CIAS
Component Impact Analysis, Agentic Delivery Governance, and Hecate Need Creation (CIAS)
1. Purpose
This specification defines how ZAYAZ converts governed human intent into traceable, machine-verifiable production software while preserving architectural authority, implementation accountability, runtime provenance, and human approval.
It governs the relationship between:
- human product and engineering intent;
- canonical Docusaurus/ZAYAZ Docs specifications;
- Hecate Need Creator;
- Jira implementation work;
- AI engineering agents;
- GitHub implementation repositories;
- deterministic validation and CI/CD;
- AWS runtime environments;
- feedback from runtime telemetry, defects, incidents, audits and regulatory change.
The objective is to enable safe AI-assisted and increasingly agentic software delivery while preserving:
- architectural integrity;
- compliance traceability;
- system-wide correctness;
- schema and contract compatibility;
- auditability;
- agent accountability;
- human governance of intent and outcomes.
2. Canonical Operating Principle
The ZAYAZ software delivery operating model SHALL be summarized as:
Humans define intent. ZAYAZ Docs defines truth. Jira defines work. AI builds and verifies. ZAYAZ Platform contains the implementation. AWS executes it.
This statement establishes responsibility boundaries; it does not imply that any single layer acts without governance from the others.
A second normative principle defines the human/machine boundary:
Humans govern intent and approve outcomes. Machines prove as much implementation conformance as can be deterministically proven. AI handles interpretation, generation, coverage expansion, diagnosis and remediation.
AI-generated confidence, review, explanation or code SHALL NOT be represented as mathematical proof of complete software correctness. Deterministic validators, schemas, tests, formal constraints and runtime controls establish the strongest available evidence of conformance; humans remain accountable for the intended behavior and approval boundaries assigned to them.
3. Six-Layer Delivery Model
HUMAN INTENT
│
│ product need / business rule / acceptance outcome
▼
ZAYAZ DOCS — Governance & Contract Plane
│
│ canonical architecture / ontology / schemas / registries
│ contracts / invariants / acceptance semantics
▼
JIRA — Implementation Orchestration Plane
│
│ machine-readable implementation packet
│ dependencies / acceptance criteria / gates / provenance
▼
AI ENGINEERING — Interpretation, Build & Verification
│
│ implementation / tests / validation / diagnosis / remediation
▼
ZAYAZ PLATFORM — Implementation & Delivery Plane
│
│ service code / adapters / APIs / migrations / IaC / pipelines
▼
AWS — Runtime & Data Plane
Telemetry / defects / incidents / discoveries
└──────────────────────────────→ governed feedback loop
The six layers form one delivery system. They are not competing sources of truth.
4. Layer Responsibilities
4.1 Human Intent
Humans define and govern:
- desired capability;
- business outcome;
- unacceptable behavior;
- policy intent;
- risk tolerance;
- acceptance outcomes;
- architecture decisions requiring human authority;
- approval of consequential outcomes where governance requires it.
Human intent may originate from product management, architecture, engineering, sustainability, compliance, assurance, customer feedback, incidents or strategic decisions.
Human intent SHALL NOT require the requester to manually specify every technical implementation detail when that detail can be derived from canonical architecture.
4.2 ZAYAZ Docs — Governance & Contract Plane
ZAYAZ Docs is the canonical source of architectural truth.
It may contain executable governance tooling, including:
- canonical MDX specifications;
- ontology and controlled registries;
- JSON Schemas;
- API/event/payload contracts;
- invariants;
- constitutional rules;
- validators;
- compatibility checks;
- fixtures;
- contract tests;
- architecture linting;
- generators for derived metadata.
ZAYAZ Docs SHALL NOT contain mutable production tenant state or execute production business workloads.
The distinction is:
governance-time execution ≠ production-time execution
4.3 Jira — Implementation Orchestration Plane
Jira defines governed implementation work derived from canonical truth.
Jira SHALL NOT become the source of architectural truth. A Jira work item SHALL reference the canonical specification, contract and registry versions that define the work.
The preferred Jira artifact is a machine-readable implementation packet, not an isolated free-text ticket.
It should carry or reference sufficient context for an AI or human engineer to execute the task without reconstructing architecture from memory.
4.4 AI Engineering — Interpretation, Build & Verification
AI engineering performs work such as:
- interpreting an approved implementation packet;
- generating or modifying source code;
- generating unit, contract, integration, negative, property, replay and workflow tests where applicable;
- expanding test coverage from canonical invariants;
- validating schemas and contracts;
- detecting incompatible implementation behavior;
- diagnosing CI/runtime failures;
- proposing or executing bounded remediation;
- generating implementation evidence.
AI SHALL NOT silently redefine canonical architecture to make implementation easier.
Where implementation reveals a missing or contradictory architecture contract, the issue SHALL return through Jira/Hecate to ZAYAZ Docs for governed resolution.
4.5 ZAYAZ Platform — Implementation & Delivery Plane
viroway-ltd/zayaz-platform contains production implementation artifacts such as:
- domain/service code;
- runtime APIs;
- persistence adapters;
- event handlers;
- integration adapters;
- database migrations;
- infrastructure-as-code;
- deployment definitions;
- implementation-level automated tests.
ZAYAZ Platform implements canonical contracts; it does not redefine them.
4.6 AWS — Runtime & Data Plane
AWS executes deployed ZAYAZ production workloads and stores governed operational state.
Representative runtime responsibilities may include:
- APIs;
- workers;
- queues/event buses;
- databases;
- object storage;
- secrets/keys;
- observability;
- runtime calculations;
- supplier/customer workflows;
- production integration execution.
AWS configuration and infrastructure SHALL be derived from governed implementation artifacts and deployment policy rather than becoming an independent architecture source.
5. Authority Hierarchy
For software-delivery semantics, the authority direction is:
Human-approved intent
↓
Canonical ZAYAZ Docs architecture/contracts
↓
Derived Jira implementation packet
↓
AI/human implementation
↓
ZAYAZ Platform source
↓
Deployed AWS runtime
A lower layer SHALL NOT silently override a higher layer.
Examples:
- Jira cannot redefine an EPU identity invariant defined canonically in ZAYAZ Docs.
- An AI agent cannot alter a governed schema merely to make generated code pass.
- Runtime behavior that conflicts with a canonical contract is a defect, not a new source of truth.
- A discovered production requirement may trigger an architecture change, but the change becomes authoritative only after canonical update and approval.
6. Architecture Before Work
Tickets never define architecture.
Tickets reference architecture.
A production implementation task SHOULD NOT enter agent-ready state until its architectural target and required contracts can be resolved.
If canonical information is missing, Hecate SHALL classify the need as requiring architecture resolution rather than asking an implementation agent to invent the missing semantics.
7. Jira Implementation Packet
The target Jira work-item structure is an implementation packet such as:
implementation_packet:
need_id:
jira_key:
capability:
change_intent:
canonical_source:
document_ids: []
source_refs: []
source_commit:
target:
module_ids: []
component_ids: []
section_ids: []
implementation_repository: viroway-ltd/zayaz-platform
contracts:
interfaces: []
schemas: []
payloads: []
events: []
registries: []
rulesets: []
invariants: []
forbidden_changes: []
acceptance_criteria: []
impact:
upstream: []
downstream: []
blast_radius:
validation:
required_test_profiles: []
required_gates: []
test_plan_ref:
rollback_plan_ref:
agent:
allowed:
context_pack_ref:
autonomy_boundary:
evidence_required:
- implementation_commit
- contract_validation
- test_results
- ci_result
The concrete Hecate/Jira schema is governed by HNC and SHALL be upgraded separately. This section establishes the normative capability and information categories, not a final Jira field schema.
8. Hecate Need Creator Role
Hecate Need Creator (HNC) is the transformation engine between human/canonical intent and Jira implementation work.
Canonical flow:
Human Need
↓
Canonical Resolution
↓
Component / Interface / Contract / Schema / Registry Resolution
↓
Dependency & Blast-Radius Analysis
↓
Acceptance and Validation Derivation
↓
Machine-Readable Implementation Packet
↓
Jira
Hecate SHALL minimize manual ticket authoring where canonical metadata can be resolved automatically.
The future HNC upgrade SHALL expand its existing Jira output model so that Jira can serve as the implementation-orchestration plane described in this specification.
HNC remains separately governed by the Hecate Need Creator Specification and its Jira Epic ZYZ-904.
9. Component Model
Each component specification should expose sufficient metadata for implementation derivation, including where applicable:
component_id:
semantic_id:
interfaces:
schemas:
contracts:
rulesets:
test_profiles:
rollback_profiles:
criticality:
implementation_targets:
Canonical component metadata remains the source for engineering governance.
10. Interface Model
Interfaces represent logical capabilities provided by a component.
Interfaces SHALL NOT be represented by Jira tickets.
A work item may modify or implement an interface, but the interface identity and semantic contract remain canonical architecture.
11. Contract, Schema and Ruleset Governance
Contracts represent versioned interoperability promises between components.
Schemas validate structural properties such as types, required fields, enumerations and structural constraints.
Rulesets and governed semantic logic define meaning that cannot be established by schema shape alone.
Implementation packets SHALL resolve all affected contracts, schemas and rulesets that can be derived from canonical metadata.
Breaking or governance-sensitive changes SHALL route to the required human review instead of being silently performed by an implementation agent.
12. Dependency Graph and Blast Radius
Impact analysis SHALL be derived from metadata and graph relationships whenever authoritative metadata exists.
Representative graph traversal:
Component
→ Interface
→ Contract
→ Schema
→ Ruleset
→ Consumer
→ Workflow
→ Deployment/Runtime dependency
Blast-radius analysis informs:
- agent autonomy;
- test depth;
- reviewer routing;
- rollout strategy;
- rollback requirements;
- required environment validation.
13. Verification Model
The objective is not for AI to merely state that an implementation is correct.
The objective is to accumulate machine-verifiable evidence of conformance.
Possible evidence layers include:
schema validation
registry validation
static/type validation
unit tests
negative tests
property/invariant tests
contract tests
compatibility tests
integration tests
workflow tests
replay tests
security validation
compliance validation
migration validation
infrastructure validation
staging validation
runtime health/telemetry
A work item may declare a subset mandatory based on its impact and contract profile.
AI may generate and expand tests, but the relevant deterministic test/validator result is the evidence artifact.
14. Implementation Evidence Packet
Completion should produce a machine-readable evidence packet or equivalent governed references.
Representative structure:
implementation_evidence:
need_id:
jira_key:
canonical_source_commit:
implementation_repository:
implementation_commit:
contract_versions: []
schema_versions: []
registry_versions: []
validation_results:
schema:
registry:
unit:
contract:
negative:
integration:
workflow:
security:
ci_run_ref:
deployment_ref:
reviewer_approvals: []
The evidence packet allows humans and machines to inspect conformance without requiring a human to manually audit every generated source line.
15. Requirements-to-Runtime Traceability
ZAYAZ SHALL aim to preserve traceability in both directions:
Human Need
↓
Canonical specification / decision
↓
Hecate Need
↓
Jira implementation packet
↓
Agent context / implementation task
↓
ZAYAZ Platform commit
↓
CI evidence
↓
AWS deployment
↓
Runtime telemetry
And backwards:
AWS deployment/runtime observation
↑
implementation commit
↑
Jira work item
↑
Hecate need
↑
canonical specification/version
↑
human-approved intent
This is requirements-to-runtime provenance.
It applies the same traceability philosophy used by ZAYAZ for sustainability evidence and computation lineage to ZAYAZ's own engineering system.
16. Feedback and Remediation Loop
Runtime does not modify canonical truth directly.
Telemetry / defect / incident / audit / regulation
↓
Need or anomaly created
↓
Hecate canonical resolution
↓
Is architecture change required?
┌──────────────┴──────────────┐
YES NO
↓ ↓
Update/approve ZAYAZ Docs Implementation remediation
↓ ↓
New implementation packet ←──────┘
↓
AI/human delivery
↓
ZAYAZ Platform
↓
AWS
Production discoveries may therefore improve architecture without allowing production state to become architecture by accident.
17. Agentic Development Governance
Agents operate within approved governance boundaries.
Canonical execution pipeline:
Approved implementation packet
↓
Agent context assembly
↓
Implementation
↓
Generated/expanded validation
↓
Deterministic validation gates
↓
Diagnosis/remediation loop
↓
Human review where required
↓
Merge
↓
Deployment gates
↓
AWS runtime
Agent autonomy SHALL be derived from impact, governance and risk policy.
A critical change may permit AI to draft code and tests while prohibiting autonomous merge or deployment.
18. Human Approval Boundary
Human review should focus increasingly on:
- whether the intended capability is correct;
- whether acceptance criteria represent the intended outcome;
- whether architecture/policy changes are appropriate;
- whether high-impact or consequential deployment should proceed;
- whether machine-produced evidence is sufficient for the applicable governance level.
Humans need not manually reproduce deterministic validation already performed by trusted tooling, but human approval SHALL NOT be replaced where policy requires accountability or judgment.
19. Repository and Environment Boundaries
Canonical responsibilities:
viroway-ltd/zayaz-docs
architecture, registries, schemas, contracts, governance tooling
Jira
governed implementation work and delivery state
viroway-ltd/zayaz-platform
runtime implementation and infrastructure-as-code
AWS
deployed runtime and operational data
Executable tests and validators may exist in multiple layers for different purposes:
- ZAYAZ Docs tests validate canonical architecture/contracts.
- ZAYAZ Platform tests validate implementation conformance and runtime behavior.
- AWS/staging tests validate deployed system behavior and environment integration.
Duplication of validation intent across these layers may be deliberate; duplication of architectural authority is prohibited.
20. Delivery Gate Principle
A delivery gate SHALL answer a specific question and produce inspectable evidence.
Examples:
Architecture gate:
Does the requested work resolve to canonical architecture?
Contract gate:
Does implementation conform to required schemas/contracts?
Behavior gate:
Do deterministic tests demonstrate required behavior?
Integration gate:
Do affected components interoperate correctly?
Deployment gate:
Is the implementation eligible for the target environment?
Outcome gate:
Does the resulting capability satisfy the approved human intent?
No single AI review substitutes for these gates.
21. Failure Semantics
When AI or CI discovers a failure, remediation SHALL preserve authority boundaries.
Implementation defect
→ fix in ZAYAZ Platform
Missing/ambiguous implementation instruction
→ refine Jira/Hecate packet
Missing/contradictory canonical contract
→ resolve in ZAYAZ Docs
Runtime/environment defect
→ implementation/IaC or AWS remediation as appropriate
Human intent ambiguity
→ return to human decision
Agents SHALL NOT resolve a higher-layer ambiguity by silently changing a lower-layer interpretation.
22. Jira Governance
Jira is a delivery and implementation-orchestration system, not the architecture source of truth.
A Jira work item should progressively contain or reference:
- canonical source and version/commit;
- target module/component;
- affected interfaces/contracts/schemas/registries/rulesets;
- acceptance criteria;
- forbidden changes;
- impact/blast radius;
- required validation profile;
- test/rollback references;
- agent eligibility/autonomy;
- implementation repository target;
- produced implementation/evidence references.
Fields derivable from canonical metadata SHOULD be machine-generated by Hecate rather than manually maintained.
23. GitHub Governance
GitHub repositories contain versioned canonical or implementation artifacts according to repository responsibility.
GitHub issues may be used as derived engineering artifacts where required by the delivery workflow, but they SHALL NOT become a competing source of implementation intent when Jira is the governed implementation-orchestration plane.
Where a GitHub issue is created, it SHALL preserve Jira and canonical-source traceability.
24. Hecate Upgrade Requirement — Deferred Implementation
The current HNC specification already defines canonical resolution, impact analysis, Jira creation, test-plan generation, rollback planning and agent context packs.
A future HNC revision SHALL extend that foundation to produce the implementation packet required by this v1.1 operating model, including at minimum:
- canonical source commit/version binding;
- explicit target implementation repository;
- resolved payload/event/registry contracts;
- forbidden-change boundaries;
- deterministic validation gates;
- implementation evidence requirements;
- requirements-to-runtime trace identifiers;
- AI autonomy boundary;
- remediation routing by authority layer.
This is an architectural requirement now, but HNC implementation is intentionally deferred until the currently active delivery work permits that upgrade.
25. Future Extensions
Future extensions may include:
- automated blast-radius simulation;
- agent routing optimization;
- multi-agent implementation orchestration;
- continuous dependency monitoring;
- workflow replay validation;
- autonomous regression generation;
- predictive impact forecasting;
- generated implementation packets with cryptographic canonical-source binding;
- deployment evidence aggregation;
- runtime-to-requirement observability;
- automated architecture-drift detection between ZAYAZ Docs and ZAYAZ Platform.
26. Normative Statement
This specification is the authoritative source governing:
- component impact analysis;
- dependency resolution;
- Hecate Need Creation within the delivery lifecycle;
- the ZAYAZ software delivery operating model;
- Jira implementation orchestration;
- agentic development workflows;
- implementation verification;
- requirements-to-runtime traceability;
- workflow validation;
- rollback generation.
All future delivery automation SHALL preserve the responsibility model:
Humans define intent. ZAYAZ Docs defines truth. Jira defines work. AI builds and verifies. ZAYAZ Platform contains the implementation. AWS executes it.