#Agent评估

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Stories, products and papers tagged "Agent评估", newest first.
Oct 27
Oct 21
PapersBuildersSelected00:11
SFResearch Study Tests Fragility of Memory-Based Self-Improving Agents

The takeaway is that agent self-improvement may be more fragile than reported scores suggest, because evaluation conditions differ sharply from production task streams. Teams building memory-based agents should run multi-seed evaluations, randomise task order, and enrich memory construction with explicit rubrics and feedback. Code and data are released openly, so practitioners can replicate the findings directly.

官方账号Salesforce AI Research@SFResearchSource
Oct 6
Sep 23
How-toBuildersMulti-sourceSelected00:53
Clay evaluates AI agents on quality, throughput and cost

The framework shifts agent evaluation from single accuracy scores to a three-way trade-off, which changes how teams should size models and routing for production workloads. Builders deploying agents in cost-sensitive markets can use LangSmith evals to run the same quality-versus-cost benchmarking on their own pipelines before committing to a provider.

Event
官方账号LangChain@LangChainAI6 sourcesSource
Jul 23