★ Recruiting Coordinator · agent or hire?
AI Agent vs Hiring a Recruiting Coordinator: Cost Calculator
Screening applications and scheduling interviews takes most of a coordinator’s week. An agent can summarise CVs against a rubric and handle calendars. Ranking people is where you need care: bias and legal exposure are real, and a person must own the decision.
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Presets are starting assumptions for a small team, not industry statistics. Change every number to match your reality.
The agent saves
$1,769 / month
Pays back the build cost in 8.5 months.
People only
$5.6K/mo
With agent
$3.8K/mo
Hours freed
55 h/mo
12 months, side by side
Cumulative cost. The agent line starts at the build cost.
| Month | People only | Agent |
|---|---|---|
| 0 | $0 | $15K |
| 1 | $5.6K | $18.8K |
| 2 | $11.1K | $22.6K |
| 3 | $16.7K | $26.4K |
| 4 | $22.2K | $30.1K |
| 5 | $27.8K | $33.9K |
| 6 | $33.3K | $37.7K |
| 7 | $38.9K | $41.5K |
| 8 | $44.4K | $45.3K |
| 9 | $50K | $49.1K |
| 10 | $55.6K | $52.9K |
| 11 | $61.1K | $56.6K |
| 12 | $66.7K | $60.4K |
Where the agent cost goes
- Work people still do$2,778
- Reviewing the agent$500
- Model usage$8
- Maintenance$500
Model cost per task: $0.0210 on Claude Sonnet 5.5, for 400 agent tasks a month.
An agent that pays back in 8.5 months.
I build production AI agents with human review, guardrails and logging.
What the agent can and can't take
Good jobs for the agent
- Summarise a CV against the role’s must-have criteria
- Answer candidate questions about the process
- Schedule and reschedule interviews
- Send status updates so nobody is ghosted
Keep with a person
- Make the hiring decision
- Judge potential that is not on paper
- Guarantee it is free of bias without testing
What review looks like: A person reviews every rejection recommendation and a sample of advances. Keep the rubric written down and audit outcomes across groups.
Good fit, bad fit
| Good fit if… | Not a good fit if… |
|---|---|
| High application volume for well-defined roles | Senior or creative roles where judgement is the whole job |
| A written rubric already exists | Jurisdictions that restrict automated screening |
| Scheduling eats the most time today | No capacity to audit outcomes |
Risks to plan for
- Discrimination through biased screening criteria
- Rejecting strong unconventional candidates
- Breaking local rules on automated decisions about people
How the comparison works
The calculator compares two ways of getting the same tasks done each month: people doing all of them, or an agent doing a share while people do the rest and review the agent's work.
people only = tasks × minutes ÷ 60 × hourly cost
with agent = remaining human work + review time + model cost + maintenance
model cost = agent tasks × (input tokens × input price + output tokens × output price)
savings = people only − with agent
payback = build cost ÷ savings (never, if savings ≤ 0)
It only counts the share of someone's time spent on these tasks, so it answers “what does this work cost”, not “can I remove a job”. Model prices come from the LLM API Cost Calculator.
Worked example
The recruiting coordinator preset with the example salary of $60,000 plus 25% overhead, 800 applications a month at 10 minutes each, the agent handling 50% and a person reviewing 60% of those:
| Line | Per month |
|---|---|
| People only | $5,556 |
| Remaining human work | $2,778 |
| Reviewing the agent | $500 |
| Model usage (Claude Sonnet 5.5) | $8 |
| Maintenance | $500 |
| With the agent | $3,786 |
| Saving | $1,769 |
With a $15,000 example build cost, payback is 8.5 months. Notice how small the model cost is next to the human time that remains: review time and the share the agent can't finish usually decide the answer, not token prices.
Questions about this role
Is it legal to screen candidates with AI?
What should an AI recruiting agent handle first?
How do I check a screening agent for bias?
Is an AI agent cheaper than hiring someone?
Where do the model prices come from?
Compare by role
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Fine print
Tools: on the house.
Engineer: by the hour.
The math was free. A reliable agent takes engineering, which is what I do.
I'm Himanshu, an AI and full-stack engineer. I build MVPs, AI agents and the growth plumbing around them for early-stage startups.