Evolutionary coding agents achieve most benchmark gains through a small subset of edit types and by cycling previously deleted code lines rather than developing new algorithmic structures.
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Measuring agents in production
12 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
LLM-based agents already operate in production across many industries, yet we lack an understanding of what technical methods make deployments successful. We present the first systematic study of Measuring Agents in Production, MAP, using first-hand data from agent developers. We conducted 20 case studies via in-depth interviews and surveyed 86 deployed systems practitioners across 26 domains. We investigate why organizations build agents, how they build them, how they evaluate them, and their top development challenges. Our study finds that production agents are built using simple, controllable approaches: 68% execute at most 10 steps before human intervention, 70% rely on prompting off-the-shelf models instead of weight tuning, and 74% depend primarily on human evaluation. Reliability (consistent correct behavior over time) remains the top development challenge, which practitioners currently address through systems-level design. MAP documents the current state of production agents, providing the research community with visibility into deployment realities and underexplored research avenues.
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2026 12roles
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Anchor generates consistent long-horizon agent tasks from parametric constraint programs, yielding ERP-Bench of 300 ERP tasks where frontier models reach optimal solutions in 17.4% of trials.
SynAE is a multi-metric framework that evaluates how well synthetic benchmarks replicate real data characteristics for multi-turn tool-calling agent testing.
A new benchmark shows LLM smartphone agents achieve comparable success with screen text alone as with screenshots, but both fail often due to UI accessibility and reasoning gaps.
YoloFS is an agent-native filesystem that stages mutations for review, provides snapshots for agent self-correction, and uses progressive permissions to reduce user interruptions while matching baseline task success.
No existing AI security framework covers a majority of the 193 identified multi-agent system threats in any category, with OWASP Agentic Security Initiative achieving the highest overall coverage at 65.3%.
Echo is a framework that harvests user-driven refinements of agent proposals as training signals to align models with real-world needs, demonstrated by raising code completion acceptance from 25.7% to 35.7% in production.
RAC is a log-based recovery paradigm implemented as an architectural extension to agent frameworks, achieving 1.5-8X better latency and token economy than LLM-based recovery on τ-bench and REALM-Bench.
Supervised fine-tuning with 0.1% labeled data outperforms all 60 tested prompt variants for CLIPSeg cloud segmentation on satellite imagery under domain shift.
Copa is a theory-guided multimodal LLM agent that supports high school computational modeling through adaptive feedback, shown in a 33-dyad study to increase student confidence and conceptual verbalization without fostering dependence.
Compiled AI generates deterministic code artifacts from LLMs in a one-time compilation step, enabling reliable workflow execution with zero runtime tokens after break-even.
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Do LLMs Need to See Everything? A Benchmark and Study of Failures in LLM-driven Smartphone Automation using Screentext vs. Screenshots
A new benchmark shows LLM smartphone agents achieve comparable success with screen text alone as with screenshots, but both fail often due to UI accessibility and reasoning gaps.