Ensure enterprise integrity with Agent Eval service.
Stop worrying about AI reliability. Lyzr AgentEval is the comprehensive evaluation framework that ensures your enterprise AI agents are accurate, safe, and ready for deployment.
- Eliminate Factual Inaccuracies
- Prevent Toxic Outputs
- Maintain Contextual Relevance
Trusted by leaders: real-world AI impact.
From AI risk to reliable AI performance
Unreliable AI agents expose your business to misinformation, brand damage, and operational failure. AgentEval provides the rigorous testing and validation needed to transform unpredictable AI into a trustworthy, high-performing asset.
Verify Factual Accuracy
Our truthfulness feature cross-references agent outputs against verified data sources using advanced HybridRAG technology.
Control Harmful Content
Implement our deterministic, ML-powered toxicity controller to detect and mitigate offensive or inappropriate content effectively.
Assess Contextual Understanding
Evaluate how well agent responses align with the context of a query, ensuring coherent and relevant interactions.
Confirm Logical Groundedness
Ensure agent outputs are supported by verifiable data and sound logical reasoning, not hallucinations.
What's live in action
From months of tuning to minutes of trust
Deploy faster, reduce costs, and achieve superior performance by embedding evaluation directly into your workflow.
- 40%
Improvement in AI agent response accuracy and effectiveness, leading to better user experiences and business outcomes.
- 30%
Reduction in AI agent development and deployment times by catching issues early and automating quality assurance.
- 25%
Decrease in ongoing maintenance and moderation costs by building reliable, self-sufficient agents from day one.
- 100%
Confidence in deploying secure, compliant, and trustworthy AI agents that protect your brand and your users.
A comprehensive toolkit for AI integrity
AgentEval provides everything you need to build enterprise-grade AI agents that you can trust completely.
- HybridRAG Powered Truthfulness
- Deterministic Toxicity Controller
- Automated Prompt Optimizer
- Built-in PII Redaction
Hear it from the customers
Frequently asked questions
AgentEval uses HybridRAG technology to cross-reference AI-generated content against verified databases and enterprise knowledge graphs, ensuring all outputs are factually correct.
Unlike competitors who use LLMs for moderation, we use a more reliable, deterministic Machine Learning model specifically trained to detect and mitigate harmful, offensive, or inappropriate content.
Yes. AgentEval is designed as an inbuilt feature of Lyzr agents for seamless integration, allowing you to embed robust evaluation directly into your existing development and deployment pipelines.
It employs sophisticated algorithms for fact-checking, analyzing semantic consistency, and flagging potential inconsistencies or false statements in real-time.
Our comprehensive suite addresses all pillars of AI integrity: truthfulness, groundedness, context relevance, answer relevance, and toxicity, providing a 360-degree view of agent performance.
By combining vector-based similarity search with structured knowledge graph queries, it provides richer contextual information, leading to more accurate and relevant answers.
AgentEval can leverage a combination of trusted public databases and your own proprietary, internal knowledge bases to ensure maximum accuracy and relevance.
Our 'Groundedness' feature traces the agent's reasoning process from start to finish, verifies the sources used, and evaluates the logical consistency of its arguments.
Our customers typically see up to a 40% improvement in response accuracy and a 30% reduction in deployment time, leading to a faster and higher ROI.
It is a purpose-built ML model, not a general-purpose LLM. This makes it more deterministic, reliable, and faster at identifying and filtering toxic content before it reaches your users.
Stop guessing. Start deploying with confidence.
Equip your teams with the tools to build trustworthy, safe, and high-performing AI solutions.
