Legora builds legal agents without visibility into their own work

The core insight isn't that Legora uses ChatGPT for legal work (covered in late October), but that client confidentiality forces them to build agents that cannot inspect their own outputs during development or testing. This inverts the standard debugging workflow and raises a practical question: how do you iterate on a system you cannot observe?

Legora's earlier coverage from late October positioned the company as an agentic platform automating contract review and NDA work, with Emma Kadri Bergström describing how automation freed lawyers from routine tasks. This new interview reveals the operational cost of that privacy guarantee. Where the October stories emphasized capability and labor reallocation, this one exposes the engineering tradeoff: Legora had to abandon traditional observability to meet client requirements, then recently reconsidered by adding model selection. The shift suggests that as legal AI matures, firms face a choice between full transparency (easier to debug, harder to sell) and operational blindness (easier to deploy, harder to improve).

If Legora publishes benchmarks or case studies showing measurable accuracy gains after adding model selection, that confirms the constraint was genuinely limiting iteration speed. If they remain silent on performance improvements over the next six months, the move was likely defensive (matching competitor offerings) rather than capability-driven.