4.3AI's Role in Contract Maintenance

Task	Traditional Approach	AI-Augmented Approach
New consumer feature	Manually write new Pact tests	AI analyzes client code, generates contract
Provider schema change	Consumer tests fail in CI	AI detects drift, suggests contract update
Contract conflict resolution	Manual negotiation between teams	AI identifies minimal compatible contract
Coverage gap detection	Manual audit	AI compares client code paths to Pact interactions

Detecting Coverage Gaps

AI can compare the consumer's API client code against existing Pact interactions to find missing coverage:

class ContractCoverageAnalyzer:
    def __init__(self, llm, client_code: str, pact_file: str):
        self.llm = llm
        self.client_code = client_code
        self.pact = json.load(open(pact_file))

    def find_gaps(self) -> list[str]:
        """Find API calls in client code without Pact coverage."""
        prompt = f"""
        Compare these two artifacts:

        1. API CLIENT CODE (all HTTP calls the consumer makes):
        {self.client_code}

        2. PACT INTERACTIONS (all API calls covered by contracts):
        {json.dumps([i['description'] for i in self.pact['interactions']])}

        List any API calls in the client code that do NOT have a
        corresponding Pact interaction. For each gap, describe:
        - The HTTP method and path
        - What the client expects in the response
        - Why this gap matters (what could break without a contract)
        """
        return self.llm.generate(prompt)

Automatic Contract Updates

When the provider's schema changes, AI can suggest the minimal contract update:

class ContractUpdateAdvisor:
    def suggest_update(self, old_schema: dict, new_schema: dict, pact: dict) -> str:
        """Suggest contract updates when provider schema changes."""
        prompt = f"""
        The provider's schema has changed. Analyze the change and determine
        if any Pact contracts need updating.

        OLD SCHEMA (relevant section):
        {json.dumps(old_schema, indent=2)}

        NEW SCHEMA (relevant section):
        {json.dumps(new_schema, indent=2)}

        CURRENT PACT INTERACTIONS:
        {json.dumps(pact['interactions'], indent=2)}

        For each affected interaction:
        1. Describe what changed
        2. Whether the change is backward-compatible
        3. If not backward-compatible, suggest the minimal contract update
        4. Flag if the consumer code likely needs changes too
        """
        return self.llm.generate(prompt)