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Schema-Enforced Domain Contracts

Intent

Establish deterministic structural and semantic validation boundaries between non-deterministic actors and core business state through strict, strongly-typed domain schema contracts.

Context

In multi-step autonomous pipelines and agentic reasoning systems, non-deterministic models generate state modifications, compliance evaluations, and steering metadata. Without mandatory validation at pipeline step boundaries and persistence sinks, hallucinated or structurally malformed payloads propagate downstream, causing cascading runtime failures, defensive null-checking proliferation, and state corruption across knowledge layers.

Agentic Profile

Defines a mandatory boundary-validation guardrail applied to all autonomous agent task outputs, pipeline step handoffs, and persisted knowledge entities, ensuring model-generated artifacts satisfy rigid typing invariants before altering system state.

Forces

Balances the necessity of flexible, non-deterministic agent reasoning against the imperative for predictable, deterministic business state integrity.

Drivers

  • Elimination of hallucinated, incomplete, or malformed payloads emitted by probabilistic LLMs
  • Deterministic type guarantees for downstream automated consumers and calculation engines
  • Centralized contract definitions preventing scattered, ad-hoc assertions across workflow steps

Constraints

  • Abstract domain contracts must remain decoupled from specific validation engine implementation details
  • Validation runtime overhead must remain sub-millisecond to prevent latency accumulation across multi-stage pipelines
  • Schema evolution across versioned artifacts requires backwards compatibility and formal migration testing

Tradeoffs

  • Upfront schema design and maintenance effort increases as domain entities evolve
  • Rigid validation can reject novel, valid emergent model outputs if schemas fail to anticipate legitimate variations

Solution

Enforce strict schema validation contracts at every input, output, and persistence boundary using strongly-typed domain models. Non-deterministic agent outputs and pipeline step results are validated immediately upon generation; payloads failing validation trigger deterministic fail-fast diagnostics and halt state mutation before corrupting downstream stores.

Operationalisation

Operationalised via automated build validation gates, compliance evaluators, and step boundary hooks that validate payloads against registered schemas.

Workflow Implications

  • All workflow steps must declare explicit input and output schemas as part of their interface contract
  • Downstream consumers ingest verified domain models directly without defensive null or type checks
  • Schema evolution workflows must incorporate automated regression testing and explicit migration suites

Automation Opportunities

  • Automated schema validation gates integrated into CI pipelines and runner step executors
  • Machine-readable field-level diagnostic generation to accelerate automated remediation and prompt retry loops
  • Automated derivation and synchronization of JSON Schema definitions from canonical domain models

Implementation Signals

Observed and implemented via AST symbols in governance and steering models, enforced by CI validation workflow gates and compliance engines across forensic diagnostic, AEM generation, and AID generation flows.

Evidence

High-confidence empirical backing demonstrated across runner governance modules, CI build validation workflows, and multi-flow execution pipelines.

Signals

  • Concrete AST symbols implemented in src/cas_runner/governance/models.py and src/cas_runner/steering/models.py
  • Enforced by build validation workflow gates and the ComplianceEngine
  • Validated across forensic diagnostic, AEM generator, and AID generation flows
  • Cross-project applicability for agentic pipeline step boundaries

Supporting Evidence

  • aw-ideas-to-life-cas-2026-06-06-001
  • aw-ideas-to-life-cas-20260605-07

Confidence Basis

Empirical implementation in core AST symbols and verified test passes in CI build validation gates

Consequences

Guarantees system state integrity and robust error attribution at the cost of schema evolution and maintenance overhead.

Benefits

  • Guarantees system boundary and state integrity across all workflow stages
  • Eliminates hallucinated attributes and structural payload corruption before persistence
  • Lowers mean time to resolution (MTTR) through structured, field-level validation diagnostics
  • Removes defensive validation logic from downstream consumers

Risks

  • Schema evolution friction when upstream external LLM capabilities change rapidly
  • Potential vendor lock-in if concrete validator semantics leak into abstract domain contracts

Governance Implications

  • Validation contracts serve as authoritative architectural specifications subject to formal change control
  • Breaking schema modifications require explicit deprecation lifecycles and compatibility verification

Reuse Notes

Applicable universally across agentic pipelines, pipeline step boundaries, and persisted knowledge entities in autonomous orchestration runtimes and continuous architecture steering systems.

Confidence

High confidence supported by concrete AST implementations, verified CI gates, and extensive test suite coverage.

Level: high

Rationale

Validated through production implementations in src/cas_runner/governance/models.py, verified compliance engine checks, and automated gating in CAS Build Validation workflows.