Enterprise AI Agents: Best Practices with Amazon Bedrock AgentCore

Building a proof-of-concept AI agent is easy. Scaling it for enterprise use—where reliability, security, and accuracy are paramount—is a completely different challenge. Too many initiatives stall because the "prototype" agent hallucinates or fails to handle complex, multi-step workflows.

In this guide, we'll explore best practices for building robust enterprise agents using Amazon Bedrock AgentCore. We'll use MediAssist AI as our case study—a healthcare automation system designed to handle patient triage, scheduling, and follow-ups without compromising data privacy or accuracy.

1. Start Small & Define "Non-Goals"

For MediAssist AI, the temptation is to build a "General Hospital Assistant." This is a trap. Instead, we define a narrow scope: Outpatient Scheduling Support.

Equally important is defining what it should not do.

2. Build a Deliberate Tooling Strategy

Your AI is only as smart as the tools you give it. Vague tool definitions lead to hallucinations. You must treat tool descriptions as code documentation for the LLM.

// BAD: Ambiguous and confusing for the Agent
{
    "name": "get_slots",
    "description": "Finds times for doctors."
}

// GOOD: Amazon Bedrock AgentCore Standard
{
    "name": "find_available_appointment_slots",
    "description": "Retrieves open appointment slots for a specific department and date range. returns ISO 8601 timestamps. Requires 'department_id' (e.g., cardio, ortho) and 'date' parameters.",
    "parameters": {
        "department_id": { "type": "string", "description": "The medical department code." },
        "start_date": { "type": "string", "description": "Format YYYY-MM-DD" }
    }
}

3. Decompose with Multi-Agent Systems

Instead of one giant "Super Agent" that tries to know everything (and fails), use AgentCore to orchestrate specialized agents.

• The Triage Agent: Its only job is to understand the patient's intent. "Do you need a checkup or is this urgent?" It creates a structured context.
• The Scheduling Agent: Takes the context ("Cardiology", "Non-urgent") and purely focuses on calendar logistics. It doesn't know about medical symptoms, only time slots.
• The Supervisor (Router): Sits on top, managing the handoff. It ensures the Scheduling Agent gets the patient ID collected by the Triage Agent.

4. Instrument & Evaluate from Day One

You cannot improve what you cannot measure. Use Amazon CloudWatch integrated with Bedrock to trace reasoning steps.

METRIC                  TARGET      DESCRIPTION
-----------------------------------------------------------------------------
Tool Selection Accuracy  > 95%       Did it call 'find_slots' or 'get_info'?
Argument Extraction      > 98%       Did it correctly parse "next Tuesday"?
Refusal Accuracy         100%        Did it refuse to prescribe meds?
Latency (P95)            < 3s        Is it fast enough for chat?

Conclusion

Enterprise AI isn't about magic; it's about discipline. By defining clear boundaries for MediAssist AI, creating precise tools, and using a multi-agent architecture, we turn a cool demo into a reliable business asset. Amazon Bedrock AgentCore provides the infrastructure—it's up to you to provide the structure.