Opening — Why this matters now As large language models quietly slide from novelty to infrastructure, a less glamorous question has become existential: who pays the inference bill? Agentic systems amplify the problem. A single task is no longer a prompt—it is a chain of reasoning steps, retries, tool calls, and evaluations. Multiply that by production scale, and cost becomes the bottleneck long before intelligence does. ...
The punchline Static multi‑agent pipelines are expensive on easy questions and underpowered on hard ones. DAAO (Difficulty‑Aware Agentic Orchestration) proposes a controller that first estimates the difficulty of each query, then composes a workflow (operators like CoT, ReAct, Multi‑Agent Debate, Review/Ensemble) and finally routes each operator to the most suitable model in a heterogeneous LLM pool. The result: higher accuracy and lower cost on suite benchmarks. Why this matters (business lens) Spend less on routine queries. Easy tickets don’t need five agents and GPT‑Ultra—DAAO keeps them shallow and cheap. Don’t whiff on the edge cases. When the question is gnarly, DAAO deepens the DAG and upgrades the models only where it pays. Procurement leverage. Mixing open‑weights (Llama/Qwen) with commercial APIs lets you arbitrage price–performance per step. What DAAO actually does DAAO is three tightly coupled decisions per query: ...