Agent Evaluation

Agent Evaluation is an enterprise-ready solution built on Microsoft Azure that provides comprehensive evaluation for end-to-end AI solutions—covering the model layer, agent orchestration, and full AI-driven workflows. Designed for enterprises adopting AI at scale, it ensures systematic testing, compliance, and observability across every stage of the AI lifecycle.

By integrating an Evaluation Orchestrator Agent with modular evaluator agents, it validates models, agents, and complete workflows. MCP sandbox servers enable safe tool-call validation, while a Context Orchestrator (Redis, Cosmos DB, AI Search, Graph RAG) ensures grounding and memory. Langfuse observability delivers full transparency, traceability, and actionable dashboards for enterprise AI operations.

Key Benefits

Holistic Evaluation: Validates models, agents, and end-to-end pipelines, not just isolated components.

Automated AI Quality Checks: Detects hallucinations, bias, safety issues, latency, and fairness gaps.

Safe Tool Testing: Sandbox MCP connectors ensure secure validation of APIs and external tools.

Enterprise Observability: Langfuse and Azure Monitor provide detailed traceability and monitoring.

Azure-Native Deployment: Scalable, secure orchestration with AKS, Cosmos DB, Redis, and AI Search.

Responsible AI Compliance: Built for audit-ready evaluation with fairness, safety, and governance controls.

How It Works

Agent Evaluation integrates evaluator agents with an orchestrator agent deployed on Azure Kubernetes Service (AKS).

• Model Evaluation: LLMs, fine-tuned, and multimodal models tested for factuality, efficiency, bias, and hallucinations.

• Agent Evaluation: Tool-using and multi-step agents validated for correctness of tool usage, reasoning chains, and task completion.

• Workflow Evaluation: End-to-end pipelines—including retrieval, orchestration, and user-facing results—tested for performance, compliance, and safety.

MCP sandbox servers validate tool calls in a controlled environment, while Redis, Cosmos DB, and Graph RAG ensure contextual grounding. Langfuse observability integrates with Azure Monitor to provide transparent metrics, dashboards, and compliance logs.

Business Impact

Improved Trust: Ensures reliable, transparent, and responsible AI adoption.

Reduced Risk: Identifies compliance and governance gaps before deployment.

Operational Efficiency: Automates regression testing across complex AI workflows.

Scalable Validation: Enables continuous evaluation of AI across enterprise use cases.

Ideal for

• MLOps & DevOps Teams → Automate regression testing for AI models and workflows.

• Compliance & Risk Officers → Enforce Responsible AI standards with audit-ready logs.

• Product & AI Leaders → Compare and validate AI solutions at scale before rollout.

• Engineering Teams → Validate orchestration, integrations, and user-facing AI reliability.

Industries

Agent Evaluation benefits enterprises deploying AI across highly regulated and performance-driven industries, including:

• Finance → Regulatory compliance and bias-free decisioning.

• Healthcare → Safety and fairness validation for clinical AI.

• Retail → Reliable AI-driven personalization and recommendations.

• Telecom → Scalable evaluation of customer-facing AI services.

• Manufacturing → Secure orchestration and workflow validation across production systems.