★ Ops Coordinator · agent or hire?
AI Agent vs Hiring an Ops Coordinator: Cost Calculator
Operations work is glue: chasing updates, moving data between tools, keeping trackers current. That glue is often the easiest place to start with agents, because the tasks are clear and mistakes are easy to spot.
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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
$2,072 / month
Pays back the build cost in 7.2 months.
People only
$4.2K/mo
With agent
$2.1K/mo
Hours freed
62 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 | $4.2K | $17.1K |
| 2 | $8.3K | $19.2K |
| 3 | $12.5K | $21.3K |
| 4 | $16.7K | $23.4K |
| 5 | $20.8K | $25.5K |
| 6 | $25K | $27.6K |
| 7 | $29.2K | $29.7K |
| 8 | $33.3K | $31.8K |
| 9 | $37.5K | $33.8K |
| 10 | $41.7K | $35.9K |
| 11 | $45.8K | $38K |
| 12 | $50K | $40.1K |
Where the agent cost goes
- Work people still do$1,458
- Reviewing the agent$135
- Model usage$0
- Maintenance$500
Model cost per task: $0.0007 on GPT-6 Luna, for 650 agent tasks a month.
An agent that pays back in 7.2 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
- Chase people for updates and log the answers
- Move data between tools that have APIs
- Keep trackers and dashboards current
- Write weekly status summaries
Keep with a person
- Resolve conflicts between teams
- Decide priorities when everything is urgent
- Handle a vendor relationship that has gone wrong
What review looks like: Spot-check a sample of updates each week, and require approval for anything that sends messages outside the company.
Good fit, bad fit
| Good fit if… | Not a good fit if… |
|---|---|
| Repetitive cross-tool chores | Work that is mostly negotiation |
| Tools with good APIs | Processes that change weekly |
| A process that is already written down | Tools with no API or export |
Risks to plan for
- Automating a broken process and making it faster
- Messages sent to the wrong people
- Permissions that let the agent change more than it should
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 ops coordinator preset with the example salary of $60,000 plus 25% overhead, 1,000 operational tasks a month at 6 minutes each, the agent handling 65% and a person reviewing 15% of those:
| Line | Per month |
|---|---|
| People only | $4,167 |
| Remaining human work | $1,458 |
| Reviewing the agent | $135 |
| Model usage (GPT-6 Luna) | $0 |
| Maintenance | $500 |
| With the agent | $2,094 |
| Saving | $2,072 |
With a $15,000 example build cost, payback is 7.2 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
What ops tasks should I automate first?
Isn’t this just Zapier?
How much access should an ops agent have?
Is an AI agent cheaper than hiring someone?
Where do the model prices come from?
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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.