Product Introduction
Janus is an AI agent testing platform designed to identify performance failures, policy violations, and reliability issues through large-scale simulated interactions. It employs custom AI-generated user populations to stress-test conversational agents across text and voice interfaces while providing quantitative metrics for improvement. The system runs thousands of parallel simulations to uncover weaknesses that might escape traditional testing methods.
The core value lies in its ability to prevent operational risks by systematically detecting three critical failure categories: factual inaccuracies (hallucinations), policy breaches, and technical tool execution errors. Through automated evaluations and custom test scenarios, Janus enables enterprises to deploy AI agents with measurable confidence while maintaining compliance with organizational rules and external regulations.
Main Features
Hallucination Detection tracks instances where AI agents generate fabricated information through pattern recognition in output consistency and cross-referencing with verified knowledge bases. The system measures hallucination frequency trends over time and pinpoints specific conversation contexts where inaccuracies occur, providing timestamped logs with confidence scores for each detected instance.
Policy Enforcement Monitoring allows users to define custom rule sets using natural language or structured criteria, automatically flagging violations in real-time during simulated interactions. This feature supports multi-layered policy checks including content moderation, data handling compliance, and operational boundary enforcement through continuous pattern matching across all agent responses.
Tool Execution Diagnostics monitor API call success rates, function output validity, and integration point reliability across connected systems like Vector DBs, web search modules, and code execution environments. The platform generates failure heatmaps showing error frequency per tool/service and identifies systemic issues in orchestration workflows through dependency chain analysis.
Problems Solved
Janus addresses the critical challenge of undetected AI agent failures that can lead to regulatory penalties, brand damage, and operational disruptions in production environments. Traditional monitoring often misses complex failure patterns that only emerge during specific interaction sequences or under particular query conditions.
The platform primarily serves AI engineering teams responsible for deploying and maintaining enterprise-grade conversational agents across industries like banking, healthcare, and customer service. Secondary users include compliance officers needing audit trails and product managers requiring performance benchmarks.
Typical applications include pre-launch stress testing of new agent versions, continuous compliance monitoring for regulated industries, and post-incident root cause analysis through simulation replay. Financial institutions use it to validate investment advice bots, while healthcare providers employ it to test patient triage systems against HIPAA violations.
Unique Advantages
Unlike basic testing frameworks, Janus combines behavioral simulation (through AI-generated user personas) with technical instrumentation, enabling detection of both conversation-level and system integration failures. Competitors typically focus on either functional testing or content moderation, but not both in integrated workflows.
The platform's Soft Evaluation Engine uses fuzzy matching algorithms to detect high-risk responses that contain subtle policy violations or emerging bias patterns, even when responses technically pass literal rule checks. This probabilistic assessment layer complements deterministic rule-based evaluations for comprehensive risk management.
Competitive differentiation comes from the platform's dual-layer testing architecture that simultaneously runs load testing (volume) and adversarial testing (complex edge cases). This enables performance benchmarking under realistic operational conditions while maintaining attack-surface analysis capabilities typically found in security-focused tools.
Frequently Asked Questions (FAQ)
How does Janus detect hallucinations in AI agent responses? The system employs a combination of output consistency checks across multiple test runs, semantic comparison against approved knowledge sources, and statistical anomaly detection in factual claims. High-risk responses undergo manual review sampling with integrated annotation tools for model retraining.
What types of AI agents can be tested using the platform? Janus supports text-based chatbots, voice assistants, and multi-modal agents through REST API integrations. The system can test agents built on all major LLM platforms (GPT, Claude, Gemini) and custom models, provided they have programmatic interaction endpoints.
Can we create custom rules for industry-specific compliance requirements? Yes, the Rule Studio allows creating nested compliance templates using natural language descriptions or importing legal/regulatory documents for automated rule extraction. The system maintains version-controlled policy sets with audit trails for compliance reporting.
How does the simulation engine generate realistic test scenarios? The platform uses adversarial AI models trained on historical interaction data to create edge-case scenarios, combined with demographic-based persona generators that mimic target user populations. Test scenarios can be weighted by real-world occurrence probability for accurate risk prioritization.
What integration capabilities exist for existing monitoring tools? Janus provides native integrations with popular AI orchestration platforms (LangChain, LlamaIndex), observability tools (Datadog, New Relic), and ticketing systems (Jira, ServiceNow). All findings export via standardized formats (JSON, OpenTelemetry) for custom pipeline implementations.
