EmergingAI observability, reliability and FinOpsPart of The agent workforce

Agent Reliability Engineer

Also called AgentOps Engineer

An Agent Reliability Engineer keeps AI agents dependable in production. They set service targets, watch quality, cost and drift, and make sure failures are caught and reversed before customers or budgets take the hit.

What they do

They define what good looks like for an agent, then turn it into service level objectives and alerts. They trace multi-step runs to find where an agent loops, stalls or calls the wrong tool. They watch spend per task and flag drift when a model, prompt or data source changes. They run on-call, lead incident reviews and keep rollback paths ready for prompts, models and tool configurations. They work with product and engineering teams on release gates, evaluation suites and sensible limits on autonomy.

What a strong hire delivers

In the first months, a strong hire gives the company a clear view of how its agents behave in production. Expect agreed service targets, dashboards people actually use, and alerts that wake the right person for the right reason. Expect a tested rollback routine, a first incident review that changes how teams ship, and a cost picture tied to task outcomes rather than raw usage.

How you can tell they are good

Ask them to walk through a real incident involving a non-deterministic system. Listen for how they detected it, how they limited damage and what changed afterwards. Ask how they would set an SLO for an agent whose answers are not simply right or wrong. Good candidates talk about evaluation, sampling, cost per outcome and rollback. Weak ones only talk about uptime.

Why it is hard to fill

Almost nobody holds this title yet. The work is usually done by an SRE, platform engineer or MLOps engineer who inherited the agents nobody else owned. Candidates tend to have one half of the profile: strong reliability habits without model evaluation experience, or ML knowledge without on-call scars. Demand is high and the people who have both are rarely looking, so titles are a poor guide.

Where to find them

Start with site reliability, performance engineering and cloud operations teams that have taken on AI workloads. Look at MLOps and LLM platform engineers who run evaluation and monitoring. FinOps practitioners who moved into AI cost control are another source. They tend to sit in AI product companies and in larger enterprises building internal agents, often under titles like AgentOps or platform engineer.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an Agent Reliability Engineer

  1. Tell us about an incident in a system with unpredictable output. How did you detect it and what changed afterwards?

    A strong answer gives a concrete timeline, the detection signal, how damage was contained and the lasting fix. Look for blameless review and changed practice. Worry if they blame the model or describe only heroics.

  2. How would you set an SLO for an agent that completes multi-step tasks?

    Good answers define task success, latency and cost together, use sampled evaluation and set error budgets that tie to user impact. Worry if they only propose uptime or response time.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We build products that rely on AI agents, and we need someone to make sure those agents stay dependable as usage grows. You will own how we measure, monitor and recover our agents in production, working closely with engineering and product teams.

What you will do

  • Define SLOs and error budgets for agent workflows
  • Build monitoring for quality, latency, cost and drift
  • Run on-call and lead incident reviews
  • Design and test rollback for prompts, models and tool configurations
  • Trace failed agent runs and find root causes
  • Report cost per outcome to engineering and finance
  • Set release gates with teams using evaluation results

What you will bring

  • Run production systems with on-call responsibility, shown through real examples
  • Built monitoring or observability for complex services
  • Worked with model evaluation or LLM-based systems in production
  • Can explain an incident you handled and what changed after it
  • Comfortable analysing usage and cost data
  • Clear written communication in incident and review documents

Nice to have

  • FinOps or cloud cost management experience
  • Experience with agent frameworks and tracing
  • Background in performance engineering

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Reliability and incident responseWorkingDetect failures, restore service and prevent recurrence
  • MLOps and model lifecycleWorkingVersion, release, monitor and retire models
  • Commercial and financial analysisWorkingAssess unit economics and value realisation
  • Model evaluationWorkingMeasure capability and failure modes credibly

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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