How to Build a Safe and Trustworthy AI Support Agent with Amazon Bedrock AgentCore

AI agents are quickly moving from cool demos to real business tools. But once you let an AI agent talk to customers or connect to your systems, one big question comes up:

Can I trust it?

In this post, we’ll walk through a real-world use case and show how Amazon Bedrock AgentCore helps you build AI agents that are useful, controlled, and safe to run in production.

An AI Customer Support Agent

Imagine you want an AI agent to help with basic customer support, like:

• Checking order status
• Resetting passwords
• Answering common questions
• Creating support tickets

You want it to reduce workload for your human agents, but you don’t want it doing anything risky.

Rules

• No refunds
• No access to payment details
• No changing customer data
• Escalate to humans when unsure
• Be polite, accurate, and helpful

This is exactly the kind of problem Amazon Bedrock AgentCore is designed to solve.

High-Level Architecture

The Agent Brain (Amazon Bedrock)

At the center is Amazon Bedrock, which provides the large language model (LLM).

This is what allows the agent to:

• Understand customer questions
• Reason about what to do next
• Respond in natural language

But the model does not act on its own because AgentCore is in charge of what happens next.

Controlling What the Agent Can Do (Policy Controls)

This is where trust starts.

Before the agent can call any tool or API, AgentCore checks a policy.
If the policy says “no,” the action is blocked.

Example of what the Agent Is Allowed to Do:

• Look up order status
• Reset passwords
• Create support tickets

Example of what the Agent Is NOT Allowed to Do:

• Issue refunds
• Access payment systems
• Modify customer accounts

Example of a Cedar Policy

Cedar is AWS’s policy language used by AgentCore.

Here’s an example:

permit (
  principal,
  action in [
    Action::"getOrderStatus",
    Action::"resetPassword",
    Action::"createSupportTicket"
  ],
  resource
);

And here’s a deny rule for sensitive actions:

forbid (
  principal,
  action in [
    Action::"issueRefund",
    Action::"accessPaymentInfo"
  ],
  resource
);

If the agent tries to refund an order it gets Blocked.
If it tries to access card data it gets Blocked.
The agent is forced to choose a safe alternative, like escalating to a human.

How Actions Flow at Runtime

Here’s what happens when a customer asks a question:

1. user asks a question
2. agent decides what action to take
3. agent checks cedar policy
4. if allowed, then agent calls endpoint
5. if denied, then agent adjusts response

This makes the agent’s behavior predictable and auditable.

Making Sure the Agent Is Doing a Good Job (Evaluations)

AgentCore supports episodic memory, which lets the agent learn from experience.

For example:

• If users often get confused after a password reset, the agent can add clearer steps.
• If certain issues always end in escalation, the agent can do that earlier.

This improves the experience without retraining the model.

Natural Conversations (Real-Time Streaming)

With bidirectional streaming, the agent can:

• Respond faster
• Be interrupted mid-sentence
• Support voice conversations

This makes it feel less robotic and more human, especially for live support.

Conclusion

AI Agents are no longer just experiments, they are becoming real teammates in customer support, operations, and decision-making. But for it to be truly useful in production, it must be predictable, safe, and measurable.

By combining clear policy controls, continuous quality evaluations, and built-in memory, AgentCore gives you confidence that your AI agent will:

• Only do what it’s allowed to do
• Deliver consistent, high-quality responses
• Improve over time without losing control

Instead of relying on hope or manual oversight, you define the rules upfront and verify performance continuously.