Architectural Principles
Correspondence Architecture rests on ten principles. These principles are not software requirements. They are theoretical claims about what organizational architecture must preserve in AI-native conditions.
3.1 Intent Primacy
Organizational architecture originates from shared intent. Intent provides the primary orientation against which decisions, work, evidence, outcomes, learning, and governance become meaningful. Without represented intent, an organization may coordinate action without knowing whether that action still expresses what the organization seeks to become or preserve.
3.2 Representation Primacy
Organizations exist through representations rather than structures alone. Structures, processes, systems, roles, and routines matter because they create, transform, interpret, and govern organizational representations. Architecture must therefore begin with represented meaning, not only with formal structure.
3.3 Canonical Identity
Every organizational object requires a stable semantic identity independent of runtime implementation. A decision is not identical with a document, a database row, an issue ticket, or a graph node. These may represent or project the decision, but they do not define its organizational meaning.
3.4 Representation Network
Representations form interconnected semantic networks. Their meaning depends not only on content but on relationships to other representations. A decision is meaningful because it relates to intent, evidence, alternatives, governance, work, outcomes, and learning.
3.5 Correspondence Preservation
Relationships among representations must remain coherent over time. Correspondence may be weakened by drift, contradiction, unsupported claims, stale relationships, orphaned artifacts, or unincorporated learning. Architecture must make such degradation visible and governable.
3.6 Traceability
Every architectural transformation should preserve semantic traceability (Cleland-Huang et al., 2012; Gotel and Finkelstein, 1994). When representations are summarized, translated, approved, implemented, evaluated, or revised, the organization should retain enough provenance and relationship context to understand what changed and why.
3.7 Organizational Memory
Memory is an architectural capability, not merely stored information (Alavi and Leidner, 2001; Walsh and Ungson, 1991). It preserves representations, relationships, provenance, evidence, lifecycle state, and learning so that the organization can reuse prior cognition rather than merely retrieve old artifacts.
3.8 Evidence-Based Evolution
Architectural change should be justified by observable evidence. Evidence does not eliminate judgment, but it anchors correspondence claims in reviewable support. This is especially important when AI systems generate interpretations or recommendations.
3.9 AI Participation
AI operates as an architectural participant within organizational cognition, not merely as a tool and not as an authority (Amershi et al., 2019; Floridi and Cowls, 2019; Jobin et al., 2019; National Institute of Standards and Technology, 2023). AI may generate, summarize, infer, retrieve, and recommend, but its outputs must remain traceable, reviewable, and bounded by governance.
3.10 Continuous Adaptation
Organizations evolve while preserving identity and correspondence (March, 1991; Teece et al., 1997). Adaptation is not only change. It is change that retains enough semantic continuity for the organization to learn from experience and revise future action intelligibly.
Supplementary Figure S1
Architectural Foundations
