Tool Calling at Scale in a Sovereign AI Factory
September 9, 2025•7 min read
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