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A finance benchmark asks agents to finish the whole assignment
The new DAYJOB: Finance benchmark tests whether agents can perform actual professional accounting and financial analysis tasks rather than just answering questions.
What: Surge AI released DAYJOB: Finance, a benchmark measuring AI agents on 80 long-horizon financial tasks such as store expansion feasibility, debt covenant analysis, and bond pricing corrections. Claude Opus 5.5 leads the current leaderboard with a 23.9% success rate on these complex, multi-step workflows.
Why it matters: Existing benchmarks often reward reasoning-lite answers; this benchmark requires agents to maintain internal logic across long-duration workflows, mirroring real-world corporate finance roles.
Deep dive
- The benchmark requires agents to process realistic documents and generate professional analysis.
- Tasks include evaluating expansion proposals, identifying accounting errors, and ensuring compliance with financial covenants.
- Claude Opus 5.5 and GPT 6 Astra are the highest-performing models currently tested.
- Models struggle significantly with complex multi-step tasks, as evidenced by low success rates across the board.
- Success requires mastery of tool use, planning, groundedness, and domain-specific knowledge.
Decoder
- Long-horizon task: A project that takes many steps over time to complete, requiring the agent to maintain context and planning.
- Covenant breach: A situation where a company violates the terms of a loan agreement, such as exceeding debt-to-EBITDA ratios.
- DCF (Discounted Cash Flow): A valuation method used to estimate the value of an investment based on its expected future cash flows.
- Unlevered IRR: The internal rate of return on an investment calculated without considering the impact of debt financing.
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