Orchestration

Compounding Orchestration drift.

Errors compound across a 20–50 LLM chain. A 1% per-step slip becomes a 40% wrong answer.

Compounding Orchestration intelligence.

ShareContext learns from every chain — sharper, cheaper, more accurate over time.

— The Problem

A 1% slip per step becomes 40% drift at the end.

100% 75% 50% 25% 0% 0 10 25 50 CALLS IN CHAIN → 60.5% · 1% 7.7% · 5% 0.5% · 10%
  • 1% per call × 50 calls → 60.5% final accuracy
  • 5% per call × 50 calls → 7.7% final accuracy
  • Adding QA agents reduces error — but spikes cost and latency
— The Answer

Balance deterministic rules with LLM calls, per step.

ACCURACY LATENCY RISK COST LEARN TO BALANCE
  • Deterministic rules when they suffice — cheap, fast, exact
  • LLMs when judgment is needed — under guardrails
  • Four levers the engine balances: cost · latency · accuracy · risk

Like the best of AI — ShareContext orchestration sharpens with every chain, every run.