Hire engineers who put bounds on a multi-agent run.
CrewAI makes a multi-agent prototype quick to assemble. What it does not give you is a bound on delegation, a way to stop a crew that has started looping, or state to resume from when one step fails. Those are the three things that decide whether a crew can run unattended. Our engineers have shipped CrewAI systems in production, and harder stacks than it.
What they own.
- Design crews where task delegation is bounded rather than open-ended
- Choose sequential or hierarchical process per job instead of by habit
- Put a budget across the whole run, not per agent, so cost cannot compound
- Detect a crew that has stalled or started looping and stop it
- Persist state so a failed step resumes instead of restarting the crew
- Know when a job has outgrown CrewAI and say so
What actually breaks, and what they do about it.
Delegation with no ceiling
An agent that can hand work to another agent that can hand it back will, and the run does not end on its own. A bound on delegation depth and a stop condition are what separate an unattended crew from one somebody has to watch.
The budget is per agent, not per run
Cost controls applied to each agent individually still let a five-agent crew spend five times what anyone approved. The budget has to cover the whole run, and the run has to stop when it is spent.
No state to resume from
When one task in a sequential crew fails, the default is to start the crew again, repeating and re-paying for every step that already succeeded. Checkpointing per task turns a failure into one retry.
The job outgrew the framework
Role-and-task modelling stops fitting once the work needs durable execution, fine-grained human approval, or branching that is not a sequence. Recognising that boundary early is cheaper than discovering it in production, and porting off is a normal outcome rather than a failure.
What you can ship with them.
Tools they reach for in production.
- Framework
- CrewAICrewsTasks
- Control
- Run budgetsLoop detectionHITL
- State
- CheckpointsResumptionPostgres
- Run
- TracesPer-agent evalsCost per run
Seniority: Engineers who have already worked on this stack, and on harder ones than it.
Five stages.
The top 3% remain.
Every stage asks the same question: can they keep AI running once real customers are using it? Getting something started is the easy part, and it is not what we screen for.
400 applicants
3%of applicants reach
your shortlist
What removes them
We start with something they built
100% → 20%One real system, pressed hard. How much traffic did it take? What broke first? Who got the call when it did?
We break something and watch them fix it
20% → 9%A working system with a bug hidden inside it. Anyone can build a demo in a weekend. Fixing code you have never seen is the actual job.
How will you know it is working?
9% → 5%Before they write anything, they have to tell us how they would test it, and what they would do when it gets an answer wrong.
Make it fast without running up the bill
5% → 4%We give them a speed target and a budget, then ask them to explain the tradeoffs they made to hit both.
It is late and the AI got it wrong
4% → 3%What do you do first? How do you find out what happened, undo it, and explain it to the customer in plain words?
Often hired together.
AI Agent Engineers
Builders of agents that plan and take real actions in your systems, safely.
Generative AI Engineers
All-rounders who ship a whole AI feature end to end, or anchor a pod of the specialists below.
MLOps & AI Infra Engineers
The infra people who keep AI fast, efficient, and observable in production.
Find the people to accelerate your roadmap.
You don’t need more resumes. You need proven AI engineers embedded in your workflow and ready to build from day one. Tell us what’s missing and we’ll line up a shortlist.