AI agent cost for a company in 2026 is usually driven by workflow complexity, integration depth, and control requirements, not model pricing alone. For a company in Poland or the wider EU, a narrow internal assistant may fit into a low five-figure setup budget, while an agent that reads from and writes into business systems can move into high tens of thousands or six figures once security, approvals, and operational ownership are handled properly.
The real buying question is not whether AI agents are affordable in theory. It is whether one workflow is stable enough to justify production work now. If the process is fuzzy, the data is messy, or nobody owns exceptions, the project will cost more than the demo suggests.
| Deployment type | Planning setup range | Planning monthly run range | Main cost drivers |
|---|---|---|---|
| Internal knowledge assistant | Low five figures to low tens of thousands of euros | Low four figures per month | Content cleanup, permissions, SSO, retrieval quality |
| Support drafting agent | Tens of thousands of euros | Low to mid four figures per month | Ticketing integration, order data, approval flow, multilingual quality |
| Workflow agent with system actions | High tens of thousands to six figures | Mid four figures upward | ERP or CRM actions, audit trail, exception handling, role controls |
These are planning ranges, not market averages. They assume one defined workflow, production intent rather than a lab prototype, basic testing, and at least some integration or retrieval setup. The ranges are anchored to visible cost drivers: how many systems the agent touches, whether it can take actions, expected usage volume, and how much governance is needed around personal data, approvals, and logging.
If a vendor leads with token savings before mapping workflow risk, integration depth, and post-launch ownership, they are probably pricing a demo rather than a production capability.
What actually makes up AI agent cost in 2026
Public model pricing from providers such as OpenAI or Anthropic is useful, but it answers only one part of the budget question. A company still has to pay for the work that makes an agent usable inside a real process: connecting systems, controlling access, testing outputs, handling exceptions, and keeping the workflow reliable after launch.
The budget usually falls into four lines: implementation, model usage, governance, and operations. The mix changes by use case. A read-only assistant leans toward content and access setup. An agent that updates records leans toward integration, testing, and controls.
Implementation and integration
This is often the biggest year-one cost. It includes workflow design, API connections, retrieval setup, identity rules, prompt and tool orchestration, interface work, testing, and deployment hardening. The gap between a document assistant and an agent that checks order status, drafts a reply, and writes back to CRM is not cosmetic. It is a different cost class.
That matters in Poland for a simple reason: many firms still run mixed stacks. One department may use modern SaaS, while finance, warehouse, or service operations still depend on older ERP modules, local customizations, or partner-built logic. The AI layer inherits that complexity. It does not remove it for free.
Model usage and token spend
Token cost is the cleanest line item and the easiest one to misread. It depends on prompt size, output length, retrieval design, retries, tool calls, embeddings, and traffic. Finance teams often want this separated because it behaves more like usage-based infrastructure than project delivery.
For many internal assistants, token spend stays secondary. It becomes material when volume is high, prompts are bloated, or the workflow is poorly designed. Long context windows, repeated lookups, and unnecessary orchestration loops can inflate monthly cost faster than the model choice itself. Poor workflow design is often a bigger cost problem than premium model pricing.
Governance, security, and legal review
For EU deployments, this is not optional overhead. If prompts, logs, or retrieved context contain personal data, the company may need contract review, retention decisions, access controls, transfer review, and internal policy changes. The relevant logic comes from GDPR requirements around data protection by design and by default.
The timing issue matters as much as the legal issue. Approval often slows down when employee data, support logs, or customer records are involved, especially in firms where legal, security, and architecture review happen late instead of upfront. Buyers should budget for internal coordination earlier than vendor demos imply.
Security cost also rises when the agent can do more than answer questions. A read-only assistant may need permission-aware retrieval and logging controls. A workflow agent that can trigger actions usually needs stronger identity mapping, approval checkpoints, and a clearer audit trail. That difference is why two projects using the same model can land in very different budget bands.
Ongoing operations
Production agents need owners. Someone has to monitor quality drift, update prompts, maintain connectors, review source changes, handle incidents, and decide when the workflow should escalate to a human. Teams that budget only for launch usually discover the real cost a quarter later, when the pilot still works in demos but nobody trusts it enough to rely on it.
A common failure pattern is simple: the business approves a pilot before it decides who owns the workflow after launch. That ownership gap turns into direct cost because every unresolved exception becomes manual supervision, rework, or delay.
Operations also include less visible work such as prompt versioning, source-content review, fallback tuning, and periodic access checks. None of that looks exciting in a sales deck. All of it matters once the agent becomes part of a real business process.
Typical pricing ranges by use case
Specific numbers without context are not very useful. The ranges below are for buyer-side planning and proposal review. They assume one primary workflow, a commercial rollout, basic governance, and no attempt to automate half the company in phase one.
Internal knowledge assistant
This is usually the cheapest category because it is read-only. The agent answers employee questions from policies, product documentation, procedures, or internal knowledge bases. It may use retrieval-augmented generation, but it does not update core systems.
Planning range: low five figures to low tens of thousands of euros for setup, then low four figures per month for operation.
The lower end assumes one main content source or a small number of sources, SSO, permission mapping, basic analytics, and rollout to one team or department. If the content is scattered across stale PDFs, duplicated files, and conflicting policy versions, the budget rises quickly because content cleanup becomes part of implementation rather than a side task.
This is often the right first move for a company that wants adoption data without operational risk. It also tests whether the knowledge base is good enough to support later automation. If employees cannot get consistent answers from approved documents, adding system actions later is usually premature.
Support drafting agent
This is where many buyers misread the economics. The agent may classify tickets, retrieve order or account context, draft replies, and suggest next actions. If humans approve outbound responses, the business case can still be strong. Push for full autonomy too early and the cost moves from labor to error handling, escalations, and customer damage.
Planning range: tens of thousands of euros for setup, then low to mid four figures per month. Higher monthly cost is common when volume, languages, or retrieval depth increase.
That range usually assumes one ticketing environment, one commerce or CRM source, approval queues, analytics, and a controlled set of support intents such as order status, returns, or policy-based responses.
Illustrative scenario: a mid-sized e-commerce company serving Poland and Germany wants an agent to draft replies for delivery status, returns, and damaged shipment claims. The cost pressure comes less from model usage than from connecting order data, carrier status, policy content, and a human approval path for refund-related messages. If exception handling differs by market or team, rollout slows down before the model becomes the issue.
Support is also where multilingual complexity starts to matter commercially. A company may accept minor variation in internal answers, but customer-facing drafts need tone control, policy consistency, and escalation rules that survive across languages. That usually means more testing and more review cycles than buyers expect from the initial demo.
Workflow agent with system actions
This is the expensive category. The agent does not just answer or draft. It updates records, triggers workflows, creates cases, routes approvals, or coordinates back-office steps across systems. Once the agent can act, the software and governance burden rises sharply.
Planning range: high tens of thousands to six figures for setup, then mid four figures upward per month depending on usage, support model, and control requirements.
That budget assumes at least one core system integration, role-based access, audit trail, fallback logic, testing, and post-launch support. If the workflow touches finance, HR, or regulated records, the budget can move above this range because the company needs stronger evidence that the process is controlled.
For many firms, this should not be phase one. A workflow agent becomes commercially sensible after a narrower assistant or drafting workflow proves that the process, data, and ownership model are stable enough. Companies that skip that step often pay to discover process ambiguity with more expensive tooling.
