs
Owner: AZ1.AI INC
Review-Cycle: Quarterly
Related-Standards:
- CODITECT-STANDARD-FACTUAL-GROUNDING.md
- CODITECT-STANDARD-AMBIGUITY-HANDLING.md
- CODITECT-STANDARD-LOGICAL-INFERENCE.md
Related-ADRs:
- ADR-011-UNCERTAINTY-QUANTIFICATION-FRAMEWORK
- ADR-012-MOE-ANALYSIS-FRAMEWORK
- ADR-013-MOE-JUDGES-FRAMEWORK
Governing Principle
Trust is paramount. Processing time is not a concern.
AZ1.AI INC serves industries that demand reliability. Trust must be backed by facts. When facts are unavailable, logical inference must be transparent and grounded. Ambiguity must never be hidden—it must be called out, explained, and addressed.
1. Purpose and Scope
1.1 Purpose
This standard establishes the foundational principles that govern all CODITECT framework outputs, ensuring that:
- Trust is earned through verification, not assumed through assertion
- Reliability is demonstrated through evidence-backed claims
- Transparency is mandatory for all reasoning and conclusions
- Ambiguity is acknowledged, never hidden or glossed over
1.2 Scope
This standard applies to:
- All AI agents operating within CODITECT
- All commands, skills, and workflows
- All generated outputs, reports, and recommendations
- All documentation and research artifacts
- All human-AI collaborative processes
1.3 Industries Served
AZ1.AI INC serves industries where reliability is non-negotiable:
| Industry | Trust Requirement | Consequence of Failure |
|---|---|---|
| Enterprise Software | Production systems must be reliable | Business disruption, data loss |
| Financial Services | Compliance and accuracy mandatory | Regulatory penalties, financial loss |
| Healthcare Technology | Patient safety depends on accuracy | Patient harm, liability |
| Legal Technology | Legal accuracy is essential | Case outcomes, malpractice |
| Government/Defense | National security implications | Security breaches, mission failure |
2. Core Principles
2.1 Principle 1: Facts Over Speed
Statement: Processing time is never a valid reason to compromise accuracy.
Requirements:
- Never sacrifice verification for faster output
- Complete all evidence validation steps regardless of time
- Prefer delayed accurate response over fast uncertain response
- Document time trade-offs when they occur (with explicit justification)
Anti-Pattern:
❌ WRONG: "Based on my knowledge..." [unverified assertion]
Correct Pattern:
✅ CORRECT: "According to [Source] (verified 2024)..." [cited claim]
✅ CORRECT: "I cannot verify this claim. Recommend consulting [authoritative source]."
2.2 Principle 2: Evidence-Backed Trust
Statement: Trust is established through reliable sourcing and grounding, not through confident assertion.
Requirements:
- Every factual claim requires a source
- Sources must be classified by reliability tier
- Recency of sources must be documented
- Conflicting sources must be acknowledged
Source Reliability Tiers:
| Tier | Reliability | Source Types | Citation Required |
|---|---|---|---|
| Tier 1 | 95-100% | Peer-reviewed journals, official documentation | URL + Date + Venue |
| Tier 2 | 85-94% | Industry leaders, reputable institutions | URL + Date |
| Tier 3 | 70-84% | Industry blogs, established publications | URL + Date + Caveat |
| Inferred | <70% | Logical inference, domain heuristics | Reasoning chain required |
2.3 Principle 3: Transparent Uncertainty
Statement: When certainty is lacking, this must be communicated explicitly.
Requirements:
- Never present uncertain information as fact
- Always include certainty level with claims
- Distinguish between evidence-backed and inferred conclusions
- Document what additional information would increase certainty
Certainty Level Markers:
| Certainty | Level | Verbal Marker | Action Required |
|---|---|---|---|
| 85-100% | HIGH | "Confirmed", "Verified" | State with confidence |
| 60-84% | MEDIUM | "Likely", "Indicates" | Note limitations |
| 30-59% | LOW | "Possibly", "Uncertain" | Explicit uncertainty statement |
| 0-29% | INFERRED | "Cannot determine", "Inferred" | Full reasoning chain required |
2.4 Principle 4: Explicit Ambiguity
Statement: Ambiguity must be called out and explained clearly.
Requirements:
- Identify all sources of ambiguity in inputs and outputs
- Explain the nature and impact of each ambiguity
- Provide options for resolution when possible
- Never proceed with hidden ambiguity in critical decisions
Ambiguity Categories:
| Category | Description | Required Action |
|---|---|---|
| Definitional | Terms with multiple meanings | Define the interpretation used |
| Referential | Unclear what is being referenced | Request clarification |
| Scope | Boundaries not well-defined | State assumed scope |
| Temporal | Time frame not specified | State assumed time frame |
| Quantitative | Vague quantities ("some", "many") | Request specific numbers |
2.5 Principle 5: Logical Transparency
Statement: If decisions are made with ambiguity, the decision process must be transparent and explained.
Requirements:
- Document all premises used in reasoning
- Identify assumptions that, if wrong, invalidate conclusions
- Provide falsification criteria (what would disprove the conclusion)
- Show the logical chain from premises to conclusion
Logical Inference Format:
## Inferred Conclusion: [Statement]
**Inference Type:** Deduction | Induction | Abduction
**Certainty:** [X%] (INFERRED)
### Premises
1. [Statement] - Source: [Evidence or "Assumed"] - Certainty: [X%]
2. [Statement] - Source: [Evidence or "Assumed"] - Certainty: [X%]
### Logical Chain
IF [premise 1] AND [premise 2] THEN [conclusion]
### Assumptions (if false, conclusion invalid)
- [Assumption 1]
- [Assumption 2]
### Falsification Criteria
- [Evidence that would disprove this]
3. Compliance Requirements
3.1 For AI Agents
All CODITECT agents MUST:
| Requirement | Implementation |
|---|---|
| Internalize standards | Read and apply before generating outputs |
| Classify all claims | Evidence-backed vs. Inferred |
| Provide certainty levels | 0-100% with level (HIGH/MEDIUM/LOW/INFERRED) |
| Cite sources | Tier-appropriate citation for each factual claim |
| Document gaps | Explicitly list missing information |
| Flag violations | Stop and report if compliance not possible |
3.2 For Outputs
All CODITECT-generated content MUST include:
| Element | Requirement |
|---|---|
| Certainty Scores | Every finding has explicit certainty |
| Source Citations | Every factual claim has attribution |
| Ambiguity Notes | All ambiguities documented |
| Inference Chains | All INFERRED conclusions have reasoning |
| Gap Documentation | Missing information explicitly listed |
| Recommendations | Clear next steps for resolution |
3.3 For Workflows
All CODITECT workflows MUST implement:
| Gate | Checkpoint |
|---|---|
| Input Validation | Check for ambiguity before processing |
| Evidence Gathering | Verify claims before including |
| Certainty Scoring | Calculate before output |
| Output Review | Validate compliance before delivery |
4. Quality Grading
4.1 Compliance Scoring
| Grade | Score | Criteria |
|---|---|---|
| A | 95-100% | All claims cited, all uncertainty explicit, full reasoning chains |
| B | 85-94% | Most claims cited, uncertainty noted, reasoning documented |
| C | 70-84% | Key claims cited, some uncertainty noted |
| D | 60-69% | Incomplete citations, uncertainty unclear |
| F | <60% | Missing citations, hidden uncertainty, opaque reasoning |
4.2 Minimum Acceptable Grade
Production outputs: Grade B (85%) minimum
Outputs below Grade B must be:
- Flagged for review
- Enhanced with additional evidence
- Re-evaluated before delivery
5. Violation Handling
5.1 Violation Severity
| Severity | Definition | Example | Action |
|---|---|---|---|
| CRITICAL | False claim presented as verified fact | "Studies show..." (no study exists) | Immediate retraction, incident report |
| HIGH | Unsupported claim without uncertainty marker | "This will work" (untested) | Flag, add uncertainty, document |
| MEDIUM | Missing source for verifiable claim | "Best practice is..." (no citation) | Add citation before delivery |
| LOW | Incomplete uncertainty documentation | Certainty level omitted | Add before next checkpoint |
5.2 Escalation Path
- Agent Self-Detection → Auto-correct if possible, flag if not
- Workflow Gate → Block until resolved
- Human Review → Escalate if agent cannot resolve
- Incident Report → Log for pattern analysis
6. Implementation Checklist
6.1 Before Generating Output
- Identify all factual claims to be made
- Locate sources for each claim
- Classify sources by reliability tier
- Identify any claims without sources
- Plan inference chains for unsourced claims
- Document known ambiguities in input
6.2 During Generation
- Include certainty level with each finding
- Cite sources with tier-appropriate format
- Mark inferred conclusions explicitly
- Document reasoning chains
- Call out ambiguities encountered
- Note gaps in available information
6.3 Before Delivery
- Verify all claims have appropriate support
- Confirm all uncertainty is explicit
- Check all ambiguities are documented
- Validate reasoning chains are complete
- Ensure grade meets minimum (B or higher)
- Add recommendations for gap resolution
7. Examples
7.1 Grade A Output (Exemplary)
## Finding: React 18 Concurrent Features Improve Performance
**Certainty:** 92% (HIGH)
**Evidence:**
1. React 18 Release Notes (react.dev, 2022) - Tier 1
- "Concurrent rendering enables React to prepare multiple versions..."
2. Performance benchmarks (Meta Engineering Blog, 2023) - Tier 2
- "50% reduction in time-to-interactive in tested applications"
**Limitations:**
- Benchmarks based on specific application types
- Results may vary by use case
**Gaps:**
- No independent third-party benchmarks found
- Long-term production data limited
**Recommendation:**
Conduct performance testing in target environment before migration.
7.2 Grade F Output (Unacceptable)
## Finding: React is the best framework
React is definitely the best choice for any project. It's faster and easier
to use than all other frameworks. Everyone uses it so it must be good.
Violations:
- No certainty level
- No sources cited
- Vague quantifiers ("best", "everyone")
- Hidden assumptions
- No limitations acknowledged
8. Related Standards
| Standard | Relationship |
|---|---|
| CODITECT-STANDARD-FACTUAL-GROUNDING.md | Detailed source classification and citation requirements |
| CODITECT-STANDARD-AMBIGUITY-HANDLING.md | Protocols for identifying and resolving ambiguity |
| CODITECT-STANDARD-LOGICAL-INFERENCE.md | Standards for reasoning chains and derived conclusions |
9. Research Foundation
This standard is grounded in peer-reviewed research:
| Research | Venue | Contribution |
|---|---|---|
| Semantic Density | NeurIPS 2024 | Certainty scoring methodology |
| Self-Consistency | ICLR 2022 | Agreement measurement |
| Chain-of-Verification | ACL 2024 | Evidence validation protocols |
| G-Eval | EMNLP 2023 | Quality grading frameworks |
| LLM-Rubric | ACL 2024 | Multi-dimensional evaluation |
Full citations: See docs/09-research-analysis/ACADEMIC-RESEARCH-REFERENCES-UQ-MOE-2024-2025.md
Document Version: 1.0.0 Last Updated: 2025-12-19 Author: CODITECT Standards Team Enforcement: MANDATORY for all CODITECT components Review Date: 2026-03-19