How much does an AI agent cost?
Two costs: building it and running it. The build is mostly integrations, tools, guardrails, evals and review screens, priced as engineering hours. Running costs are per task: every model call, tool call and human review, which add up quickly when an agent takes many steps.
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What drives the build
- 1The tools the agent can call: each API or internal system is an integration with its own permissions and error handling.
- 2Guardrails: limits on actions, approval steps for risky ones and checks on output.
- 3Evals built from real cases, so you know it works before and after each change.
- 4Human review screens for the cases the agent should not decide alone.
- 5Logging and monitoring of every step, for debugging and audit.
What drives running costs
Steps per task times tokens per step times price, plus any paid tools it calls and the time people spend reviewing. Long histories passed to every step make each step dearer. Set a maximum number of steps, cache the fixed prompt, and route simple steps to a smaller model.
How OceanLab approaches AI builds
For AI automation we start with a clickable prototype of the workflow and the review screens, and test the AI on real examples before the full build, so both the build scope and the running cost per task are known before you commit.
Check it for your case.
Common follow-up questions.
Is a workflow cheaper than an agent?
Often. If the steps are known in advance, a fixed workflow with a model at a few points is cheaper and easier to test than an agent deciding each step.
How do I stop an agent running up costs?
Cap the steps and tokens per task, set spending alerts and stop loops on repeated errors.
Can I buy an agent instead of building one?
For common tasks like support triage, often yes. Build when the agent must work inside your own systems and rules.
