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Before Deploying an AI Agent: 5 Things I'd Test First

Before Deploying an AI Agent: 5 Things I'd Test First

A practical checklist for testing AI agents before production, covering answer accuracy, tool selection, RAG faithfulness, safety, and latency and cost.

Abhishek

Before Deploying an AI Agent: 5 Things I'd Test First

AI agents are becoming more capable. They can retrieve information, call tools, interact with APIs, and complete multi-step tasks. But a successful demo does not automatically mean an agent is ready for real users.
An agent might select the wrong tool, retrieve irrelevant information, ignore a permission boundary, or take too long to complete a task.
As I explore LLM development, RAG, and agentic AI, these are the five areas I would test before deploying an agent.

1. Answer Accuracy

Does the agent actually solve the user's problem?
Test it with normal questions, ambiguous requests, incomplete information, and questions it cannot answer reliably.
What I would measure:
  • Factual correctness
  • Task completion rate
  • Handling of missing information
  • Frequency of unsupported claims
A fluent response is not necessarily a correct response.

2. Tool Selection and Execution

An agent may have access to search, databases, APIs, or other tools. Choosing the wrong tool can lead to an incorrect result even when the underlying model is capable.
Test whether the agent:
  • Selects the appropriate tool
  • Supplies valid parameters
  • Handles tool errors and timeouts
  • Avoids unnecessary tool calls
For actions that modify data or affect users, verify authorization before execution.

3. RAG Retrieval and Faithfulness

Retrieval-Augmented Generation (RAG) allows an application to retrieve relevant information from external documents before generating an answer.
But retrieving documents is only part of the problem.
I would check:
  • Whether the retrieved passages are relevant
  • Whether the answer is supported by those passages
  • Whether citations point to the correct sources
  • Whether the agent admits when the available information is insufficient
A RAG application should not sound confident when its retrieved evidence does not support the answer.

4. Safety and Permissions

An agent should not be allowed to perform every action it can technically access.
I would test prompt injection attempts, restricted actions, sensitive information exposure, and requests that require human approval.
Important controls include:
  • Least-privilege access
  • Input and output safeguards
  • Explicit authorization for sensitive actions
  • Human review for high-impact decisions
  • Logs that help investigate failures
Safety should be part of the architecture, not an afterthought.

5. Latency and Cost

An agent that works correctly but takes too long or makes too many model and tool calls may be unsuitable for production.
I would measure:
  • End-to-end response time
  • Number of model and tool calls
  • Cost per completed task
  • Failure and retry rates
  • Performance across repeated runs
The goal is not simply to minimize cost. It is to achieve reliable results within an acceptable time and budget.

A Simple Evaluation Loop

My approach would be to create a small set of realistic test cases, run the agent repeatedly, record failures, identify their causes, make improvements, and run the tests again.
The loop would be:
Define test cases → Run the agent → Measure results → Analyze failures → Improve → Retest
This is more useful than judging an agent from a handful of successful conversations.

How AWS Fits Into This

For developers building on AWS, Amazon Bedrock provides model evaluation capabilities, while Amazon Bedrock AgentCore Evaluations focuses on evaluating agent behavior. Amazon Bedrock Guardrails can also help implement safeguards for generative AI applications.
These capabilities address different parts of the problem, so the right combination depends on the application's architecture and requirements.
Official resources:
Amazon Bedrock Evaluations:
https://aws.amazon.com/blogs/machine-learning/evaluate-models-or-rag-systems-using-amazon-bedrock-evaluations-now-generally-available/ 
Amazon Bedrock AgentCore Evaluations:
https://aws.amazon.com/blogs/machine-learning/build-reliable-ai-agents-with-amazon-bedrock-agentcore-evaluations/ 
Amazon Bedrock Guardrails:
https://aws.amazon.com/blogs/machine-learning/safeguard-generative-ai-applications-with-amazon-bedrock-guardrails/ 

Final Thoughts

I am currently learning the fundamentals of LLMs and exploring how these concepts connect to real AI applications.
My takeaway is simple: building an agent is only one part of the work. Testing its decisions, measuring its failures, and improving its reliability are equally important.
Question for the AWS community: What do you consider the most important evaluation metric for an AI agent in production: task accuracy, tool-use reliability, safety, latency, or cost? And how do you test it in your projects?
# agentic-ai# ai-agents# amazon-bedrock# generative-ai# machine-learning
Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.
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