AIHafsteinn Runarsson · AI Konsulent11 Aug 2026 · 7 min

How much does it cost to build an AI agent?

AI agent development cost starts with scope. Daia does not publish one universal price for every agent build because the work required depends on what the agent must do, the systems it must work with, and the controls it needs around it.

The short answer is that Daia's AI agent engagements start from EUR18k. The final price is agreed for the defined scope before work starts.

That starting point is useful for qualification, but it is not a package price. A reliable quote needs to show what is being built, what is included, what is excluded, and what your team needs to provide.

What drives the cost of building an AI agent?

The main cost drivers are scope, integrations, evaluation and reliability work, human oversight, data readiness, and ongoing support. Each one changes the amount of product, engineering, and operational work needed to move from a promising demo to something people can use in a real workflow.

Scope and workflow complexity

Start with the job the agent is expected to complete. A focused workflow with a clear starting point, defined users, and an agreed end state is easier to scope than a broad request to automate a department or business process.

Complexity increases when the agent must handle more cases, make more decisions, coordinate several steps, or serve different roles. Exceptions matter too. If the workflow has many edge cases, the team needs to define how the agent should respond when information is missing, instructions conflict, or an action cannot be completed safely.

A useful scope answers practical questions:

• What job should the agent complete?
• Who will use it?
• What does a successful result look like?
• Which cases should remain outside the initial scope?
• What should happen when the agent is uncertain?

Clear boundaries make the quote more meaningful. They also help prevent a narrow workflow from quietly becoming a much broader product during delivery.

The number and complexity of integrations

Integrations often shape both the build effort and the operational risk. An agent that works with a limited, well-understood set of systems is different from one that must read from and act across several tools.

The number of integrations is only part of the picture. Their complexity matters just as much. Available interfaces, authentication, permissions, data formats, rate limits, and the actions the agent is allowed to take all affect the work.

Read-only access is also different from permission to create, update, send, approve, or delete information. The more consequential the action, the more carefully access and safeguards need to be designed.

When requesting a quote, list the systems the agent may use and the actions it should perform in each one. Mark anything that is still unknown. That gives the delivery team a better basis for assessing integration work than a generic request to “connect our tools.”

Evals and reliability work

An AI agent needs a clear definition of a good result. Evals provide a structured way to test whether it follows the workflow, uses the right information, and handles important cases as expected.

The amount of evaluation work depends on the range of tasks and the consequences of failure. A narrowly defined internal workflow may have a smaller test surface than an agent that supports several roles or performs actions across business systems.

Reliability work also includes deciding how the system should behave when a model response is incomplete, a connection fails, required information is missing, or an action cannot be confirmed. Those situations are part of production delivery, not an afterthought.

A quote should therefore clarify what will be evaluated, which examples or acceptance cases are available, and what level of reliability the agreed workflow requires. Without that context, two proposals may use the same label while including very different amounts of validation work.

Human-in-the-loop requirements

Human oversight is a design decision, not a fallback added at the end. Some agent actions can run automatically. Others should pause for review, approval, or escalation.

The cost impact depends on where those checkpoints sit and how they work. The team may need to design approval states, notifications, permissions, audit information, and a clear route for resolving exceptions.

Ask these questions early:

• Which actions should always require human approval?
• Who is allowed to approve them?
• What context does that person need to make a decision?
• What happens if nobody responds?
• Which events need to be visible for review later?

Clear answers make the workflow safer and make the delivery scope easier to price.

Data readiness

An agent can only work with the information it can access and interpret. Data readiness affects how much preparation is needed before the agent can perform the intended job.

Relevant information may be spread across systems, use inconsistent formats, contain gaps, or lack clear ownership. Access may exist in principle but still require permissions or internal decisions. In other cases, the team may need to identify which source should be treated as authoritative when records disagree.

A useful discovery process identifies the required information, where it lives, who controls access, and whether it is ready for the workflow. If data cleanup, mapping, migration, or new access decisions are needed, those activities should be visible in the scope rather than hidden inside the build estimate.

Ongoing support and operating costs

The build price and the cost of operating an agent answer different questions. A project quote covers the agreed delivery scope. Ongoing costs may include model usage, hosting, third-party services, monitoring, maintenance, and support, depending on the engagement and the chosen systems.

Do not assume those items are automatically included or excluded. Ask the supplier to separate delivery from ongoing operation and explain the support arrangement after handover.

It is also worth clarifying how changes will be handled. Workflows, systems, and policies evolve. A useful proposal explains what happens when an integration changes, a new use case appears, or the agent needs further evaluation after launch.

What should an AI agent quote include?

A useful quote should connect the price to an agreed outcome. It should describe the workflow, users, integrations, permissions, evaluation approach, human approval points, data dependencies, delivery terms, and handover expectations.

It should also make exclusions visible. Open wording can make a price look precise while leaving the obligation unclear. A clear proposal gives both sides a shared understanding of what will be delivered and what would require a change to scope.

Timing, access, ownership, delivery, and handover should be agreed for the engagement. That is more useful than relying on a generic promise that may not fit the work.

How to compare AI agent development proposals

Compare proposals only after normalizing the scope. Start with the promised outcome and check whether each supplier is pricing the same workflow, users, systems, actions, and operating conditions.

Then compare what each proposal includes for evals, reliability, human oversight, data preparation, and support. A lower headline price may reflect a narrower obligation rather than a more efficient route to the same result.

Look for explicit assumptions and exclusions. If access, data quality, third-party costs, or approval workflows are unresolved, the proposal should say so. Unknowns are easier to manage when they are visible.

Finally, separate build cost from ongoing consumption. Platform or model usage does not necessarily include the work required to define the workflow, connect systems, implement controls, evaluate behavior, and prepare the agent for normal use. Likewise, an agreed build price does not automatically include every future usage or support cost.

Prepare for a useful quote

You do not need a complete technical specification before starting a conversation. Bring a clear description of the job, the people who will use the agent, the systems it may access, and the actions it should take.

Also identify where a person must stay in control, what information the workflow depends on, and what a successful result looks like. If something is unknown, say so. That gives the delivery team a better basis for discovery than a confident assumption.

Daia's AI agent engagements start from EUR18k. We agree the scope and price before work starts, with engagement-specific terms covering timing, access, ownership, delivery, and handover.

Get a quote or start a conversation about the workflow you want to build.