Your roadmap is overloaded. Your sprint velocity isn't keeping up. Your hiring pipeline has three open reqs that have been sitting for two months, because the candidates you actually want are entertaining four other offers.
You've been asked to do more with the same team. Again.
There are two ways to add engineering capacity right now. One of them takes six months and costs $300K. The other takes an afternoon and costs under $2K a month. I want to walk through both, honestly.
What hiring actually costs
I'll use U.S. numbers because that's what I know. Adjust for your market.
A senior software engineer costs roughly $170K to $200K in base salary. Add benefits, equity vesting, and payroll taxes, and the loaded cost is $220K to $280K depending on your location and stage.
Then there's recruiting. If you're using an agency, that's 20 to 25 percent of first-year salary. If you're sourcing internally, your recruiters and hiring managers are spending 30 to 50 hours per hire across sourcing, screening, and interviewing.
Then there's onboarding. A senior engineer doesn't ship meaningful code in their first month. They're reading docs, learning the codebase, sitting in orientation meetings, building relationships. Most engineering managers estimate three to four months before a new hire reaches full productivity. During that ramp, you're paying full salary for partial output.
Add it all up and you're looking at $300K to $400K in first-year fully loaded cost, with meaningful output starting around month four or five after headcount was approved. Include the hiring timeline itself and the distance from "we need more capacity" to "we have more capacity" runs six to nine months.
What an agent team costs
A working Fleet setup for a mid-sized team is six to ten agents configured into roles (product owner, implementation, review, testing, release management, SRE monitoring), running against your existing AI provider.
Fleet Pro is $49 a month. API costs for eight agents at moderate usage run $800 to $2,000 a month depending on your provider, models, and workload. Infrastructure is zero, because Fleet runs on machines you already have.
Total: roughly $850 to $2,050 a month, or $10,200 to $24,600 a year.
One note on those API estimates. They assume you're choosing appropriate models per agent. Fleet lets you configure each one independently. Your PO agent refining tickets? Sonnet handles that. Your SRE agent watching deployments? Haiku. Your triage agent labeling issues? Haiku. Save Opus for the two or three agents doing complex implementation work. Running Opus on every agent regardless of task complexity pushes costs two to three times higher with no meaningful improvement on most tasks.
Time to first output: the same day you set it up.
What agents can and can't do, honestly
I'd be lying if I told you an AI agent team replaces a senior engineer. It doesn't.
Here's what agents handle well today. Implementing features from well-scoped tickets. Writing and maintaining tests. Doing first-pass code review against established standards. Managing CI/CD pipeline steps. Triaging and routing issues. Monitoring deployments. Generating boilerplate. With a PO agent refining tickets before work starts, the output quality goes up because the input quality went up first.
Here's what they handle poorly. Architectural decisions that involve trade-offs across systems. Navigating ambiguous product requirements. Mentoring other engineers. Communicating with stakeholders. Solving novel problems in domains where there isn't much public training data.
The useful comparison isn't "agent team versus senior engineer." It's "your current team plus agents versus your current team alone." The agents take the volume work off your humans. Your humans spend more time on the work that justifies their salary.
The comparison that actually matters
A 15-person engineering team today might ship 40 to 50 PRs a week. Some portion of every engineer's time goes to review, triage, and pipeline babysitting. Estimates vary, but 30 to 40 percent of engineering time on non-coding tasks is a commonly cited figure. And remember, today most of those developers are manually operating their AI agents. Prompting, reviewing output, opening PRs, assigning reviewers, waiting for email responses. The AI writes fast but the human pipeline around it moves at human speed.
That same team running Fleet with eight to ten coordinated agents operating autonomously through event-driven pipelines can reasonably expect 120 to 160 PRs a week. The PO agent refines tickets. Development agents implement. Reviewer agents handle first-pass review in seconds instead of hours. Release agents merge and deploy. SRE agents monitor.
The throughput increase isn't free. Somebody needs to write good tickets, though the PO agent helps with this. Somebody needs to review the agents' work periodically. Somebody needs to tune the pipeline as they learn what works.
But the cost of that throughput increase is under $2K a month, not $300K a year. And it's available tomorrow instead of in six months.
One more thing worth considering
An AI agent team doesn't take PTO. It doesn't get sick, it doesn't leave for a competitor, it doesn't need a promotion path, and it doesn't have a bad week. I'm not saying this to be callous about the humans on your team. I'm saying it because reliability of output is something engineering leaders think about constantly and rarely say out loud. Agent teams give you a capacity floor that doesn't fluctuate, and that's something you can actually plan against.
Combine that floor with what your human team is good at (creativity, judgment, the stuff that requires taste) and you end up in a better place than either group could get to alone.