FlowSteer is a prompt-only attack that biases multi-agent LLM workflow planning to propagate malicious signals, raising success rates by up to 55%, with FlowGuard as an input-side defense reducing it by up to 34%.
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Agentops: Enabling observability of llm agents
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Catalogs ten patterns and synthesizes a four-layer reference architecture for skill harnessing in LLM agents, evaluated via cross-instantiation on eight systems.
TrajAudit pinpoints the earliest wrong step in long AI-coding-agent logs with 50.9% exact accuracy on the new RootSE benchmark, beating prior methods by about 24 percentage points.
Presents an AI Toolkit plugin for JetBrains IDEs that integrates trace capture and evaluation into the Run/Debug loop, guided by practitioner needs and showing early adoption signals in PyCharm.
PROBE turns runtime telemetry from failed software engineering agent runs into evidence-grounded diagnoses and actionable recovery guidance, achieving 65.37% diagnosis accuracy and 21.79% recovery rate on 257 cases.
Agent-BOM is a unified hierarchical attributed directed graph that models static capability bases and dynamic semantic states of LLM agents for path-level security auditing and risk assessment.
ClawTrace enables cost-aware LLM agent skill distillation by tracing per-step costs and generating preserve, prune, and repair patches, with ablations showing reduced regressions and prune rules transferring to cut costs by 32%.
No agent system can be accountable without auditability, which requires five dimensions (action recoverability, lifecycle coverage, policy checkability, responsibility attribution, evidence integrity) and mechanisms for detect/enforce/recover.
GRADE models any LLM agent run as a graph with execution and graded dependency edge layers to enable failure prediction and fault localization across tool, coding, and web agent corpora.
This survey defines execution provenance as a typed graph of agent execution and evidence tracing as its projection onto evidence-support relations, then reviews methods, taxonomy, benchmarks, and challenges for auditable LLM agents.
Hyperparam supplies under-70KB JS libraries (Hyparquet, Squirreling, Icebird) for async-native SQL over Parquet/Iceberg with per-cell LLM UDFs, claiming 300x speedup versus DuckDB-WASM on filter queries and two-thirds lower cost on agent analyst tasks.
The central challenge in AI-augmented CI/CD is designing authority transfer from humans to agents under constraints, as current systems remain limited to bounded data-plane autonomy backed by external governance.
Introduces Agent Behavior Mining to translate generative AI agent activities into standardized process logs, enabling process mining for policy deviation detection and variability quantification in business processes, with practitioner feedback.
This survey categorizes anomalies in agent systems into intra-agent and inter-agent types and introduces the AgentOps framework with four operational stages.
citing papers explorer
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FlowSteer: Prompt-Only Workflow Steering Exposes Planning-Time Vulnerabilities in Multi-Agent LLM Systems
FlowSteer is a prompt-only attack that biases multi-agent LLM workflow planning to propagate malicious signals, raising success rates by up to 55%, with FlowGuard as an input-side defense reducing it by up to 34%.
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Harnessing Agent Skills: Architectural Patterns and a Reference Architecture for Skill-Mediated LLM Agents
Catalogs ten patterns and synthesizes a four-layer reference architecture for skill harnessing in LLM agents, evaluated via cross-instantiation on eight systems.
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TrajAudit: Automated Failure Diagnosis for Agentic Coding Systems
TrajAudit pinpoints the earliest wrong step in long AI-coding-agent logs with 50.9% exact accuracy on the new RootSE benchmark, beating prior methods by about 24 percentage points.
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In-IDE Toolkit for Developers of AI-Based Features
Presents an AI Toolkit plugin for JetBrains IDEs that integrates trace capture and evaluation into the Run/Debug loop, guided by practitioner needs and showing early adoption signals in PyCharm.
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Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
PROBE turns runtime telemetry from failed software engineering agent runs into evidence-grounded diagnoses and actionable recovery guidance, achieving 65.37% diagnosis accuracy and 21.79% recovery rate on 257 cases.
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Towards Security-Auditable LLM Agents: A Unified Graph Representation
Agent-BOM is a unified hierarchical attributed directed graph that models static capability bases and dynamic semantic states of LLM agents for path-level security auditing and risk assessment.
-
ClawTrace: Cost-Aware Tracing for LLM Agent Skill Distillation
ClawTrace enables cost-aware LLM agent skill distillation by tracing per-step costs and generating preserve, prune, and repair patches, with ablations showing reduced regressions and prune rules transferring to cut costs by 32%.
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Auditable Agents
No agent system can be accountable without auditability, which requires five dimensions (action recoverability, lifecycle coverage, policy checkability, responsibility attribution, evidence integrity) and mechanisms for detect/enforce/recover.
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GRADE: Graph Representation of LLM Agent Dependency and Execution
GRADE models any LLM agent run as a graph with execution and graded dependency edge layers to enable failure prediction and fault localization across tool, coding, and web agent corpora.
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From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents
This survey defines execution provenance as a typed graph of agent execution and evidence tracing as its projection onto evidence-support relations, then reviews methods, taxonomy, benchmarks, and challenges for auditable LLM agents.
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A Query Engine for the Agents
Hyperparam supplies under-70KB JS libraries (Hyparquet, Squirreling, Icebird) for async-native SQL over Parquet/Iceberg with per-cell LLM UDFs, claiming 300x speedup versus DuckDB-WASM on filter queries and two-thirds lower cost on agent analyst tasks.
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From Assistance to Agency: Rethinking Autonomy and Control in CI/CD Pipelines
The central challenge in AI-augmented CI/CD is designing authority transfer from humans to agents under constraints, as current systems remain limited to bounded data-plane autonomy backed by external governance.
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Agent Behavior Mining: Generative AI Agent Governance in Business Processes
Introduces Agent Behavior Mining to translate generative AI agent activities into standardized process logs, enabling process mining for policy deviation detection and variability quantification in business processes, with practitioner feedback.
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Agent System Operations: Categorization, Challenges, and Future Directions
This survey categorizes anomalies in agent systems into intra-agent and inter-agent types and introduces the AgentOps framework with four operational stages.