Orchestration

One model doesn't do everything. So you need orchestration: routing requests to the right system, coordinating multi-step processes, handling failures, managing dependencies. Orchestration is the conductor making sure all the pieces play together. Simple orchestration: if intent is X, route to model A; if intent is Y, route to model B. More complex: start with model A, if it's uncertain, ask model B for confirmation. Parallelize where possible (run steps in parallel), serialize where required (step 2 needs step 1's output). Even more complex: adaptive orchestration that learns which routing works best, adjusts as context changes. The failure handling angle is critical. If step 1 fails, does the whole thing fail? Do you retry? Do you fall back to a different approach? Human intervention? Most production systems have multiple fallback strategies. Orchestration frameworks handle this: Langchain, Llamaindex, etc. Though honestly a lot of orchestration is custom business logic. Your system knows that billing questions need billing team context, technical questions need documentation context. You're routing and preparing context. The computational cost matters. Orchestration adds overhead. If you're routing through three models to answer a question that one model could handle, you've wasted compute. So good orchestration minimizes unnecessary overhead while maintaining reliability. There's also the latency angle. Serialized orchestration (step 1, then step 2, then step 3) is slow. Parallel where possible. But not all steps can parallelize. Dependencies matter. Synap's orchestration framework handles complex multi-step workflows, intelligent routing, and failure handling, letting developers build sophisticated systems without managing orchestration details themselves.