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Manage your AI agents and your cloud without babysitting them.

For a business whose AI agents went live and then became nobody’s job: AK Run’s AI agents watch every one of them and the cloud under them, our engineers fix what they find, and your people say yes to what changes. We cut Pixis’s cloud spend by 25% while its platform stayed up through the work.

Book a free workshop

Part of AI-Native Managed Services

A one-day workshop with the engineers who’d run it for you. AWS funding typically covers the workshop.

An AWS Premier Tier partner, one of about 300 among more than 130,000, validated by AWS for agentic AI consulting.

Every AI agent on its own line and the cloud it flies in looked after: AK Run’s crew holds the ropes, and your people say what goes live. The names are examples.

Launch day had a whole team.
Every day since, nobody.

AlertsSupport AI agent, an online shop

of wrong answers before a customer said so

    Example

    A growing business launches an AI agent with a contractor or a pilot team, it works, and everyone goes back to their day job. The cloud it runs on gets the same treatment, and from then on nobody is paid to keep either of them right.

    • The provider updates the model behind an AI agent, nobody reruns its tests, and from the next morning it answers differently.
    • A longer prompt or a bigger model can triple what each answer costs, and it turns up on the card statement at the end of the month.
    • The cloud under it has no owner either. Nobody can say which AI agent or service the AWS bill paid for, the servers are still sized for last year’s peak, and patches wait for a quiet week.
    • Prices, policies and customers’ questions change, and the AI agent keeps answering from what it was given at launch.
    • Its alerts go to whoever built it, and they’ve moved on to the next project.

    The alerts beside this are an example.

    Your AI agents keep working.
    We keep them working right.

    AK Run’s AI agents take the first look at every alert and every change. Our engineers give the yes on every fix, and answer for the result. Your people decide what reaches your customers, and stop babysitting the rest.

    AK Run’s AI agents

    • Watch every answer, alert, change and bill
    • Take the first look, group the alerts and gather the evidence
    • Rerun your real cases on every change

    Our engineers

    • Confirm the cause and decide the fix
    • Hold the pager
    • Make each change in code, through your pipeline
    • Sign the monthly evidence pack

    Your people

    • Say yes to each change your customers will meet
    • Grade a few answers a week, so the AI grader stays honest
    • See their corrections become tests

    Take Priya, who runs operations at the online shop in our example. Reading every support chat to catch what its AI agent got wrong would have taken her evenings, so nobody did, and a customer found the refund mistake nine days in. Now she grades ten answers on a Monday and says yes to a change or two a month, and she looks after five AI agents in less time than reading one AI agent’s chats would take. Example

    Six things that happen every week to AI agents and the cloud under them, from six kinds of business:

    A dental group

    After a model update

    Bookings AI agentModel updated by the provider, Tuesday

    186of 186 real bookings from last quarter, rerun and passed
    • WednesdayReleased to 1 in 10 patients first
    • ThursdayReleased to everyone

    Rerun by AK Run’s AI agent, released on the practice manager’s yes

    Example

    An insurance agency

    Answers graded every week

    Quotes AI agentThis week’s sample of real answers

    47of 50 right

    The threeEach quoted last year’s deductible on a van policy. Fixed, and kept as a test.

    Graded by AK Run’s AI agent, checked against your team’s grades

    Example

    A property manager

    Its own identity, only its tools

    Maintenance AI agentWhat it may use

    • Tenant messages
    • Calendar
    • Contractor portal: book a job
    • Contractor portal: pay an invoice

    SoIt can book a plumber. It can’t pay one.

    Set up by our engineer, checked on every call

    Example

    A wholesaler

    A cloud bill with owners

    AWS accountSeptember’s bill, every line given an owner

    $410a month off the bill, and every line now owned
    • Nobody’s$640 a month, found
    • Test servers2, on since spring, switched off
    • DatabaseSized for last year’s peak, sized down
    • Old filesMoved to cheaper storage as they age

    Found by AK Run’s AI agent, changed by our engineer in code, through their own pipeline

    Example

    The online shop, the same night with AK Run

    The 2:14 am failure

    Support AI agentWednesday 7 October

    1. 212 order lookups failing
    2. AK Run’s AI agent: a retry loop has filled the order database’s disk with logs, so 212 alerts are one problem
    3. Our engineer stops the loop, pauses lookups and grows the disk. Customers are told they’ll get an email.
    4. Back. No order lost.

    First look by AK Run’s AI agent, the yes by our engineer on call

    Example

    An accounting firm

    October’s evidence pack

    Documents AI agentEvery month, for each AI agent and its cloud

    • ReachedThe client portal and the tax software, read only
    • Controls5 on, all month
    • ChangedA model update on 2 October, rerun, passed
    • Cloud3 patches and 1 certificate, on schedule; a backup restored to test it
    • SignedThe managing partner, and our service lead

    Put together by AK Run’s AI agent, signed by our service lead

    Example

    More of what we watch and fix, for example

    Your AI agents

    • A booking AI agent offering appointments on a bank holiday. A physio clinic
    • A quotes AI agent still using last month’s price list. A builders’ merchant
    • A chat AI agent taking 20 seconds an answer at lunchtime. A restaurant group
    • A tool that renamed its fields overnight, so an AI agent’s calls started failing. A recruitment agency
    • An instruction hidden in a supplier’s invoice, aimed at the inbox AI agent. An accounting firm
    • A cost per answer that doubled when someone added a long example to the prompt. An online shop

    Your cloud

    • A certificate on the client portal two weeks from expiring. An insurance broker
    • Nightly backups nobody had ever restored. A dental group
    • A storage bucket of old exports, open to the internet. A property manager
    • Security patches three months behind on the booking site. A travel agency
    • Pages slowing in the month-end rush before any alarm fired. A payroll bureau
    • A database still sized for last December’s peak. A florist

    An AI agent can be up all day
    and wrong all day.

    Server monitoring says it’s running. It can’t say that the answers changed after Friday’s model update, or that each one now costs three times as much. Each decision below stops a failure that would otherwise reach a customer first. Our engineers make them with you, and AK Run runs them every day, for your AI agents and the cloud under them, the way AK AI Factory runs every AI system.

    Support AI agentThe same days, with AK Run

    of last quarter’s real cases passed on the new model, so it waited

      Example

      1. 1

        One register of every AI agent.

        Every AI agent goes on one list, the ones nobody listed too, with its owner, its model and version, the tools, data and cloud services it can reach, and its cost per task. Where someone else built them, we learn them like a good new hire: from the code, the traces and the people who built them.

        • Only the models you approved can be called, and only through the gateway.
        • AK Run’s Agent ControlPlane sends every model call through Amazon Bedrock AgentCore Gateway, and AWS Service Control Policies refuse any that doesn’t.

        WhyNo AI agent keeps running that nobody knew about, on a key nobody owns.

      2. 2

        Every change tried on your real cases, then yours to approve.

        We pin the model version, so a provider’s update is a change like a new prompt or a new tool, and goes through the same checks. Before a change goes live, we rerun last quarter’s real cases, with a sealed set kept back that nobody tunes to.

        • Where there are no tests, we first write them from your real cases, recording what each AI agent does today, so the next model update has something to be checked against.
        • Then your approver says yes, with the scores in front of them. Nothing your customers will meet changes without it, and it ships through your own pipeline.
        • It reaches a few customers first, with automatic rollback if the scores drop. A rollback can’t take back a refund already given, so we reconcile anything that moved money instead of assuming it undone.

        WhyA model update can’t quietly change the answers overnight.

      3. 3

        Watch the answers, not only the servers.

        A trace of every step records the prompt, the model, each tool call, what it cost and who approved it. It runs on OpenTelemetry, so it lands in the tools you already use.

        • A sample of real answers is graded every week, by an AI grader we check against your team’s grades on cases it hasn’t seen.
        • Drift alerts go to an owner when the scores slip or customers start asking new things.
        • Personal, card and health data are masked as they’re collected. Our AI agents run in your accounts and call models through Amazon Bedrock, in the AWS Regions you allow.

        WhyAn AI agent can’t be up all day and wrong all day without anyone knowing.

      4. 4

        A cost per task, and a ceiling.

        Every AI agent has a cost per task, and cost by team shows in AWS Cost Explorer, tagged at the gateway. A spend alert isn’t a cap, so each AI agent has both: an alert when its cost per task moves, and hard limits on time and spend per task.

        • Each job runs on the smallest model that’s as accurate, tested on your own cases, with a bigger one only for the questions that need it.

        WhyThe bill doesn’t grow faster than the work.

      5. 5

        Each AI agent on its own line.

        Each AI agent gets its own identity and only the tools its job needs. No shared keys, and no standing access.

        • Every prompt, answer, and tool call is checked before it can act.
        • We pin each tool by version and hash when you approve it, and a changed tool isn’t allowed until we’ve checked it again.
        • We attack your AI agents the way an attacker would, and every attack that works becomes a test.
        • Our own access works the same way. Day to day, we read through roles you grant and can revoke, scoped to the AI agents and systems in your plan, with no standing admin access. For emergencies there’s a break-glass role: it needs your on-call’s approval, expires within the hour, and every use is reported to you that day.

        WhyOne hidden instruction in an email doesn’t get to turn an AI agent against the business.

      6. 6

        Someone holds the pager.

        Each alert has an owner. AK Run’s AI agents watch every signal around the clock, take the first look, group the alerts and gather the evidence. A named engineer on call confirms the cause and decides what happens. The AI agents do the watching; the engineers do the deciding.

        • One switch stops one AI agent, or all of them, and we finish or hand back whatever was queued or half done, so nothing is dropped.
        • In an incident, our on-call engineer can pause or stop an AI agent under the runbook you agreed, and tells you that night. The fix itself still waits for your yes.
        • An incident becomes a test before it’s closed. Each month an evidence pack shows which AI agents ran, what they reached, which controls were on and what changed.

        WhyYour customer doesn’t find an incident before you do.

      7. 7

        Your cloud, run the same way.

        We watch it by what your customers feel, day and night: whether pages load and orders go through, as well as the servers’ graphs. Every alert has an owner.

        • Every AWS account and service gets an owner and a cost. We switch idle servers off, right-size the rest and move old data to cheaper storage as it ages, making each change in code through your pipeline.
        • Patches, upgrades, certificates and backups go on a calendar, each with an owner, and we try a restore before anyone needs one.

        WhyNobody is left with an outage, or a bill, they can’t explain.

      A one-day workshop first.
      Then we replay your last quarter.

      The ReplayReport: the online shop’s last 13 weeks

      Example

      We start with a one-day workshop, How to keep your AI agents and cloud running without an on-call team, with the engineers who’d look after them. Bring last month’s worst incident, and your cloud and AI bills.

      • In the morning we map what you run today: every AI agent and cloud account, who looks after each one, who gets woken at night and how often, and which alerts reach nobody.
      • Then we replay last month’s worst incident together, to see what would have caught it, who would have acted and how long it would have taken.
      • In the afternoon we design the target with you: which AI agents and services go on the register first, which tests to write from your real cases, who owns each alert, what limit each cost gets and when the patches run.
      • You leave with a findings report, a target architecture and a costed roadmap, yours to act on with us or without us.

      AWS funding typically covers the workshop.

      • After it, we spend two weeks read-only, replaying your last quarter on AK Run: the alerts, the changes, the answers and the bills, for your AI agents and the cloud under them. You get the report beside this: what we’d have caught, the gaps, the plan and the price. It’s yours either way.
      • The price is a fixed monthly one for the AI agents and cloud services in the plan, and comes with the replay’s report. It grows with more AI agents and systems, how much each one matters to the business and how much engineering time you want. Your cloud and model bills stay in your own accounts, where you can see them.
      • Then we take them on one at a time. Each AI agent goes on the register, with its version pinned, its tests written from your real cases, its alerts given an owner and its runbook rehearsed with your team. Each cloud service gets an owner, a cost and a patch calendar.
      • Every month you get the evidence pack, and we go through the scores, the costs and every incident with you.
      • If you want it back in-house, your team takes it. It all runs in your accounts, the runbooks are rehearsed with your engineers, and handover is a step in the plan, not a negotiation.

      What we’ll tell you, even when it means less work for us.

      • Switch some of it off instead. An AI agent nobody uses and a server nobody owns cost less switched off than looked after, so we list those first, before we price anything.
      • Don’t put every AI agent on a pager at night. The one that answers your customers needs one. The one that drafts the month-end report can wait until morning, and costs less to look after.
      • Run them yourselves if AI agents are what you sell. Hire the team, and we’ll set up the register, the tests and the runbooks for the people you hire, then hand them over.

      Our clients’ AI agents
      and clouds, looked after.

      At four of our clients, every new model is checked against production, an outage nobody could explain for months was explained in 60 hours, and two platforms stayed up while their cloud bills came down.

      An executive search firm

      C-suite and board search, worldwide

      Automatic rollback

      An AI agent returns a ranked, reasoned shortlist from thousands of candidate profiles. Each new ranking model is checked against production, with automatic rollback, and ranking is monitored and quality-controlled for the first time. Every workload runs on systems the firm owns, with zero disruption through its old vendor’s shutdown.

      A Canadian insurer

      Sold direct, about 1,000 policies a day

      60 hours

      A production outage kept coming back for six to ten months, and every restart erased the evidence. A 60-hour DevOps Care pod kept the evidence from the last outage and found the cause: Apache thread exhaustion, with about 800 database queries per request. It fixed it with connection pooling and session timeouts chosen to protect policy binding and payments. Amazon RDS Performance Insights and request tracing now show what used to be a guess, and the retainer continues.

      Pixis

      An AI marketing platform, 50+ microservices

      99.996% uptime

      The infrastructure bill kept rising, nobody could see which containers drove it, and nine AWS accounts blurred billing and governance. We standardized on Amazon EKS with GitOps pipelines, cut nine accounts to four, built cost dashboards on AWS CUDOS and right-sized the workloads. Cloud spend fell 25%. Compute cost fell 48%, and the platform stayed up 99.996% of the time through the migration and the cost work. Next come automated right-sizing and forecasting, and wider anomaly detection and budget alerts.

      DigitalStrike

      A marketing platform for hundreds of car dealerships; project work, the same practices

      99.9% uptime

      We hardened tenant isolation, cut cost with lifecycle policies, auto scaling and right-sized compute, and rolled out event-driven observability, with deployment automation and infrastructure as code. Uptime went from 95% to 99.9% and held under peak campaign load. Monthly AWS spend fell 20%.

      Checked by AWS. An AWS Premier Tier Services Partner, validated by AWS for agentic AI consulting, with 1,300+ engineers and more than 500 customer launches on AWS.

      How AI-Native Managed Services works Read the case studies

      We're able to focus on growing the product because we don't have to worry about DevOps or testing.

      Owneran equipment dealer software company

      Reasons to leave it
      another quarter.

      And what we tell owners who give them. Got another? Bring it to the workshop.

      Our AI agents are working fine.

      They may be. An AI agent can be up all day and wrong all day, and server monitoring can’t tell you which.

      The two-week replay shows what your last quarter held, on your own alerts, changes and bills, and the report is yours either way.

      Our engineers can look after them.

      They can, at the cost of the next thing they build and the nights they’re woken. Looking after an AI agent means a rerun on every model update, real answers graded every week, a cost ceiling and a pager, for every one of them.

      We do the watching and the routine fixes on AK Run, and your engineers keep building. If you’d rather they ran it, we set up the register, the tests and the runbooks with them and hand it over.

      There’s no budget for it this year.

      AWS funding typically covers the workshop, and AWS has funded part of the work for several of our clients. At the workshop we’ll tell you whether yours could qualify.

      The replay looks for money, too: servers nobody uses, a database sized for last year’s peak, AI spend nobody owns. In the wholesaler’s example on this page, two idle test servers switched off, the database sized down and old files moved to cheaper storage took $410 a month off the bill.

      We already have an IT provider, and we’re mid-contract.

      Keep them for laptops and networks. We run the cloud your software runs on, the software, and the AI agents.

      Many teams start with their AI agents, because no team owns them once they’re live. That part can start now, and the cloud can follow when the contract ends.

      We’re not on AWS. Can you still look after us?

      Often, yes. The way we look after AI agents and a cloud works on any cloud: the register, the reruns on your real cases, the traces, the limits, the patch calendar and an owner for every alert.

      The parts we’ve already built run on AWS, Agent ControlPlane among them, so on another cloud we’ll tell you at the workshop which of them you’d go without.

      Every AI agent needs someone
      holding its rope.

      We start with a one-day workshop with the engineers who’d hold those ropes. AWS funding typically covers the workshop. You leave with a findings report, a target architecture and a costed roadmap: every AI agent and cloud account you run, who looks after each one today, and what we’d watch first. If something isn’t worth looking after, we’ll say so.

      Book a free workshop