Observability in Generative AI: Building Trust with Systematic Evaluation in Microsoft Foundry

Why observability matters for generative AI

Generative AI systems operate in complex and dynamic environments. Without systematic evaluation and monitoring, these systems can produce outputs that are factually incorrect, irrelevant, biased, unsafe, or vulnerable to misuse.

Observability helps teams understand how their AI systems behave over time. It enables early detection of quality degradation, safety issues, and operational problems, allowing teams to respond before users are impacted. In GenAIOps, observability is not a one-time activity but a continuous process embedded throughout development and deployment.

What is observability in generative AI?

AI observability refers to the ability to monitor, understand, and troubleshoot AI systems throughout their lifecycle. It combines multiple signals, including evaluation metrics, logs, traces, and model or agent outputs, to provide visibility into performance, quality, safety, and operational health.

In practical terms:

• Metrics indicate how well the AI system is performing
• Logs show what happened during execution
• Traces explain where time is spent and how components interact
• Evaluations assess whether outputs meet defined quality and safety standards

Together, these signals help teams make informed decisions about improving their AI applications.

Evaluators: measuring quality, safety, and reliability

Evaluators are specialized tools used to assess the behavior of generative AI models, applications, and agents. They provide structured ways to measure quality and risk across different scenarios and workloads.

General-purpose quality evaluators

These evaluators focus on language quality and logical consistency. They assess aspects such as clarity, fluency, coherence, and response quality in question-answering scenarios.

Textual similarity evaluators

Textual similarity evaluators compare generated responses with ground truth or reference answers. They are useful when measuring overlap or alignment in tasks such as summarization or translation.

Retrieval‑Augmented Generation (RAG) evaluators

For applications that retrieve external information, RAG evaluators assess whether:

• Relevant information was retrieved
• Responses remain grounded in retrieved content
• Answers are relevant and complete for the user query

Risk and safety evaluators

These evaluators help detect potentially harmful or risky outputs, including biased or unfair content, violence, self-harm, sexual content, protected material usage, code vulnerabilities, and ungrounded or fabricated attributes.

Agent evaluators

For tool‑using or multi‑step AI agents, agent evaluators assess whether the agent follows instructions, selects appropriate tools, executes tasks correctly, and completes objectives efficiently.

To align with compliance and responsible AI practices, it is important to describe these capabilities carefully. Instead of making absolute claims, language such as “helps detect” or “helps identify potential risks” should be used.

Observability across the GenAIOps lifecycle

Observability in Microsoft Foundry aligns naturally with three stages of the GenAIOps lifecycle.

1. Base model selection

Before building an application, teams must select the right foundation model. Early evaluation helps compare candidate models based on:

• Quality and accuracy for intended scenarios
• Task performance for specific use cases
• Ethical considerations and bias indicators
• Safety characteristics and risk exposure

Evaluating models at this stage reduces downstream rework and helps ensure a stronger starting point for development.

1. Preproduction evaluation

Once an AI application or agent is built, preproduction evaluation acts as a quality gate before deployment. This stage typically includes:

• Testing using evaluation datasets that represent realistic user interactions
• Identifying edge cases where response quality might degrade
• Assessing robustness across different inputs and prompts
• Measuring key metrics such as relevance, groundedness, task adherence, and safety indicators

Teams can evaluate using their own datasets, synthetic data, or simulation-based approaches. When test data is limited, simulators can help generate representative or adversarial prompts.

AI red teaming for risk discovery

Automated AI red teaming can be used to simulate adversarial behavior and probe AI systems for potential weaknesses. This approach helps identify content safety and security risks early. Automated scans are most effective when combined with human review, allowing experts to interpret results and apply appropriate mitigations.

1. Post-production monitoring

After deployment, continuous monitoring helps ensure AI systems behave as expected in real-world conditions. Key practices include:

• Tracking operational metrics such as latency and usage
• Running continuous or scheduled evaluations on sampled production traffic
• Monitoring evaluation trends to detect quality drift
• Setting alerts when evaluation results fall below defined thresholds
• Periodically running red teaming exercises to assess evolving risk

Microsoft Foundry integrates with Azure Monitor and Application Insights to provide dashboards and visibility into these signals, supporting faster investigation and issue resolution.

A practical evaluation workflow

A repeatable evaluation process typically follows these steps:

1. Define what you are evaluating for, such as quality, safety, or RAG performance
2. Select or generate appropriate datasets, including synthetic data if needed
3. Run evaluations using built-in or custom evaluators
4. Analyze results using aggregate metrics and detailed views
5. Apply targeted improvements or mitigations and re-evaluate

This iterative approach helps teams continuously improve AI behavior as requirements and usage patterns evolve.

Operational considerations

When planning observability and evaluation, teams should consider:

• Regional availability of certain AI-assisted evaluators
• Networking constraints, such as virtual network support
• Identity and access requirements, including managed identity roles
• Cost implications, as evaluation and monitoring features are consumption-based

Reviewing these factors early helps avoid deployment surprises and delays.

Conclusion

Trustworthy generative AI systems are built through continuous measurement, learning, and improvement. Observability provides the foundation to understand how AI applications behave over time, detect issues early, and respond with confidence.

By embedding evaluation and monitoring across model selection, preproduction testing, and production operation, Microsoft Foundry enables teams to make trust measurable and maintain high standards of quality, safety, and reliability as AI systems scale.

Key takeaways

• Observability is essential for understanding and managing generative AI systems throughout their lifecycle
• Evaluators help assess quality, safety, RAG performance, and agent behavior in a structured way
• GenAIOps observability spans base model selection, preproduction evaluation, and post-production monitoring
• Automated techniques such as AI red teaming help identify risks early and should complement human review
• Continuous evaluation and monitoring support reliable, safe, and evolving AI systems

Useful resources

• Microsoft Foundry Documentation : Microsoft Foundry documentation | Microsoft Learn
• Observability in generative AI: Observability in Generative AI - Microsoft Foundry | Microsoft Learn
• Transparency Note for Safety Evaluations:  Microsoft Foundry risk and safety evaluations (preview) Transparency Note - Microsoft Foundry | Mic...
• Microsoft Foundry QuickStart:  Microsoft Foundry