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.
Hiring and agent workflows are two ways to add capacity, but they do not buy the same thing. One adds a person who can own ambiguous problems and relationships. The other helps your existing team run repeatable work through explicit agent roles. The useful math starts by keeping those jobs separate.
What hiring actually costs
I'll use U.S. numbers because that's what I know. Adjust for your market.
A hiring model should include salary, benefits, equity, payroll taxes, recruiting time or agency fees, and the ramp before a new teammate understands the codebase and organization. Those inputs vary too much by market and company to hide behind one universal number. Use your own finance and recruiting data.
The output is also different. A senior engineer can own ambiguous product decisions, mentor teammates, negotiate interfaces across groups, and carry context that was never written down. Do not price that work as if it were interchangeable with an automated task runner.
What an agent team costs
Fleet Team is $299 per fleet per month with unlimited roles and an agent-run throughput allowance. Model-provider usage is billed separately, and Fleet does not claim dependable token or dollar totals for normal interactive runs. The worker runs on infrastructure you provide; the dashboard is hosted at app.fleetctl.ai.
Your comparison should therefore include four numbers you can verify: the Fleet subscription, your model provider's actual bill, the worker capacity you choose to provide, and the human time spent defining and reviewing the workflow. Run a small workflow first, record those inputs, then decide whether the repeated work justifies scaling it.
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. Working inside governed CI/CD workflows. Triaging issues. Monitoring deployments. Generating boilerplate. With a product-owner step refining tickets before development 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
Start with your own baseline: how many review hours, triage steps, and repeatable handoffs does the team perform in a normal week? Then move one bounded flow into Fleet and measure accepted output, intervention time, model-provider cost, and cycle time. A claim about triple-digit PR throughput is useless if the work is rejected or creates more review load than it removes.
The gain is not free. Somebody still defines the workflow, provides good input, approves consequential actions, and improves the process when evidence says it is failing. Fleet makes those handoffs explicit and repeatable; it does not turn weak requirements into reliable output by itself.
One more thing worth considering
Agent sessions do not have human scheduling constraints, but they do fail, stall, consume model budget, and produce work that needs review. The planning advantage comes from a defined workflow with visible state and bounded retries, not from pretending the agents are reliable employees.
Combine repeatable agent work with what your human team is good at—creativity, judgment, mentoring, and taste—and then judge the result with your own delivery data.