A guide to managing AI agents effectively

Based on research from Gartner, 33% of enterprise software will offer agentic AI by 2028.

These AI agents are expected to help users save time, avoid countless tedious tasks, and access timely and actionable insights.

But without effective measures in place, enterprise software companies' agents can leak data or perform the wrong set of tasks, leading users to avoid them.

To that end, we’ll walk you through how AI agent management works, why it’s critical, and how you can perform it.

What is AI agent management?

It's a combination of proactive and reactive measures to ensure AI agents operate securely, reliably, and in alignment with organizational policies. This involves enforcing governance rules, monitoring performance, and addressing issues as needed.

AI agent management is typically handled through an AI agent management platform: a centralized system that helps organizations securely integrate AI agents with tools from Model Context Protocol (MCP) servers, and monitor and manage tool calls.

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The core components of managing AI agents

AI agent management includes several components, which can vary slightly depending on the platform you’re using.

Here are just a few core items:

• A dashboard to monitor your agents’ activities holistically. This includes insights like the tool calls your agents have made over a set period of time, the connectors that are active for certain users, the rule violations that have recently taken place, and so on

• Customizable rules to determine how agents interact with certain data types. This includes blocking specific data from being shared, allowing sharing with redactions, or allowing sharing with mandatory logging and alerts. You can also apply these rules to particular agents, users, tools, or connectors before tool calls are executed

• Alerts when rules are violated. These alerts should include all of the details necessary to troubleshoot a rule violation, such as the date, tool name, and data type compromised

• Fully-searchable logs to get visibility on agents’ activities and troubleshoot issues. These logs can include the user who invoked a tool call, the arguments used and returned from the call, the date it happened, and more 

• A testing suite to pressure test your tools. You can test your AI agents with any prompt across large language models (LLMs), tools, connectors, and more

• Ability to connect your AI agents with a wide range of connectors. These connectors and associated tools can be pre-built or created by users

Why AI agent management is important

It comes down to several factors:

• Prevents data leaks: AI agents can unintentionally expose sensitive information (e.g., social security numbers) through hallucinations. They can also be manipulated by malicious actors using techniques like prompt injection to extract confidential data

Effective agent management mitigates these risks by enforcing authentication, restricting certain behaviors, alerting you when an agent shows signs of compromise, and triggering predefined remediation workflows.

• Helps agents call the right tools: By providing comprehensive and descriptive tools, AI agent management platforms can help your AI agents consistently make the right tool calls, regardless of the prompts they receive

• Supports pre-built connectors: Agent management platforms often come with a large library of pre-built connectors and tools, enabling fast integrations without the need to stand up and maintain third-party MCP servers. This also reduces your reliance on external tools that may be insecure or inconsistently maintained 

• Provides visibility on potential issues during testing: Even if you’re confident your agents will make the right tool calls, unexpected edge cases can cause failures. AI agent management platforms let you safely test agents in controlled environments, helping you uncover and fix these issues before they reach production

Related: Why AI agent authentication is critical

Best practices for managing AI agents

To help you manage AI agents effectively, you should adopt the following best practices.

Establish collections of connectors and tools that map to use cases

As AI agents expand across your organization or platform, the number of connectors and tools they rely on can quickly grow complex. Without structure, agents may access tools they don’t need, duplicate functionality, or—worse—call unvetted and insecure resources. 

To that end, organize connectors and tools into collections that map directly to business use cases.

For example, if you’re building a customer support agent, you can give it access to a set of connectors and tools that allow it to identify product bugs (e.g., in Linear), create and update issues (e.g., in Jira) and deliver updates to the relevant stakeholders (e.g., in Slack).

Test tools across every potential prompt

You can likely guess the majority of prompts for using an AI agent, but there may be unexpected ones that can lead to failures or data leaks.

To account for every prompt imaginable, you can:

• Analyze the prompts used for similar agents you’ve built (assuming they exist) 
• Invite a diverse group of testers across teams to experiment with the agent and see the prompts they use 
• Use an AI chatbot like ChatGPT to generate edge-case or adversarial prompts that mimic how real users—or attackers—might try to push your agent beyond its expected use

Once you have a handle on all of the potential prompts a given agent may receive, you can add and evaluate each through your agent management platform.

Related: Best practices for securing AI agents

Adopt a platform that supports all of your agent management needs

Merge Agent Handler offers the most complete AI agent management platform. 

It provides everything you need to securely connect and control your agents—such as prebuilt connectors, Tool Packs, least-privilege identities, policy-based rules and approvals, and fully-searchable logs. 

On top of that, it includes an evaluation suite and Connector Studio, enabling you to move fast while maintaining security, auditability, and enterprise-grade scale.

Start using Merge Agent Handler for free by creating an account!