AI Agents, Explained (When Chatbots Start Doing Things)

Most of us are familiar with AI chatbots by now. You ask a question, the model generates a response, and the interaction ends there. Whether you're debugging code or summarizing a document, the model is primarily generating information for you to use.

AI agents take this a step further by allowing models to interact with external tools and systems. Instead of explaining how to create a Jira ticket, an agent could create one. Instead of suggesting commands to investigate a Kubernetes issue, it could retrieve logs and examine the cluster itself.

That opens up quite a few possibilities, particularly for teams managing complex infrastructure. It also introduces new considerations around security, permissions, and how much autonomy an application should have.

The Basics

An AI agent is an application that uses a model to determine and execute steps toward a goal, often by interacting with external tools. These tools might provide access to APIs, databases, monitoring systems, development environments, or other applications.

A typical agent workflow involves receiving a request, deciding which tool to use, executing an authorized operation, and processing the result. The model may then decide whether additional actions are necessary or whether it has enough information to complete the task.

For example, an infrastructure troubleshooting agent could retrieve Kubernetes pod logs, inspect recent deployments, and identify a possible cause of application failures. Depending on its permissions, it might also prepare a remediation action for an engineer to review.

Why It Exists

Traditional chatbots are useful for generating explanations and recommendations, but many operational tasks require interaction with external systems. A model might understand how to troubleshoot a database connection issue, but without access to the database or its monitoring data, it can't investigate what's actually happening.

Agent architectures connect model reasoning with application functionality. Tools expose specific operations such as searching documentation, querying databases, creating tickets, or interacting with cloud APIs. The surrounding application determines which tools are available and how their requests are executed.

Some agents operate within predefined workflows, while others dynamically determine their next steps based on previous results. In either case, the model's ability to request an action doesn't mean it should automatically have permission to execute it.

Common Pitfalls

• Excessive permissions: Agents shouldn't have unrestricted access to infrastructure or sensitive information.
• Unreliable tool results: Incorrect or incomplete API responses can affect subsequent decisions.
• Unbounded execution: Repeated model requests and tool calls can increase latency and costs.
• Missing approval mechanisms: High-impact operations may require human confirmation.
• Weak isolation: Agents executing generated code need appropriately restricted environments.
• Insufficient auditability: Teams need records of which tools were called and what actions were performed.

Why It Matters

AI agents can reduce the manual work involved in repetitive operational processes. Consider an engineering team responding to infrastructure alerts. An agent could retrieve monitoring data, check recent deployments, search previous incidents, and create a ticket containing its findings. Engineers would still decide whether to approve changes, but the initial investigation could be partially automated.

These workflows require more than an inference endpoint. An agent may need access to external APIs, credentials, persistent state, logging, and isolated execution environments. Protocols such as the Model Context Protocol (MCP) provide standardized ways for AI applications to connect with tools and external data sources.

Agents also introduce additional security considerations. Retrieved documents and tool responses may contain malicious instructions intended to influence the model's behavior, commonly referred to as prompt injection. Applications need to treat this information as untrusted and enforce permissions independently of the model's decisions.

The TAM Lens

Deploying an AI agent introduces architectural requirements beyond those of a typical chatbot. A single user request might trigger several model invocations, database queries, and external API calls before completing. Each operation contributes to latency, infrastructure utilization, and overall cost.

External service limits, authentication, and error handling also become important. An agent that depends on multiple APIs needs to handle unavailable services, expired credentials, and unexpected responses. For production deployments, it's worth establishing which operations can run automatically and which require approval. Read-only diagnostic tools generally present less risk than actions involving infrastructure changes or data deletion.

Observability should cover the entire workflow, including model requests, tool calls, execution failures, and final outcomes. If an agent modifies a configuration or creates an incorrect ticket, teams need to understand how that decision was reached and which systems were involved.

How to Stay Sane

• Start with limited permissions: Give agents access only to the tools and operations they require.
• Separate read and write actions: Apply stronger controls to operations that modify systems.
• Require approval where appropriate: Keep humans involved in high-impact decisions.
• Set execution limits: Restrict tool calls, runtime, and spending.
• Use isolated environments: Sandbox generated code and other potentially unsafe operations.
• Log agent activity: Record actions and outcomes for troubleshooting and auditing.
• Test failure scenarios: Verify how agents respond to unavailable tools, invalid responses, and permission errors.

Final Thoughts

AI agents extend language models beyond generating responses by allowing them to interact with applications and external systems. They can help automate repetitive tasks, investigate issues, and coordinate workflows across multiple services.

As agents gain access to more tools and infrastructure, permissions, isolation, observability, and operational controls become essential parts of their design. The value comes from automating useful work while maintaining control over what the agent can actually do.