Tool Calling at Scale in a Sovereign AI Factory

Tool calling lets models complete real work by invoking external systems. In a sovereign AI factory, we must pair this capability with strict governance, auditability and performance controls—without sacrificing developer velocity.

Reference Architecture

• Central function catalogue with versioned JSON Schemas and typed SDKs
• Policy engine to enforce PII redaction, rate limits and allow‑lists per tenant
• Saga orchestration for multi‑step tool plans with retries and timeouts
• Signed audit logs (WORM storage) for every call and response

Performance Patterns

• Warm pools for high‑QPS tools; circuit breakers for downstream instability
• Deterministic latency budgets; short‑circuit fallback responses
• Streaming partial results back to the user interface

Key Takeaways

• Tool calling must be productised with schemas, policies and audits
• Throughput and reliability rely on pooling, back‑pressure and fallbacks
• Sovereign controls protect sensitive Australian data while enabling real outcomes