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Arthur AI provides a unified control plane for trustworthy enterprise AI, delivering agent discovery, observability, guardrails, and governance across the full lifecycle. It supports LLM, tabular, CV, and NLP models with bias detection, cost optimization, and SOC 2 Type II compliance.
Short answers to the questions buyers and builders commonly ask about Arthur AI. Each answer cites the shared ledger below, where every source is listed once.
Unified AI governance control plane bringing security, finance, and AI teams together to m · Step-level agent observability traces covering reasoning, tool calls, retrieval, and hando · Runtime security via Agent Behavioral Analytics (ABA) that monitors AI applications and al · Model performance and data drift detection to track accuracy and react to drift
Arthur unites security, finance, and the teams shipping AI on one control plane for AI you can trust.
Fortune 500 enterprises across security, audit, governance, product, and engineering teams · Data science and ML/MLOps teams operating production machine learning models
Trusted by leaders at Fortune 500s.
Monitoring, visibility, and protection for AI agents in enterprise workflows · Evaluate models across accuracy, fairness, bias, and toxicity · ML observability for cybersecurity threat detection and managed detection and response (MD · MLOps covering fast deployment, retraining, monitoring, and governance of ML models
Advanced solutions for AI agent monitoring, visibility, and protection.
Integrates with agentic workflows to provide observability, monitoring, and protection aga · Integrates with existing ML pipelines and LLM applications · Native integrations with GCP and AWS, plus SIEM connections · Tailored integration with internal data environments, workflows, and model constraints
Arthur's product suite seamlessly integrates with agentic workflows to provide comprehensive observability and monitoring as well as protection against dangerous outputs.
Discovers and governs agents running on endpoints, cloud, and private data centers · Self-hosted in user's own environment · Requires Docker to run · Centralized platform deployed to replace fragmented, custom-built in-house monitoring setu
Arthur's discovery capabilities find every agent running on endpoints, cloud, private data centers, and anywhere else in your compute environment.
Streams security findings into existing SOC tools for real-time guardrails and alerts · Integrations implemented in accordance with customer security standards
Stream findings to the tools your SOC already lives in.
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Arthur | AI governance for everyone
Unified AI governance control plane for security, finance, and AI teams · Step-level agent observability tracing reasoning, tool calls, retrieval, and handoffs · Agent Behavioral Analytics (ABA) for runtime monitoring and threat alerting
Unified AI governance control plane bringing security, finance, and AI teams together to measure, monitor, and govern enterprise AI
Step-level agent observability traces covering reasoning, tool calls, retrieval, and handoffs
Runtime security via Agent Behavioral Analytics (ABA) that monitors AI applications and alerts on anomalous behavior, suspicious patterns, and indicators of compromise
Model performance and data drift detection to track accuracy and react to drift
Fairness and bias detection with configurable thresholds across population groups
Monitoring, visibility, and protection for AI agents in enterprise workflows
Fortune 500 enterprises across security, audit, governance, product, and engineering teams
Discovers and governs agents running on endpoints, cloud, and private data centers
Streams security findings into existing SOC tools for real-time guardrails and alerts
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
Documented product formats, platforms and official distribution destinations. Availability can vary by region and plan.
A concise history of software releases and material product changes. Events appear only when a dated source supports what changed.
Showing the newest updates and meaningful milestones. Open an entry for its summary and source.
Arthur concluded its six-part series with guidance on enterprise agent governance: using frameworks with out-of-the-box telemetry, thorough instrumentation, demonstrable evals and guardrails, and clear ownership for compliance review.
View source [18]Arthur documented pre-LLM and post-LLM guardrails, including PII redaction, prompt injection detection, hallucination detection, and self-correction loops as first-class execution logic for production agents.
View source [19]Arthur documented its approach to prompt, RAG, and end-to-end agent experiments with supervised evals for regression prevention, framed as part of the Agent Development Lifecycle (ADLC).
View source [20]Facts, answers, structured details, milestones and primary resource links cite this shared ledger. Each external page appears once; release tags from the same GitHub project are grouped under one release history.
These tools share a workflow, capability or audience with Arthur AI. They may complement it rather than replace it, and are not presented as integrations or endorsements.
Integrates with agentic workflows to provide observability, monitoring, and protection against dangerous outputs
ArthurAI, Inc.
U.S.-headquartered company that transfers, stores, and processes personal information in the U.S. and other international locations
Arthur Evals Engine is a free, open-source toolkit
Real-time AI model evaluation engine
Configures tracing, prompt management, evals, and experiments for AI apps and agents
Evaluate models across accuracy, fairness, bias, and toxicity
Integrates with existing ML pipelines and LLM applications
Self-hosted in user's own environment
Requires Docker to run
Monitors and debugs GenAI and traditional ML use cases
Native integrations with GCP and AWS, plus SIEM connections
Incorporated in 2019
Enable models to act more autonomously while guaranteeing they work well
Centralized ML model observability platform
Real-time alerting for performance anomalies and drift
Drift detection and performance tracking across ML models
Central dashboards providing full visibility across all ML projects
Custom monitoring framework supporting custom metrics and project-specific monitoring needs
ML observability for cybersecurity threat detection and managed detection and response (MDR)
MLOps covering fast deployment, retraining, monitoring, and governance of ML models
Data science and ML/MLOps teams operating production machine learning models
Centralized platform deployed to replace fragmented, custom-built in-house monitoring setups
Tailored integration with internal data environments, workflows, and model constraints
Hands-on engineering support and co-development of customer-specific features
Integrations implemented in accordance with customer security standards
AI governance control plane for enterprise AI · Discovers agents across endpoints, cloud, and private data centers · Step-level observability traces of reasoning, tool calls, retrieval, and handoffs
AI governance control plane for enterprise AI
