How to Get the Most Out of Azure Agentic AI Services?

One of the biggest advantages of Azure Agentic AI is its ability to act as a central orchestrator for multiple systems.

For example, NTT DATA used Microsoft Fabric data agents with Azure AI Agent Service to let employees query enterprise data naturally and get role-specific insights. Fabric handled domain knowledge, while Azure agents planned and executed tasks across HR, sales, and operations.

The result: faster decision-making, 50% quicker time to market, and higher productivity.

Observability is the backbone of reliable agentic AI. Azure AI Foundry makes this practical with five key practices:

1. Pick the Right Model:

Use benchmark-driven leaderboards to compare models by quality, cost, and safety. This ensures the agent starts with the best foundation for your use case.

2. Continuous Evaluation:

Assess agents in development and production on intent resolution, task adherence, tool call accuracy, and response completeness.

This keeps outputs accurate, safe, and aligned with user intent.

3. Integrate with CI/CD:

Embed evaluations in your CI/CD pipelines (GitHub Actions or Azure DevOps). Every code change is automatically tested, helping catch regressions before deployment.

4. Red Teaming for Safety:

Use Azure AI Red Teaming Agent to simulate adversarial attacks, uncover vulnerabilities, and validate multi-agent workflows before production.

5. Production Monitoring:

Monitor live traffic with tracing, evaluations, and alerts via Azure Monitor, Application Insights, and Workbooks. This ensures real-time detection of drift, regressions, or performance issues.

Some companies are using “reflection” patterns where agents evaluate their own outputs and correct errors before taking action.

This approach reduces dependency on humans for oversight and increases efficiency.

For example, an agent reviewing contracts can flag inconsistencies, re-query for missing context, and generate a cleaner output without human intervention.

The saying “garbage in, garbage out” holds for agentic AI.

Agents rely on embeddings, vector stores, and structured data to make decisions. Poor data quality directly affects accuracy and trustworthiness.

Best practices:

✔️ Preprocess documents with clean chunking and metadata tagging.

✔️ Use vector indexes like Azure Cognitive Search to store embeddings efficiently.

✔️ Regularly refresh data to keep agents aligned with the latest information.

A prime example of this is KPMG Australia’s use of Azure AI Foundry to automate audit processes.

They developed a solution named KymChat, which leverages Azure’s natural language processing capabilities to analyze and extract data from vast amounts of financial documents.

To get the most from Azure Agentic AI Services, build multi-agent systems that scale, stay reliable, and remain compliant.

Microsoft Agent Framework simplifies orchestration, integrates any API via OpenAPI, enables cross-agent collaboration with Agent2Agent, and connects tools dynamically using MCP.

Customers using these features include Commerzbank, Citrix, TCS, Sitecore, and Elastic, all scaling multi-agent AI with governance, observability, and reliability.

Azure Agentic AI, powered by Azure AI Foundry, enables the creation of intelligent agents that can autonomously perform tasks, make decisions, and interact with users or systems. Unlike traditional AI models that typically respond to inputs, agentic AI agents can initiate actions, manage workflows, and adapt their behavior based on context and objectives.

Azure AI Foundry offers over 1,400 built-in connectors for seamless integration with enterprise systems like SharePoint, Microsoft 365, and various SaaS applications. This allows agents to access and interact with data across your organization, facilitating automation and enhancing decision-making processes.

To ensure the reliability and safety of AI agents, consider the following best practices:

➡️ Modular Design: Break down complex tasks into smaller, reusable components to enhance maintainability and scalability.

➡️ Observability: Implement monitoring tools like Azure Monitor and Application Insights to track agent performance and detect anomalies in real-time.

➡️ Security: Utilize Azure’s Role-Based Access Control (RBAC) and Managed Identity features to enforce strict access controls and protect sensitive data.

➡️ Adversarial Testing: Regularly test agents for potential vulnerabilities and biases to ensure robust and ethical behavior.

Azure AI Foundry provides options for data storage and encryption to meet compliance requirements:

➡️ Data Storage: Choose between Microsoft-managed or customer-managed storage accounts for your agent data.

➡️ Encryption: Support for Customer-Managed Keys (CMK) ensures that you retain control over encryption keys for data at rest.

➡️ Data Residency: Data is stored in the same region as your Azure AI Foundry endpoint, aligning with Azure’s data residency policies.

Additionally, Microsoft does not use customer data for training models, adhering to Responsible AI standards.

Performance and cost are influenced by factors such as agent complexity, data volume, and integration requirements:

➡️ Performance: Utilize efficient models and optimize agent workflows to minimize latency and resource consumption.

➡️ Cost: Costs are incurred based on Azure AI services, Azure Machine Learning, and other Azure resources used within Azure AI Foundry.

It’s recommended to monitor usage and optimize resource allocation to manage costs effectively.

1️⃣ Azure AI Foundry: A unified platform for building, orchestrating, and managing AI agents. Provides the tools and frameworks necessary to develop intelligent, multi-step agents integrated with enterprise systems.

2️⃣ Azure AI Agent Service: A managed service that enables enterprises to create, deploy, and operate AI agents capable of autonomous decision-making, multi-step task execution, and interaction with external systems.

3️⃣ Agentic Retrieval: A method where AI agents autonomously query and retrieve relevant information from external or internal sources, enabling grounded decision-making and reducing hallucinations in outputs.

4️⃣ Observability: The practice of monitoring AI agent performance, actions, and outcomes in real-time. Includes tracing, logging, metrics, and alerts to maintain reliability, performance, and trust.

5️⃣ Reflection Pattern: A design approach where AI agents analyze their own outputs, identify errors, and autonomously refine their reasoning or actions to improve overall quality and reliability.