An AI agent is a software system that receives a task, breaks it into steps, executes actions, and returns a result without waiting for instruction at every stage. How do you build an AI agent that actually works? Start with one clear task, choose tools that are easy to maintain, and build in stages with testing at every point.
This guide describes the process we follow when building such a system - from the first conversation with the client to full deployment.
What needs to be ready before you start
Before writing a single line of code, you need to understand exactly what the agent should do. The task must be specific: “answer customer questions” is not enough. “Receive a question via WhatsApp about delivery times, check the CRM system for order status by customer number, and return an answer within 30 seconds” - that’s something you can build.
You also need to know where the data lives. If the agent needs to check inventory, is it in Google Sheets, Monday, or an ERP system? If it needs to send messages, is it through WhatsApp Cloud API or email? A list of all sources and destinations saves weeks of work later.
Finally, you need to decide who is responsible for maintenance. An agent running in production requires monitoring: what happens when the API goes down, what happens when the model returns an unexpected response, who gets alerted. Without a clear owner, the system will fail within two weeks.
Choosing the tools to build the agent
We build most of our agents with n8n because it allows you to see all the logic in a visual interface and edit it without redeploying code. That doesn’t mean it’s the only tool - there are cases where writing code directly makes sense, especially if you need very high performance or full control over memory.
The model itself depends on the task. If the agent needs to classify incoming messages into categories, a small model may suffice. If it needs to write complex responses or analyze long documents, you need something more powerful. We work with OpenAI models and also with models running on local infrastructure, depending on security requirements.
Regarding integrations: if the agent needs to talk to customers, WhatsApp Cloud API is the most common choice in Israel. If it needs to update data, most systems talk to HubSpot, Monday, or Google Sheets. It’s important to check that proper API documentation exists before starting - there’s nothing more frustrating than discovering mid-project that the integration doesn’t exist.
Anyone wanting to understand more about how these systems fit into existing operations can read about AI agents and their integration patterns.
Building the agent in stages
Start with one simple scenario. If the agent is supposed to handle five types of requests, build only one of them initially. This allows you to test the entire chain - receiving input, calling the model, processing the response, returning output - and verify it works before adding complexity.
The next step is adding conditional logic. The agent needs to decide what to do based on the response it receives: if the customer asks about price, it queries one source; if they ask about availability, it queries another. In n8n this looks like nodes with IF conditions, in code it’s a regular switch or if-else.
After that, add error handling. What happens if the API doesn’t respond? What happens if the model returns an empty response? What happens if the customer sends input we didn’t anticipate? Every failure point needs to be handled - at minimum with a clear error message and automatic retry.
At this stage you have a working agent, but it’s not yet ready for production.
Testing before production deployment
Run the agent on real data, but not in the live system. If possible, create a staging environment with a copy of the data. Test at least ten different scenarios, including ones that shouldn’t work - what happens if someone sends text in another language, what happens if they send an image instead of text, what happens if they send a request in the middle of the night when the system is in maintenance mode.
Also test performance. How long does it take the agent to return a response? If it’s more than a few seconds, the customer has already given up. If there’s a bottleneck, you need to identify it now - is it the model that’s slow, is it the API query, or is the problem the size of data the agent is processing.
Then test security. Can the agent access only the data it needs? Is there a log of every action? Are passwords and API keys stored encrypted? If the business works with sensitive data - financial, medical, or personal - this step is critical. There’s a detailed security checklist worth reviewing.
Deployment and monitoring
Deploy the agent gradually. If possible, start with a small group of users or with a percentage of traffic. This gives time to identify problems before the entire organization depends on the system.
Set up monitoring from day one. You need to know how many requests the agent handles, how many end successfully, and how many fail. You also need to see examples of responses the agent returns - sometimes it technically works, but the responses aren’t accurate or useful.
Set up alerts for anything unusual: if the failure rate rises, if response time increases, if there’s an attempt to access unauthorized data. The alerts need to reach someone who can handle them - not just an email inbox nobody reads.
Then start collecting feedback. Are users satisfied with the responses? Are there questions the agent doesn’t know how to handle? This feedback becomes the basis for improvements.
Maintenance and continuous improvement
An AI agent is not a one-time project. Data changes, needs change, and models improve. We revisit the agents we’ve built every few months and check if there’s a way to improve them.
Sometimes the improvement is technical - moving to a new model that’s faster or more accurate. Sometimes it’s operational - adding a new scenario that users requested. And sometimes it’s simply fixing something that broke - an API that changed, an integration that stopped working, or a business rule that was updated.
It’s also important to track costs. Every call to a model costs money, and every API query consumes resources. If the agent runs thousands of requests per day, even a small saving per request can impact the budget.
What to do when something doesn’t work
Every agent encounters problems. The question is how you handle them.
If the agent returns incorrect answers, first check the data it receives. Sometimes the problem isn’t the model but the information it pulls - a table that wasn’t updated, a field containing old values, or an integration pulling from the wrong source.
If the agent is slow, check where the time goes. Most tools let you see how long each step takes. Sometimes you can improve performance simply by changing the order of operations - for example, filtering data before passing it to the model instead of after.
And if the agent fails completely, you need to know how to restore it quickly. That means backups, clear documentation of settings, and system access for anyone who needs to fix it.
How do you know the agent is succeeding
Success is measured by the goal we defined at the start. If the agent was built to save the team time, check how many hours it saves. If it was built to improve response times, measure average response time before and after.
But there are also technical metrics that matter: uptime percentage, percentage of requests that end successfully, and average response time. If any of these metrics drops, you need to investigate why.
And it’s also important to talk to users. Numbers don’t always tell the whole story - sometimes the agent works technically but doesn’t meet the real need.
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