პირველი AI აგენტის აშენების 7 ყველაზე გავრცელებული შეცდომა

When you build your first AI agent, optimism is easier than caution. The demo works. The first answers look good. Everyone gets excited.

Then production happens.

This list comes from the problems teams hit again and again when they move from a neat demo to a real workflow.

01. Evaluating by vibe

"I looked at the answer and it seemed good" is not evaluation. It is confirmation bias.

Build a small offline evaluation set first. Start with 50 examples. Include easy cases, boring cases, edge cases, and cases where the agent should refuse or ask for help.

The goal is not perfection. The goal is to know whether a change made the agent better or worse.

02. Prompt drift

When you change the prompt, you change the system.

That means every important prompt needs a version. If you update it, run regression checks. Otherwise, one week later you will not know why the agent started behaving differently.

Treat prompts like code:

• Version them
• Review them
• Test them
• Roll them back when needed

03. Unstructured output

An LLM response is text. Not JSON. Not a dictionary. Text.

If your system expects structure, ask for structured output and validate it. If validation fails, add a fallback.

This matters because one beautiful answer in a demo does not prove the output will stay parseable in production.

04. Cost blowups

A test run costs $0.02. That feels cheap.

Now multiply it:

• 10,000 users per day
• Several model calls per user
• Long context windows
• Tool calls
• Retries

Suddenly the demo has a monthly bill. Calculate cost using the business model, not the demo.

05. Overfilled context windows

Putting the entire conversation history into context feels simple until the token limit arrives.

Long context also makes the model less focused. It may pay attention to the wrong thing or repeat outdated information.

Use summarization, retrieval, or a sliding window. Keep context useful, not just large.

06. No retry strategy for tool calls

External APIs fail. Sometimes they time out. Sometimes they rate limit you. Sometimes they return strange data.

Your agent needs a retry strategy:

• Retry safe actions
• Use exponential backoff
• Avoid repeating dangerous actions
• Log the failure
• Ask a human when needed

Without this, the agent is only reliable when the internet is polite.

07. Deploying without monitoring

If you cannot see the system, you do not know the system.

At minimum, monitor:

• Request count
• Cost
• Latency
• Tool usage
• Error rate
• Human approval rate
• User feedback

Tools like LangSmith or Langfuse can help, but the principle matters more than the vendor.

The better path

Start smaller than you want. Evaluate earlier than feels necessary. Monitor before the launch feels serious.

Most AI agent failures are not model failures. They are product and engineering discipline failures.

The good news is that discipline can be learned.