How to Connect and Verify AI Agents: Complete Configuration Guide for AWS Bedrock, Azure AI, GCP Vertex & REST Agents

Connecting an autonomous AI agent to an enterprise evaluation pipeline requires rigorous protocol alignment and credential verification. Unlike static REST APIs, AI agents involve dynamic tool invocations, multi-turn state sessions, and unpredictable response formatting. When connecting your agent to the Agent Evaluation Lab, running a pre-flight Test Connection validation ensures that network routes, authentication headers, request body interpolation, and tool schemas are completely verified before creating a project.

This guide provides step-by-step connection procedures for all supported agent architectures: Tool-Calling Agents, AWS Bedrock AgentCore, Azure AI Agent Service, GCP Vertex AI Reasoning Engines, and Model Context Protocol (MCP) / Universal REST agents.

1. Tool-Calling Agents & Custom HTTP Endpoints

Custom agents built on frameworks like FastAPI, LangGraph, CrewAI, AutoGen, or LangChain typically expose an HTTP POST webhook endpoint.

Required Configuration

• Endpoint URL: Must be a publicly accessible or internal gateway HTTPS URL, such as https://api.yourcompany.com/v1/agent/chat.
• HTTP Method: Usually POST.
• Authentication Header: Format as Bearer <YOUR_API_KEY> or custom token headers such as X-API-Key: <token>.
• Request Body Template: The JSON payload must include the dynamic input placeholders:

  {
    "prompt": "{{test_input}}",
    "session_id": "{{conversation_id}}",
    "temperature": 0.2
  }

Expected Agent Response Contract

To enable automated tool-calling validation, your agent response should include the final answer text along with an array of selected tools and arguments:

{
  "response": "The current weather in Athens is 24°C and clear.",
  "tools_invoked": [
    {
      "name": "get_weather",
      "arguments": { "city": "Athens", "units": "celsius" }
    }
  ],
  "latency_ms": 340
}

2. AWS Bedrock AgentCore & AWS Lambda Endpoints

AWS Bedrock Agents manage conversational workflows, action groups, and knowledge bases using Amazon Bedrock foundation models.

Required Configuration

• Target ARN / Endpoint: arn:aws:bedrock:<region>:<account-id>:agent/<agent-id> or direct API Gateway URL https://<api-id>.execute-api.<region>.amazonaws.com/prod/invoke.
• Supported AWS Regions: us-east-1 (N. Virginia), us-west-2 (Oregon), eu-central-1 (Frankfurt), ap-southeast-1 (Singapore).
• Authentication Header: Provide AWS SigV4 signed authorization header or Bedrock API Key Bearer bedrock_ak_....
• Body Template:

  {
    "agentId": "<AGENT_ID>",
    "agentAliasId": "<AGENT_ALIAS_ID>",
    "sessionId": "{{conversation_id}}",
    "inputText": "{{test_input}}"
  }

3. Azure AI Agent Service (Foundry & Azure OpenAI Assistants)

Azure AI Agent Service integrates Azure OpenAI with persistent threads, code interpreters, and OpenAPI tool definitions.

Required Configuration

• Endpoint URL: https://<your-resource>.services.ai.azure.com/agents/v1/threads/runs or Azure OpenAI proxy https://<your-resource>.openai.azure.com/openai/deployments/<deployment>/chat/completions?api-version=2024-10-21.
• Authentication Header: Azure API Key header api-key: <AZURE_KEY> or Entra ID token Bearer eyJ0eXAi....
• Body Template:

  {
    "assistant_id": "asst_...",
    "thread": {
      "messages": [
        { "role": "user", "content": "{{test_input}}" }
      ]
    }
  }

4. Google Vertex AI Agent Builder & Reasoning Engines

Vertex AI Reasoning Engines and Agents execute Python-based agentic workflows deployed in Google Cloud Platform.

Required Configuration

• Endpoint URL: https://<region>-aiplatform.googleapis.com/v1/projects/<project-id>/locations/<region>/reasoningEngines/<engine-id>:query.
• Authentication Header: Google Cloud OAuth2 Access Token Bearer ya29.a0AfH... obtained via gcloud auth print-access-token.
• Body Template:

  {
    "input": {
      "query": "{{test_input}}"
    }
  }

5. Model Context Protocol (MCP) & Simulated Testing

If you are exploring the Agent Evaluation Lab before deploying live infrastructure, you can use the built-in simulated endpoint:

• Simulated Mock Agent: Enter sylvaera://mock-agent as the endpoint URL.
• Behavior: The workbench simulates realistic sub-second agent inferences with deterministic tool calls (e.g. weather, calculator, database_query), allowing you to test regression workflows immediately.

Connection Troubleshooting Checklist

Ready to benchmark your autonomous agents? Go to the Agent Evaluation Lab, click New Project, click Test Connection to verify your configuration, and start evaluating.