How to hire an AI agent developer: what to automate first and how to judge candidates
Many companies now want agents that do real work: answer leads, sort tickets, update records and prepare reports. The hard part is rarely the model. It is choosing the right first task, connecting it safely to your systems and keeping it reliable once real data starts flowing. This guide covers how to pick that first task, what to test in candidates, the questions that reveal real experience and how to plan the first months. For model features inside your product, see our AI engineers; for general scripting and back-end work, our Python developers.
Pick a first task that is worth automating
Good first tasks happen often, follow a pattern, use information that already exists in your tools and are cheap to check. Triage of incoming enquiries, drafting replies for approval, moving data between apps and preparing weekly summaries are common starting points. Avoid tasks where one mistake is expensive and hard to undo. Write down how a person does the task today, step by step, with three real examples. That document becomes the brief for your developer and the first test set for the agent.
What to test
- Tool design: can they describe a narrow tool with clear inputs, outputs and error messages?
- Control: where would they put approval steps, limits on steps and spend, and a way to stop a run?
- Data handling: how do they protect API keys, customer data and personal details in logs?
- Testing: do they build a set of real cases and measure success before and after each change?
- Tool choice: can they explain when a workflow tool is enough and when code is needed?
Questions that separate candidates
- Tell me about an agent you built that failed in production. What happened and what did you change?
- How would you stop an agent from calling the same tool in a loop?
- A customer email arrives in a language the agent has not seen. What should happen?
- How would you show our team what the agent did yesterday and why?
Strong answers name real tools, real numbers from their own projects and the trade-offs they made. Be careful with anyone who promises full automation of a complex job on day one.
Sample job post
Keep it concrete: “We are hiring an AI agent developer to automate our lead handling and support triage. You will build workflows in n8n, connect them to HubSpot and our help desk, write tools for our internal API, and set up approvals, logs and weekly reports on success rate and cost. Must have: Python or TypeScript, model APIs with tool calling, webhooks. Nice to have: LangGraph, Model Context Protocol and experience with customer-facing automations.”
Keeping data and access safe
An agent works with the same data your staff use, so treat it like a new team member with a narrow role. Give it its own accounts with the smallest set of permissions, keep API keys in a secrets store rather than in workflow settings, and strip personal details from logs where they are not needed. Decide early which data may be sent to an outside model provider and which must stay inside your systems. Ask candidates how they handled these points before, and how they would explain them to your security or legal contact in plain words.
What affects the cost
Experience moves the price most. Someone who has run agents in production for real customers costs more than someone who has built demos. The number of systems involved matters too, as does how much the work touches money, health or legal records. Running costs come on top: model usage, workflow tool plans and hosting. A good developer estimates these early and keeps them visible.
A paid trial task
Give final candidates a few paid hours on a small version of your real task, for example reading ten sample enquiries, filling in a sheet and drafting replies for approval. Use the same brief and data for everyone. Judge the result on accuracy, how errors are handled and how clearly the candidate explains the limits of what they built.
The first three months
In the first two weeks, agree on one task, collect real examples and connect read-only access first. By the end of the first month, the agent should run on real inputs with approvals switched on, and you should see a short weekly report. By the third month, your developer should be widening what the agent may do on its own where the numbers support it, and proposing the next task to automate.
Mistakes to avoid
- Giving an agent broad access to email or payments from the start.
- Skipping logs, so nobody can explain what happened when something goes wrong.
- Measuring success by demos instead of results on real cases.
- Ignoring running costs until the first large invoice arrives.
Building a wider remote team? Read our remote software engineering hiring playbook.