Hiring an AI agent development company: what agents do and how to choose

Many software vendors now say they sell AI agents. Gartner says a lot of them sell chatbots or old automation tools with a new name. Before you hire someone to build one, learn what agents really do. You also need to know what they cost to run and where they go wrong.

This guide explains AI agents in plain words. It covers real business uses, running costs, risks, and how to choose an AI agent development company.

What is an AI agent?

An AI agent is software that uses an AI model to choose its own next steps. It can also use tools, such as your email, your CRM or a booking system. You give it a goal, and it works out the steps.

Anthropic, the company behind Claude, draws a helpful line between two kinds of systems.

• Workflows: The AI follows a set path that a developer wrote in code. For example, read the email, sort it, then draft a reply.
• Agents: The AI decides its own steps and which tools to use as it goes.

For most business tasks, a workflow with a few agent steps is enough. That is a good thing, because set paths are easier to test and cheaper to run.

What can AI agents do for a business?

Agents work best on tasks with many small steps across different tools. Here are common examples.

• Inbox sorting: Read new emails, tag them, draft replies and flag urgent ones for a person.
• Lead follow-up: Look up a new lead, add the details to your CRM and draft a first message.
• Quote drafting: Pull the customer's needs from a form, check your price list and draft a quote for review.
• Data entry between systems: Read an invoice or order and enter it into your accounting or stock system.
• Research: Search several sources, collect facts and write a short summary with links.
• Scheduling: Find open slots, book a meeting and send reminders.

Agents connect to your tools through APIs. An API is a standard way for one piece of software to talk to another. Many AI tools now also support the Model Context Protocol, or MCP. MCP is an open standard for connecting AI apps to outside systems, such as files, databases and other tools.

Do you need an agent or a simpler workflow?

Anthropic's advice to builders is to find the simplest solution possible and add complexity only when needed. It also warns that the freedom agents have means higher costs and a chance that errors build on each other.

Question	Workflow	Agent
Are the steps the same each time?	Yes, best choice	Not needed
Do the steps change case by case?	Hard to cover	Good fit
How easy is it to test?	Easy	Harder
Running cost	Lower	Higher
Risk of a wrong action	Lower	Higher, needs limits

Start with a workflow if the steps are the same each time. Use an agent only for the parts where the steps truly change. A good builder will tell you this, even if it makes the project smaller.

What does an AI agent cost to run?

Building the agent is one cost. Running it is another, and owners often miss it. For build costs, see my guide to AI app development cost.

AI usage

AI providers charge by the token. A token is a small piece of text, such as a short word or part of a longer one. OpenAI, for example, lists its API prices per million tokens. It charges input and output tokens at separate rates, with a lower rate for cached input that it has seen before.

Agents use far more tokens than chats. Anthropic said in 2025 that its agents typically use about four times more tokens than chat. Its systems with several agents working together used about 15 times more. So an agent that runs many times a day can cost much more than a chatbot.

Other running costs

• Hosting: The servers that run the agent and store its logs.
• Tool fees: Subscriptions for the software the agent uses.
• Monitoring: Time to check logs and fix failures.
• Human review: Staff time to approve risky actions.

Ask your builder for a monthly running cost estimate at your real volume. Ask them to set spending limits in each AI account.

What are the risks of AI agents?

Gartner predicted in June 2025 that over 40% of agentic AI projects will be canceled by the end of 2027. It named three reasons. Costs rise, the business value is unclear, and risk controls are weak.

Gartner also warned about "agent washing." That means vendors calling old chatbots or automation tools agents. Gartner estimated that only about 130 of the thousands of agentic AI vendors are real.

The OWASP Top 10 for LLM apps is a well-known security list. Two of its risks matter most for agents.

• Prompt injection: Text written to trick the AI into breaking its rules. An agent that reads emails or web pages can meet this text without anyone noticing.
• Excessive agency: Giving the AI more power, access or freedom than the task needs.

Errors also add up. If an agent makes a small mistake in step two, every later step can build on it.

How do you keep an AI agent safe?

A good builder puts these controls in place before launch.

• Least access: The agent can only reach the data and actions its task needs.
• Human sign-off: A person approves payments, deletions, refunds and messages to customers.
• Spending limits: Caps on AI usage so a loop cannot run up a large bill.
• Full logs: A record of every step, tool call and result.
• A stop switch: A simple way to pause the agent at once.
• A test set: Real cases that the agent must pass before each update goes live.

How many businesses use AI agents?

Use is growing in large firms first. In McKinsey's 2026 survey, the share of large firms scaling agents in at least one part of the business rose from 27% to 40%. Large firms here means revenue above $1 billion. Smaller firms stayed about the same, at 22%.

Gartner predicts that at least 15% of day-to-day work decisions will be made by agentic AI by 2028. It put that figure at 0% in 2024.

How do you choose an AI agent development company?

Look for a builder who tries to make your project simpler. Then ask these questions.

1. Could a simple workflow do this job, and why or why not?
2. Which steps will the agent take alone, and which need a person?
3. What can the agent access, and how do you limit it?
4. How will you test it before launch and after each change?
5. How much will the agent cost to run each month if it handles our real number of tasks?
6. How do we pause it if something goes wrong?
7. Who owns the code, the prompts, the logs and the accounts?

Red flags

• Agent washing: They call a basic chatbot an agent.
• Full autonomy on day one: They want the agent to act with no human checks.
• No running cost estimate: They only quote the build.
• Broad access: They ask for admin access to every system you have.
• No logs: You cannot see what the agent did or why.

If you would rather hire people directly, see my guide on how to hire AI developers.

When should you not build an AI agent?

Skip the agent when the steps are the same every time. A normal workflow or a simple automation will be cheaper and easier to trust. Skip it when one wrong action would be very costly and a person must check every step anyway. Also skip it if nobody on your team will watch the logs. Every agent needs an owner.

How does Blur Studio approach AI agent builds?

For agents, I start by mapping the task step by step with you. I mark which steps are always the same and which change. The fixed steps become a plain workflow. I only use agent steps where the work truly needs them.

I give the agent the least access it needs, and I add human sign-off on risky actions. I set spending limits and keep full logs. The code, the prompts and the logs are yours, and the AI accounts are in your name. After launch, I can take care of the agent, check the logs and update it as AI models change. If your agent mainly needs to answer questions, my guide to AI chatbot development may fit better.