Building Trustworthy Agents: Guardrails and Human Oversight

When we build agents that can take action in the world, not just answer questions, trust becomes a design requirement rather than a nice-to-have. It’s one thing for a chatbot to give the wrong answer in a conversation. It’s another for an agent to cancel a user’s subscription, send money to the wrong account, or expose private data. As agents gain more autonomy, we also take on greater responsibility for their behavior.

In this lesson, we’ll explore how to make agents safer, more predictable, and easier to monitor. We’ll start with guardrails, which are systems designed to catch risky behavior before it causes harm. Then we’ll look at human oversight as the final layer of control, especially in high-stakes or ambiguous situations.

Building trustworthy agents does not mean giving up flexibility. It means designing for safety while maintaining utility, so agents can be both helpful and responsible.

By the end of this lesson, you will be able to:

• Explain why trust and safety are essential in agentic systems.

• Identify different types of guardrails and what risks they help prevent.

• Apply best practices for designing guardrails that balance safety with flexibility.

• Recognize when and where human oversight is necessary in agent workflows.

• Understand how to combine guardrails and human review into a layered safety strategy.

Guardrails

Guardrails are safety mechanisms that wrap around agent behavior. They do not tell the agent how to solve a problem; instead, they help ensure the solution stays within safe, expected limits. In practice, this means checking inputs and outputs, filtering unsafe content, and placing constraints on tool use or API calls. Guardrails operate alongside the model, rather than within it. Guardrails are implemented as distinct modules that intercept data at key points in the agentic loop:

• Input guardrails: Applied before the LLM receives an input (e.g., user query, sensor data).

• Output guardrails: Applied after the LLM generates a response or plan.

• Tool-use guardrails: Applied before a tool is executed.

They are especially important in agentic systems because agents do more than generate responses. They also take action. Guardrails help us catch the moments when those actions might go wrong, violate policy, or put users at risk.

Next, we’ll look at specific types of guardrails used in real systems and explore how they help reduce harm, enforce rules, and build trust.

Types of guardrails

Not all risks are the same, and not all guardrails function in the same way. Depending on the agent’s purpose, output format, and level of autonomy, we can apply different categories of safeguards.

Here are some of the most commonly used types of guardrails:

Contextual grounding checks

One of the most common failure modes for agents is drifting off topic or producing content unrelated to the task. A more critical concern is when an agent generates information that is not supported by the provided context, leading to hallucinations. Contextual grounding checks help ensure that an agent’s actions or outputs remain focused on the user’s intent, and are factually supported by its knowledge sources. For example, if an agent is asked to summarize a document but instead generates commentary, or provides information not present in the document, that’s a relevance or grounding failure. This can erode user trust or introduce confusion.

Following are some ways to implement contextual grounding checks:

• Use retrieval-based grounding (like RAG) to keep responses anchored to source content, explicitly preventing the agent from fabricating information.

• Add prompt constraints that remind the model to stay on topic and only use provided context.

• Apply post-response checks using classifiers or rule-based filters to assess topic alignment and factual consistency against source material.

This type of guardrail is especially important in agents that work with long documents, open-ended queries, or search-driven workflows.

Safety and moderation filters

When agents interact with users in natural language, there’s always a risk that harmful, offensive, or biased content might be generated. Safety and moderation filters are designed to detect and block such content before it reaches the user or triggers downstream actions.

This kind of guardrail is essential in both user-facing agents (e.g., customer ...