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Schema-Guided Reasoning

Intent

Enforce strict JSON Schema validation contracts directly on prompt templates and model invocation parameters to ensure deterministic, structurally verified outputs across agent reasoning steps.

Context

Unstructured completions from language models require error-prone text parsing and introduce significant risk of schema drift. In multi-step architecture pipelines where subsequent stages depend upon well-formed structured models, unvalidated outputs can silently fail or propagate corrupted state across execution runners.

Agentic Profile

Mandatory contract for all reasoning engine components, agentic mergers, and synthesis steps that generate domain data structures or mutate repository state.

Forces

Balances the generative flexibility of language models with the rigorous structural consistency demanded by automated architecture pipelines.

Drivers

  • Need for deterministic, machine-readable structures from non-deterministic language model completions
  • Prevention of malformed or hallucinated attributes from propagating to downstream synthesis stages
  • Auditable schema validation before persisting governance or architectural state

Constraints

  • Automated verification of schema drift must be enforced within CI/CD delivery pipelines
  • Prompt specifications must remain synchronized with centralized JSON Schema registries
  • Reasoning contracts must execute in src/cas_runner/orchestration/reasoning.py without provider leakage

Tradeoffs

  • Ongoing maintenance discipline required to evolve schemas alongside prompt templates
  • Immediate step termination upon schema mismatch, trading lenient execution for total state integrity

Solution

Bind prompt execution contracts directly to registered JSON Schemas. The contract injects schema definitions into prompt execution parameters and strictly validates returned model responses against the registered schema before handing control back to orchestration runners, raising descriptive validation errors at the failure boundary.

Operationalisation

Operationalised through reasoning execution contracts that automatically inject schemas, validate outputs, and gate pipeline stages in CI/CD.

Workflow Implications

  • All reasoning engine steps producing structured domain outputs must declare and bind to explicit schemas registered under src/cas_runner/schemas/
  • Validation errors trigger immediate pipeline failure with precise schema diagnostic reports
  • Schema-to-step mappings must be centrally maintained within src/cas_runner/orchestration/reasoning.py

Automation Opportunities

  • Automate schema drift detection against evolving prompt templates during pull request CI validation
  • Generate prompt parameter constraints programmatically from centralized JSON Schema files

Implementation Signals

Evidenced by ReasoningExecutionContract in src/cas_runner/orchestration/reasoning.py, centralized schemas in src/cas_runner/schemas/, and structured validation checks across pipeline steps.

Evidence

Demonstrated across core diagnostic, evolution map generation, and intent synthesis workflows backed by concrete implementation artifacts.

Signals

  • Concrete implementation of ReasoningExecutionContract in src/cas_runner/orchestration/reasoning.py
  • Centralized JSON schema repository under src/cas_runner/schemas/
  • Consistent execution across forensic diagnostic, AEM generation, and AID synthesis steps

Confidence Basis

Empirical implementation in core runner modules with end-to-end schema validation verified across reasoning pipelines.

Consequences

Substantially eliminates reasoning parsing errors and ensures domain contract adherence while introducing schema lifecycle management discipline.

Benefits

  • Prevents malformed model completions from corrupting downstream synthesis steps
  • Provides an auditable, schema-validated artifact trail for all architecture workflows
  • Localizes reasoning failures immediately to the point of model invocation

Risks

  • Runtime step failures caused by schema drift when prompt templates evolve without corresponding schema updates
  • Maintenance overhead of synchronizing schemas across distributed agent tasks

Governance Implications

  • Mandatory schema drift detection in CI/CD pipeline to satisfy ArchGov review conditions
  • Reasoning contracts are auditable compliance checkpoints across all continuous architecture runs

Reuse Notes

Applicable across all continuous architecture steering steps, agentic mergers, and forensic diagnostic tools. Can be reused across any multi-agent pipeline requiring verified JSON outputs.

Confidence

High confidence supported by complete codebase implementation, established schema registries, and operational verification.

Level: high

Rationale

Pattern is implemented in production code via ReasoningExecutionContract and proven across forensic, AEM, and AID workflows.