Build cost: what you pay to create it
- Number of integrations: each system the agent reads or updates needs a reliable connection.
- Decision complexity: simple rule-following is quicker to build than judgement across many cases.
- Accuracy target: reaching very high accuracy takes more testing and refinement than "good enough with review".
- Data readiness: clean, accessible data and documents shorten the project.
- Controls: approval steps, audit logs and permission checks are essential and take time to do well.
Running cost: what you pay every month
- Volume: how many tasks the agent handles.
- Model choice: larger models cost more per task; routing simple steps to smaller models saves money.
- Infrastructure: hosting, databases and monitoring.
- Human review: the share of tasks a person still checks.
- Maintenance: updates when your systems, policies or the underlying models change.
How to keep cost under control
- Start with one workflow and a proof of concept with a fixed scope and price.
- Measure time saved and error rate on real work before scaling.
- Cache and reuse results, and use the smallest model that meets the quality bar.
- Reduce human review gradually as measured accuracy improves.
The honest answer
Two agents that sound alike can differ greatly in cost because of the systems and rules behind them. The fastest way to a real number is a short discovery call about one specific task, followed by a written scope and estimate.