Why Your Sync Engine Should Be Deterministic

There's a growing trend of using AI for everything — including live data synchronization. While AI is transformative for building integrations, using it in the runtime path introduces a fundamental problem: unpredictability.

The problem with probabilistic sync

When you're syncing stock levels between Shopify and your warehouse system, accuracy matters. If your sync engine uses an LLM to decide how to map fields or whether to process an event, you're introducing:

• Non-determinism — the same input might produce different outputs on different runs
• Latency — LLM inference adds seconds to what should be millisecond operations
• Cost — processing millions of events through an LLM is expensive
• Debugging difficulty — "why did it sync 0 instead of 47?" becomes an AI interpretability problem

AI at build time, deterministic at runtime

PullPush takes a different approach. AI is used extensively at build time:

• Connector generation — AI reads your API documentation and generates a connector definition
• Field mapping — AI suggests canonical field mappings based on schema analysis
• Test generation — AI creates test cases for edge cases humans might miss
• Error remediation — AI analyzes DLQ errors and suggests fixes

But at runtime, the sync engine is completely deterministic:

• Canonical mapping is a fixed schema-to-schema transformation
• Per-key ordering prevents race conditions without any AI involvement
• Circuit breakers follow predictable exponential backoff
• Dead-letter recovery preserves the exact payload for retry

The null vs zero problem

Here's a concrete example of why determinism matters. When syncing stock levels, there's a critical difference between:

• quantity: 0 — the product is out of stock at this location
• quantity: null — we don't have data for this location

If your sync engine sends 0 when it should send null, you just wiped the destination's stock. This happened to us in testing — and it's exactly the kind of edge case that a probabilistic system might get wrong 1% of the time.

PullPush's extractors distinguish between real zeros and missing data. It's a simple null check — no AI needed, and it's correct 100% of the time.

The best of both worlds

The sweet spot is using AI where it excels (understanding API docs, suggesting mappings, generating code) and using deterministic logic where correctness is non-negotiable (processing events, maintaining ordering, handling failures).

This is the core principle behind PullPush: AI at build time, deterministic at runtime.