REVIEW 6 cited by
AgentOps: Enabling Observability of LLM Agents
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Large language model (LLM) agents have demonstrated remarkable capabilities across various domains, gaining extensive attention from academia and industry. However, these agents raise significant concerns on AI safety due to their autonomous and non-deterministic behavior, as well as continuous evolving nature . From a DevOps perspective, enabling observability in agents is necessary to ensuring AI safety, as stakeholders can gain insights into the agents' inner workings, allowing them to proactively understand the agents, detect anomalies, and prevent potential failures. Therefore, in this paper, we present a comprehensive taxonomy of AgentOps, identifying the artifacts and associated data that should be traced throughout the entire lifecycle of agents to achieve effective observability. The taxonomy is developed based on a systematic mapping study of existing AgentOps tools. Our taxonomy serves as a reference template for developers to design and implement AgentOps infrastructure that supports monitoring, logging, and analytics. thereby ensuring AI safety.
Forward citations
Cited by 6 Pith papers
-
How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study
SE-agent development follows a recurring seven-stage loop where evaluation drives iteration, and challenges such as unreliable evaluation signals and comprehension debt emerge.
-
AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents
An open-source agent-debugging loop attributes failures to the responsible step and uses the diagnosis to repair failed runs, recovering 13 of 73 GAIA tasks.
-
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.
-
Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry
A proposed framework and prototype auditor aim to keep citizen-created AI agents operationally ready by checking dependencies and contracts on a schedule.
-
Blockchain Empowered Trustworthy Agent Networks: Foundations, Taxonomy, and Future Directions
A survey proposing a five-dimensional taxonomy of trust crises in open AI agent networks and analyzing blockchain's role as a shared trust infrastructure.
-
RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines
RAGOps frames RAG operations as the intertwined management of a query processing pipeline and a data lifecycle, with design considerations, challenges, and two anecdotal use cases.
Discussion (0). Continue with ORCID to comment.