5 Essential Practices for Reliable AI Agents

Key points

• Agent Observability is crucial for ensuring the reliability, safety, and performance of Agentic AI systems, and it involves achieving deep visibility into the internal workings, decisions, and outcomes of AI agents throughout their lifecycle.
• Azure AI Foundry Observability is a unified solution that provides end-to-end agent observability, including evaluation, monitoring, tracing, and governance, to help teams build trustworthy and high-performing AI systems at scale.
• By following best practices such as picking the right model, evaluating agents continuously, integrating evaluations into CI/CD pipelines, scanning for vulnerabilities with AI Red Teaming, and monitoring agents in production, teams can ensure their AI agents are reliable, safe, and production-ready.

As Agentic AI becomes increasingly central to enterprise workflows, ensuring the reliability, safety, and performance of these systems is critical. Agent Observability plays a vital role in achieving this goal by providing teams with deep visibility into the internal workings, decisions, and outcomes of AI agents throughout their lifecycle. This involves continuous monitoring, tracing, logging, evaluation, and governance to ensure that agents operate ethically, safely, and in accordance with organizational and regulatory requirements.

Azure AI Foundry Observability is a unified solution that provides end-to-end agent observability, including evaluation, monitoring, tracing, and governance. This solution empowers teams to build trustworthy and high-performing AI systems at scale by providing visibility into how agents behave, make decisions, and respond to real-world scenarios across their lifecycle. With Azure AI Foundry Observability, teams can detect and resolve issues early in development, verify that agents uphold standards of quality, safety, and compliance, and optimize performance and user experience in production.

To ensure the reliability and safety of Agentic AI systems, teams should follow best practices such as picking the right model, evaluating agents continuously, integrating evaluations into CI/CD pipelines, scanning for vulnerabilities with AI Red Teaming, and monitoring agents in production. For example, Azure AI Foundry provides model leaderboards that enable teams to compare foundation models out-of-the-box by quality, cost, and performance, backed by industry benchmarks. Additionally, Azure AI Foundry integrates with CI/CD workflows using GitHub Actions and Azure DevOps extensions, enabling teams to auto-evaluate agents on every commit and catch regressions early.

By following these best practices and leveraging Azure AI Foundry Observability, teams can ensure that their Agentic AI systems are reliable, safe, and production-ready. This is critical for building trust and accountability in AI systems, as well as for maintaining the performance and user experience of these systems in production. With Azure AI Foundry Observability, teams can get started with end-to-end agent observability and ensure that their AI agents are reliable, safe, and production-ready.

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