Cost Efficiency

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: ...

The short of it A new study on SWE-agent working over SWE-bench Verified finds that masking old observations (keeping recent turns verbatim, replacing older tool outputs with a placeholder) often matches or slightly beats prompt-based LLM summarization—and at roughly half the cost. The paper also surfaces a subtle failure mode: summaries can elongate trajectories, encouraging agents to “keep going” when they should stop, diluting efficiency and, at times, performance. Why this matters for builders Most production SE agents (debuggers, PR autoresponders, test fixers) rack up spend on two things: tokens and time. Tool logs dominate both. In practice, observation tokens comprise the bulk of an agent’s turn, so trimming them intelligently is the highest‑leverage knob. The results show you might not need fancy, model‑authored summaries; a rolling “mask” window can land on the most efficient frontier—equal or better solve rate, far lower cost—across Qwen3‑Coder 480B, Qwen3‑32B (thinking/non‑thinking), and Gemini 2.5 Flash (thinking/non‑thinking). ...