Use decision models with Amazon Bedrock AgentCore Evaluations

Evaluating autonomous AI agents in production remains one of the trickiest bottlenecks for software engineering teams. When you deploy LLM-driven systems, standard unit tests fall short. You need robust evaluation frameworks to measure reasoning accuracy, hallucination rates, and task completion. While foundation models like Claude 3.5 Sonnet and OpenAI o3 excel at open-ended generation, some agent-evaluation questions are narrow, highly repeatable, and binary. This is where decision models shine.

In this deep-dive tutorial, we will explore how to connect two specialized decision models—Jev and Strands Decider—to Amazon Bedrock AgentCore Evaluations. By implementing code-based evaluators, engineering teams can automate release gating and ensure that their agent pipelines meet strict compliance and functional benchmarks before reaching production.

The Problem with Open-Ended LLMs in Agent Evaluation

When building complex agentic workflows using frameworks like LangChain or AutoGen, developers often rely on a powerful LLM to judge the output of a smaller task-specific agent. This approach, known as LLM-as-a-judge, introduces high latency, unpredictable token costs, and occasional variance in judgment.

Consider a scenario where an enterprise agent must verify whether a customer interaction strictly adheres to a regulatory compliance clause. Asking a massive foundation model every single time is expensive and slow. Decision models offer a deterministic, highly efficient alternative for narrow, repeatable evaluation criteria. By routing these evaluation checks through an optimized infrastructure provider like n1n.ai, developers can achieve rapid execution speeds with Latency < 50ms, drastically accelerating the feedback loop in CI/CD pipelines.

Architecture Overview: Bedrock AgentCore and Decision Models

Amazon Bedrock AgentCore Evaluations provide a structured harness for testing agent behaviors across various scenarios. However, out-of-the-box evaluators are often generic. To customize evaluations for domain-specific business logic, we use code-based evaluators that query external decision engines.

Here is how the components interact:

1. Agent Test Run: Amazon Bedrock triggers a set of test traces for your deployed agent.
2. Code-Based Evaluator: A custom Python script intercepts the trace outputs.
3. Decision Model Integration: The script queries Jev or Strands Decider via low-latency API endpoints (often aggregated through n1n.ai for multi-model fallback support).
4. Release Gate: The binary or scored output dictates whether the build passes or fails the deployment pipeline.

Step-by-Step Implementation Guide

Let us build a production-ready code-based evaluator in Python that integrates a decision model into your Bedrock AgentCore evaluation loop.

Step 1: Setting Up the Environment

First, ensure you have the required AWS SDK and helper libraries installed. We will also configure our API connection for high-throughput evaluation queries.

pip install boto3 requests pydantic

Step 2: Defining the Decision Model Evaluator Class

We will create a Python class that conforms to the interface expected by Amazon Bedrock AgentCore custom evaluators. This class will send payloads to our decision models (Jev and Strands Decider).

import os
import requests
from typing import Dict, Any

class DecisionModelEvaluator:
    def __init__(self, api_endpoint: str, api_key: str):
        self.api_endpoint = api_endpoint
        self.headers = \{
            "Authorization": f"Bearer \{api_key\}