As enterprises increasingly embrace AI to automate workflows, enhance customer experiences, and streamline operations, the concept of intelligent agents has emerged as a cornerstone of modern digital transformation. On the Azure platform, agent development is rapidly evolving, thanks to innovations like the Microsoft Agent Framework and Azure AI Foundry. But what makes a good agent? And how can developers ensure their agents are not only functional but also secure, reliable, and scalable? 

This blog explores the key principles and best practices for building high-performing agents on Azure, weaving in insights from Microsoft’s latest agent development tools and frameworks. 

What Makes a Good Agent? 

A well-designed agent is more than just a chatbot—it’s a goal-oriented, autonomous system capable of reasoning, acting, and adapting. The following characteristics define a high-quality agent:

Good agents operate with a defined purpose. They don’t just respond to queries—they plan and execute multi-step tasks to achieve outcomes. This involves breaking down goals into sub-tasks and choosing the best path forward, demonstrating strategic thinking and autonomy.

2. Tool Use and Execution

Agents must be empowered to take actions – accessing external tools, APIs, and services to perform real-world tasks. For example, an agent might call a database API to update a record or trigger a workflow via Logic Apps. Notably, Azure AI Foundry’s Agent Service provides built-in integration, with over 1,400 prebuilt connectors to enterprise systems. This rich toolset means your agent can interface with everything from SAP to ServiceNow with minimal custom code, truly turning decisions into outcomes.

3. Context Retention (Memory)

Agents should maintain state and context across interactions. This memory allows them to handle complex, multi-turn conversations and make informed decisions based on prior exchanges.

4. Collaboration and Orchestration

No agent operates in isolation. Effective agents collaborate with other agents or humans, leveraging multi-agent orchestration to delegate tasks, seek approvals, or coordinate workflows. Azure’s Semantic Kernel and AI Foundry support such orchestration patterns, enhancing scalability and robustness.

5. Reliability and Trustworthiness

Enterprise agents must operate within defined security and governance boundaries. This includes respecting permissions, maintaining audit trails, and adhering to ethical guidelines. Azure’s Entra ID provides managed agent identities and role-based access control (RBAC), ensuring agents act securely and responsibly.

6. Self-Reflection and Adaptation

Agents should be capable of evaluating their own outputs, detecting errors, and refining their approach. This self-awareness enables agents to improve over time without constant human intervention, making them more resilient and effective. 

Best Practices for Agent Development on Azure 

To build agents that embody these qualities, developers should follow a set of best practices tailored to the Azure ecosystem:

1. Use a Unified Agent Platform

Leverage platforms like Azure AI Foundry’s Agent Service or Microsoft Copilot Studio to manage the full lifecycle of agent development. These platforms offer integrated tools for prototyping, deployment, monitoring, and governance, reducing the need for custom scaffolding.

2.  Adopt Open Standards

Microsoft’s Agent Framework supports protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication. These standards enable agents to share tools, context, and results across different frameworks, promoting interoperability and reducing vendor lock-in. Defining tools via OpenAPI or MCP schemas ensures portability and reusability across environments.

3. Empower Agents with the Right Tools

An agent’s capabilities are only as strong as the tools it can access. Use MCP to connect agents to enterprise data sources, wrap custom APIs as plugins, and integrate with SaaS and on-prem systems using Logic Apps. This creates a rich enterprise toolchain that agents can leverage to deliver real value.

4. Design Clear Instructions and Roles

In agentic systems, instructions define behavior. Invest in precise prompt engineering, clearly outlining the agent’s role, goals, available tools, and constraints. Treat prompts as code—version-controlled and maintained like any other software artifact. Define success criteria so agents can self-regulate and retry when needed.

5. Ensure Security, Identity, and Governance

Security is paramount. Assign each agent a unique identity using Microsoft Entra ID, enforce RBAC, and use on-behalf-of authentication to ensure agents act within user permissions. Integrate with Microsoft Purview for data governance and embed ethical and compliance checks to prevent misuse. Always design with a human-in-the-loop for critical decisions.

6. Continual Evaluation and Observability

Agent development doesn’t end at deployment. Implement robust observability using Azure Monitor and OpenTelemetry to track agent behavior in real time. Use automated agent evaluators to assess performance, safety, and coherence during development. Integrate these evaluations into your CI/CD pipeline to catch regressions early and ensure consistent quality. 

Final Thoughts 

The rise of intelligent agents marks a new era in enterprise automation. With platforms like Azure AI Foundry and the Microsoft Agent Framework, developers have powerful tools to build agents that are not only smart but also secure, scalable, and interoperable. By adhering to best practices—clear objectives, robust toolchains, strong governance, and continuous evaluation—teams can create agents that deliver real-world outcomes and adapt to evolving business needs. 

Whether you're just starting out or scaling your agentic infrastructure, Azure provides the foundation to build agents that are truly enterprise-ready. 

To learn more about how Spyglass MTG can assist you with your AI needs, reach out to info@spyglassmtg.com