AI Agent Deployment: Steps and Challenges

Organizations are increasingly relying on AI agents to manage tasks that once required constant human effort, such as responding to customer queries, automating workflows, or coordinating data across different systems.

While these agents can extend productivity and reduce operational load, their value is realized only when they are deployed correctly in production. Deployment ensures that an agent transitions from controlled testing to real-world environments, where it can be monitored, configured, and scaled to deliver consistent results.

Discover the essential steps to agent deployment, explore deployment options from leading companies, understand the challenges, and best practices.

What is AI agent deployment?

AI agent deployment is the process of deploying an agent that has been developed and tested in a controlled environment into production, enabling it to perform tasks, respond to queries, and interact with other systems. This process is not limited to agent installation. It also covers configuration, access control, monitoring, updates, and documentation.

A deployed agent can run on various environments, including Linux devices (via packages or binaries), Windows servers (with installers or services), macOS systems (through Homebrew or binaries), and cloud platforms such as Google Cloud and Vertex AI (using cloud-native deployment). Each environment requires a specific set of instructions for installation and configuration.

It is essential to verify that the agent engine is installed correctly, that the correct version is running, and that all required packages are available before launching the agent.

Phases of agent deployment

1. Define requirements

The first step is to begin with precise requirements. You should create a list of the tasks the agent must perform, the type of environment it will run on, and the resources it will need. At this stage, a unique identifier should be assigned so the agent can be tracked after deployment.

2. Prototype and testing

Agents are often deployed first in a test environment. During this phase, you check latency, response quality, and the agent’s ability to continue running over time. It is essential to verify that connections to data sources, models, or APIs work as intended.

3. Deployment pipeline

When moving from prototype to production, deployment is typically done through a package that contains the code, configuration, and supporting files. Examples include:

• On Linux devices, installing the package with a package manager and configuring a service file to run at startup.
• On Windows, use an installer that sets registry entries and configures the agent as a service.
• In cloud environments, such as Google Cloud or Vertex AI, you deploy the agent through a managed service, where you configure resource details, including CPU, memory, and concurrency.

4. Production launch

After the deployment is complete, you should track the status of the deployed agent. Essential steps include monitoring resource usage, checking logs, and verifying default settings. Documentation should provide details about the version, configuration, and supported features.

Deployment options and platforms

Google ADK

Google’s Agent Development Kit enables deployment to various environments, including Vertex AI Agent Engine, Cloud Run, and Kubernetes. After building an agent, you can deploy it as a container and configure its connection points.

Figure 1: Diagram showing the agent deployment steps with Google ADK.¹

Watch the video below to learn how to deploy AI agents as code services, handle latency with async and caching, and automate workflows through agent-to-agent interaction.

Vertex AI Agent Engine

With Vertex AI, deployment involves packaging requirements, defining environment variables, and configuring resource limits.

You then create an Agent Engine instance, grant the agent permissions, and obtain its resource name as a unique identifier. Once launched, the agent can be queried via endpoints and monitored for latency, errors, and response quality.²

Workflow example using Vertex AI

1. Begin by preparing the local agent and ensuring the required packages are installed.
2. Create a package that lists its dependencies and any additional files.
3. Deploy the package into the Vertex AI Agent Engine with defined resource settings.
4. Configure access permissions through service accounts.
5. Retrieve the unique identifier of the deployed agent.
6. Query the agent through its endpoint and check latency and responses.
7. Continue to track usage, add updates, and redeploy new versions as needed.

Databricks Agent Framework

Databricks provides an agent framework that enables deployment using the deploy() function. This creates an endpoint that applications can access. Features include autoscaling, version control, and access management. Monitoring is supported through logs, inference tables, and review applications that track feedback.³

LangGraph platform

LangGraph is designed for stateful, long-running agents. Deployment can be accomplished with a single action, and the platform offers persistent memory, visualization tools, and horizontal scaling capabilities.

It also supports multi-agent workflows and human-in-the-loop settings, which are essential in complex environments.

Stateful and long-running agents

For agents that need to maintain memory or continue running between tasks, additional features are required. LangGraph demonstrates how stateful deployment can work by providing:

• Persistence of memory and history across sessions.
• Visualization tools for inspecting state and workflows.
• Options for scaling horizontally to handle variable traffic.
• The ability to run tasks triggered by events rather than only direct queries.

Figure 2: One-click deployment from LangGraph with their native GitHub integration.⁴

What are the challenges of agent deployment?

Beyond typical issues such as version mismatches, long installation times, or cost control, large-scale evaluations reveal deeper risks that emerge when agents are deployed in real environments.

Prompt injection attacks

One of the most significant risks is the risk of prompt injection. Attackers craft inputs that override default instructions or system policies, causing the agent to act outside its intended scope of operation.

These attacks can be direct, where a user provides harmful input, or indirect, where malicious instructions are embedded in external data such as files, web pages, or emails. In a large public competition, indirect injections were particularly effective, leading to unauthorized actions such as deleting calendar entries or altering system settings.

Confidentiality breaches

Agents installed in production often handle sensitive data. When attackers successfully manipulate prompts, they can cause unauthorized disclosure of personal, financial, or medical information.

The competition results showed that confidentiality violations were among the most common outcomes, with attacks generalizing across different models and environments.⁵ Even when unique identifiers or strict access rules were in place, adversarial prompts were able to bypass these settings.

Conflicting objectives

Another challenge occurs when agents adopt goals that contradict their deployment policies. For example, a financial or sales agent may be manipulated into maximizing profit at the cost of violating regulatory limits. These conflicting objectives expose organizations to compliance failures and potential legal risks.

Prohibited content and actions

Agents configured with tools for creating text, modifying files, or executing system tasks can be induced to generate prohibited content or perform unsafe actions. Examples include writing malicious code, sending spam, or executing unauthorized file operations.

Since many deployed agents are connected to external systems, the consequences of such actions can be severe.

Attack transferability and universality

Security evaluations highlight that attacks are highly transferable between models. A prompt injection designed to break one agent engine often works against other models, even across providers. Universal attacks can bypass multiple safeguards with only minor changes, making it easy for adversaries to adapt exploits across a wide range of deployed agents.

Limited correlation with model size or compute

It might be expected that larger or more advanced models are more secure, but testing shows little correlation between model capability.

Even the advanced models, which run with additional inference compute, exhibited high attack success rates. This means that simply deploying more advanced models is not sufficient to improve security.

Operational concerns

In addition to security, operational challenges continue to affect deployments:

• Installation and updates: Installing large packages increases startup latency. If dependencies are not pinned to a specific version, unexpected changes can occur.
• Resource allocation: Incorrect default settings for concurrency or memory can lead to instability or excessive costs.
• Monitoring: Without detailed logs, traces, and benchmarks, teams may fail to detect unauthorized actions or subtle performance degradations.
• Multi-agent environments: When multiple agents interact, malicious instructions can propagate, resulting in cascading failures across the system.

Best practices in agent deployment configuration

When preparing an agent for deployment, careful configuration is necessary to ensure that it runs reliably, securely, and efficiently in production. The following points highlight key practices:

Define package requirements

List all package requirements clearly and pin them to specific versions. This avoids incompatibilities that can arise when default package updates introduce breaking changes. A requirements file should include every dependency that the agent engine needs to install and run.

Minimize dependencies

Keep the deployment package as lean as possible. Every unnecessary dependency increases installation time, storage use, and the risk of conflicts. By including only what is essential, you can reduce latency during agent installation and make updates more straightforward to manage.

Configure environment variables securely

Agents often need access to API keys, database connections, or encryption settings. These should be stored as environment variables and managed through secure services rather than being hardcoded in files. Secrets must be kept separate from the deployed agent’s code to prevent leaks.

Set resource limits

Defining CPU, memory, and concurrency limits helps prevent a single deployed agent from consuming excessive resources. Providing options for scaling up or down ensures that the agent can adapt to different workloads while keeping costs under control.

Grant minimal permissions

An agent should run with only the permissions it needs to complete its assigned tasks. Using the principle of least privilege reduces the attack surface. Service accounts and access policies should be checked and updated regularly to match the agent’s scope.

Monitor performance and errors

Once launched, the deployed agent must be tracked continuously. Important metrics include latency, error rates, and query volume. Logging and monitoring systems should be configured to capture enough detail to diagnose problems quickly, while also providing insight into usage patterns that can guide updates and improvements.

External Links

• 1. https://google.github.io/adk-docs/deploy/
• 2. https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/deploy
• 3. https://learn.microsoft.com/en-us/azure/databricks/generative-ai/agent-framework/deploy-agent
• 4. https://blog.langchain.com/langgraph-platform-ga/
• 5. https://arxiv.org/pdf/2507.20526