2 citations · 3 across the 4 of their papers we have counts for
6 papers
Mining Agent Skills from Production Traces
Yue Ran Kang, Colton Mikolajczyk, Chhaya Methani +4
Agent skills that record procedural instructions are increasingly mined from execution traces rather than curated by hand. Skill-mining pipelines often use known task outcomes or f…
Is Bash All You Need? An Empirical Study of Tool Interfaces for Enterprise Digital Worker Agents
Hazel Mak, Susheel Suresh, Sahil Bhatnagar +3
In this study, we examine whether a general shell can outperform specialized tools on enterprise tasks. Shell-based agents have shown strong results in coding, but enterprise work…
Grounding Agent Memory: Environment-Probing Curation for Enterprise Agents
Susheel Suresh, Hazel Mak, Sahil Bhatnagar +2
Persistent memory is entering production-oriented agent platforms to help long-horizon agents accumulate experience across sessions. Yet a post-task curator agent restricted to com…
SLM Finetuning for Natural Language to Domain Specific Code Generation in Production
Renjini R. Nair, Damian K. Kowalczyk, Marco Gaudesi +1
Many applications today use large language models for code generation; however, production systems have strict latency requirements that can be difficult to meet with large models.…
Plan with Code: Comparing approaches for robust NL to DSL generation
Nastaran Bassamzadeh, Chhaya Methani
Planning in code is considered a more reliable approach for many orchestration tasks. This is because code is more tractable than steps generated via Natural Language and make it e…
A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation
Nastaran Bassamzadeh, Chhaya Methani
Natural Language to Code Generation has made significant progress in recent years with the advent of Large Language Models(LLMs). While generation for general-purpose languages lik…