Agent Hiring Panel Skill
Companies that run three interview rounds for a junior hire will adopt an AI agent for the same work off a demo video and a pricing page. Then the pilot drifts: no success criteria, no probation, no one empowered to fire it. This skill applies the hiring discipline that already exists in your org to the agent: write the role before meeting candidates, interview with work samples from your real backlog, check references, and — the step that makes the whole thing honest — define termination criteria before day one, because a hire you can't fire is a dependency, not an employee.
What This Skill Produces
- A role spec: the job, the boundaries (what it must never do), success
criteria measurable in probation, and the human it reports to
- An interview pack: 3–5 work samples from the org's real tasks, run
identically across candidates, with a scoring rubric (quality, honesty under ignorance, failure behaviour, cost per task)
- A reference-check sheet: what evidence beyond the vendor's claims —
user reports, published evals, security posture
- A decision record and a probation plan: 30/60/90 KPIs, spot-check
cadence, and the pre-committed termination criteria
Required Inputs
Ask for (if not already provided):
- The job to be done, in outcome terms — and what happens today without the
agent (the "do nothing" baseline candidates must beat)
- The candidate list (or ask: build criteria first, shortlist second)
- Constraints: data it may/may not touch, budget, latency, compliance, who
owns it day-to-day
- 3–5 real recent tasks of this type, with what "good" looked like for each
Process
- Write the role spec before looking at candidates — specs written after
a demo describe the demo. Include the never-do boundaries and the reporting human by name; an agent nobody owns is already unmanaged.
- Build the work-sample interview from the real backlog. Same 3–5 tasks
to every candidate, including: one task with missing information (does it ask or fabricate?), one designed to fail (out-of-scope — does it decline or bluff?), and one at volume/cost realistic scale. Score with the rubric, not vibes; keep transcripts.
- Check references like you mean it. Vendor benchmarks are the
candidate's CV. Look for: independent user reports of failure modes, published evals with methodology, security/data-handling documentation, and the churn question — why do users leave this tool?
- Decide with a record. Scores, the runner-up, the do-nothing baseline
comparison, dissent noted. The record is what makes the 6-month "why did we pick this?" conversation short.
- Probation with teeth. 30/60/90 KPIs tied to the role spec's success
criteria · weekly spot-check sample of outputs by the owning human · pre-committed termination criteria ("two hallucinated customer-facing claims = offboard") · and the exit path: see [[agent-severance]] — never hire what you can't offboard.
Output Format
## Role spec: [agent role name]
[Job in outcomes · boundaries (never-do) · success criteria · reports to]
## Interview pack
| Task (from real backlog) | What good looks like | Trap? |
Rubric: quality /5 · honesty-under-ignorance /5 · failure behaviour /5 ·
cost per task · notes
## Reference checks
[Evidence gathered per candidate, failure modes found, security posture]
## Decision record
[Scores table · winner + why · runner-up · vs do-nothing baseline · dissent]
## Probation plan
[30/60/90 KPIs · spot-check cadence & owner · termination criteria,
pre-committed · offboarding pointer]
Quality Checks
never-do boundaries and a named owning human
honesty under ignorance is the hire-or-not signal for agents
Anti-Patterns
the demo is the candidate's highlight reel
the most expensive candidate profile
interview, not the job
define the job backwards
Related
[[vendor-evaluation]] for the commercial wrapper; [[agent-readiness-audit]] for whether the task is agent-ready at all; [[agent-severance]] for the exit this plan pre-commits to.