← Saturday, September 26, 2026AgentsDeep dive
General coding agents overtake specialized data agents on newer models
On TAG-Bench and DAB, a general coding agent surpasses Agentar-Scale-SQL in accuracy and token efficiency with GPT-5.6 Sol, reversing the result with o3; the comparison holds the model constant while varying the scaffolding. The result supports testing whether persistent data context can outperform task-specific agent engineering as models improve.
- What
- The paper applies Sutton’s Bitter Lesson to data agents, arguing that improving LLMs will absorb much of today’s hand-engineered agent scaffolding.
- How
- On TAG-Bench and DAB, Agentar-Scale-SQL beat a general coding agent with o3, but the coding agent surpassed it with GPT-5.6 Sol.
- Why it matters
- The authors argue persistent semantic context about data environments can help agents answer many queries efficiently, shifting engineering effort from scaffolding to context.
Read on muratbuffalo.blogspot.com →
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