About
On a growing AWS estate, the day-to-day work leaned on a single operations engineer: patching, provisioning, incidents, database operations and cost. It was slow, inconsistent, and impossible to scale with one pair of hands. The question was not whether to change the model, but whether an autonomous engine could carry the load instead.
Rather than present a proposal, Firemind ran a live deployment of its IT Operating Engine inside the client’s own AWS development and QA account over two months. It ran eleven use cases across eight operational domains, executing inside the client’s account with human approval on anything high-risk, and proved the operating model on real infrastructure rather than a slide.
Scope: the engagement ran on a single AWS development and QA account over two months, not the client’s wider production or corporate estate. All figures on this page relate to that environment.
Challenge
The estate spanned compute, managed databases, serverless functions and container workloads, and day-to-day operations rested on one person. Three problems compounded:
- Routine work was bottlenecked on one engineer. Patching, provisioning, resizing, incident triage and database operations all queued behind the same person, crowding out higher-value work.
- Infrastructure drifted. End-of-life database engines, functions on retired runtimes and underused capacity built up quietly, untracked until someone went looking.
- Proof was needed before commitment. Validated autonomous execution on the real estate, across the full operational surface, not a demo on a clean sandbox.
The work had to cover the whole operational surface and run on an environment that would not be tidied up first.
Solution
Over two months, Firemind ran autonomous cloud operations on the client’s AWS development and QA estate, powered by its IT Operating Engine. Following Firemind’s connect, scan, heal and monitor model, it built a live map of the estate and operated it end to end, inside the client’s own AWS account, audit-logged, with human approval on high-risk actions.
Connects to the client’s existing AWS tooling, inside its own account.
Builds a live inventory across compute, databases, functions and containers.
Provisions, patches, rebuilds and resolves incidents, high-risk actions approved by a human.
Tracks the estate on an ongoing basis so issues are caught as they arise.
The live deployment proved three things:
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It provisions and reshapes infrastructure on request. Service requests ran end to end: EC2 provisioning, VM resize, EBS volume attachment, security group rule changes, Amazon S3 bucket creation with automatic public-access remediation, and an instance scale-up. The day-to-day infrastructure queue cleared itself.
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It rebuilds a database platform mid-task, and recovers from its own errors. A production-to-test clone converted a serverless DocumentDB into an EC2-based cluster. The first attempt failed on missing VPC and KMS dependencies, so the engine spawned two parallel service requests, resolved both in roughly eight minutes, and completed the clone in approximately 59 minutes total, with no human in the loop.
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It resolves incidents surgically, and patches in minutes. On a CPU alarm, the engine identified and terminated the offending process rather than rebooting the host. A full dev and QA patching report was generated in under nine minutes, and a database host patched end to end in approximately 22 minutes, including graceful reboot, pre- and post-checks, and service validation.
None of this ran unchecked. A medium-risk change was routed for human approval rather than auto-remediated, and the client kept full control over what could auto-execute, what needed sign-off and what was blocked.
Results
Running live on the client’s AWS estate over two months, the deployment carried real infrastructure work end to end. Beyond the headline figures:
- Infrastructure provisioned, resized and rebuilt on demand. From EC2 and EBS changes to a full DocumentDB-to-EC2 cluster conversion, executed autonomously.
- Full estate visibility from day one. A complete inventory with Amazon CloudWatch, AWS Security Hub and Amazon GuardDuty ingestion validated.
- A modular, repeatable model. Progressed into commercial business case discussions, with a next phase scoped across Elasticsearch rolling patches, container vulnerability remediation and message-queue recovery.
For the client’s one operations engineer, the question is settled: a live AWS estate can be provisioned, patched, repaired and optimised autonomously, at a pace and consistency a single person cannot sustain.
