Building an AI agent prototype is relatively easy. With an LLM, a retrieval pipeline, and several API connections, developers can create an impressive demonstration within days.

The real challenge begins when the system reaches production.

Real users submit unclear requests, external tools fail, business data changes, and model costs increase unexpectedly. An agent that performs well in a controlled test may become unreliable when thousands of people start using it.

A Real-World Example: Vanta’s Support Agent

Vanta provides a useful example of how an AI agent should be tested before full deployment.

According to an Intercom customer story, Vanta evaluated Fin AI Agent against its existing AI system using 400 real customer conversations. Fin resolved approximately 73% of the cases, compared with around 49% for the existing system.

After deployment, the agent achieved a 71% resolution rate for the chat conversations it handled. This represented nearly 2,500 conversations per month that did not require a human support agent.

The results are impressive, but the evaluation process is equally important. Vanta did not rely on a polished demo. It tested the agent with real questions and measured resolution rate, accuracy, and answer quality before expanding its use.

Here are seven problems developers should address when moving an AI agent into production.

1. Hallucinated Answers

LLMs can generate confident responses without reliable evidence. RAG can reduce this risk by connecting the agent to trusted information, but retrieved content must still be relevant and current.

2. Poor Retrieval Quality

A retrieval system may return incomplete, outdated, or unrelated documents. Evaluate retrieval separately using metrics such as precision, recall, relevance, and answer faithfulness.

3. Failed Tool Calls

Agents often depend on APIs, databases, search services, or MCP servers. These tools may time out or return invalid data.

def call_tool_safely(tool, arguments):
    try:
        result = tool(**arguments)
        return result if result else {"error": "Empty response"}
    except TimeoutError:
        return {"error": "Tool timed out"}

Production workflows need retries, timeout limits, validation, and fallback responses.

4. Uncontrolled Agent Loops

An agent may repeatedly plan and call tools without completing the task. Set limits for tool calls, reasoning steps, execution time, and cost per request.

5. Excessive Permissions

Agents should not have unrestricted access to business systems. Use role-based permissions and require human approval for sensitive actions such as issuing refunds or deleting data.

6. High Latency and Cost

Multiple model calls and retrieval steps can make an agent slow and expensive. Use caching, shorter prompts, parallel execution, and smaller models for simple tasks.

7. Missing Observability

Without tracing, developers cannot determine whether an error came from retrieval, the model, or an external tool.

A useful trace should capture prompts, retrieved documents, tool calls, errors, latency, token usage, cost, and final responses.

Production Readiness Is a System Problem

A reliable AI agent is more than an LLM connected to several tools. It requires testing, security, observability, fallback logic, and continuous evaluation.

Organizations building complex AI products may also work with an experienced technology partner. Varmeta develops AI and data solutions that help businesses transform early concepts into scalable production systems.

The best AI agents are not those that perform perfectly in a demo. They are those that remain useful when tools fail, data changes, and real users behave unpredictably.

Source: Intercom, “How Vanta unified its customer experience with Fin.”