Rethinking Agent ROI: The Cost of Proving the Work Was Correct
Crescitaly · reddit · 2026-07-30
OpenAI notes that users increasingly delegate long-horizon tasks to parallel agents. However, the author argues that runtime and output volume do not tell us whether the results are actually safe to use.
For consequential tasks, the real cost of an agent includes evidence collection, human review, regression testing, rollback preparation, and fixing downstream effects. A fast agent might look cheap until the verification process consumes all the time it supposedly saved. The author asks the community how they measure agent ROI and what verification costs are being ignored.
More from coding & agent
- From Memory to Tool Injection: Context Engineering in Production AI Agents — goyalshaliniuk · 2026-07-30
- Context Layering Architecture in Production AI Applications — goyalshaliniuk · 2026-07-30
- Multi-Source Retrieval and Context Compression for Reliable AI — goyalshaliniuk · 2026-07-30
- OpenDocs: Convert GitHub READMEs and Notebooks into Docs and Slides — tom_doerr · 2026-07-30
- Demystifying Evals for AI Agents: Anthropic's Engineering Guide — burny_tech · 2026-07-30
- jQuery UI Creator: The Bottleneck for AI Code is Editing and Judgment, Not Tooling — cen6wkf · 2026-07-30