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Citius Cloud Services
infraBOT

Autonomous Automation as a Service (A4S)

Agents that find the problem, propose the fix, and wait for your approval.

infraBOT puts AI agents on the infrastructure you already run. They monitor continuously, diagnose against what they have seen before, and write out the commands they intend to run. An engineer approves before anything executes.

See it on your estateTalk to the team
  1. Detect
  2. Diagnose
  3. Propose
  4. Approve
  5. Execute
  6. Record
  7. Learn

Step four is a person. There is no setting that skips it.

Industry evolution

How IT operations got from shell scripts to agents.

Each stage fixed the one before it and brought a new limit with it. None of them could decide anything.

  1. Manual

    Manual Ops

    No scale, no data

    Done by hand, every time. Works while the estate is small, and leaves no record to learn from.

  2. Point automation

    Batch Processing

    Cron jobs & scripts

    Scheduled jobs take the repetitive work off a person. Each script knows nothing about the system around it.

  3. Point automation

    Siloed Automation

    Team-owned tools

    Every team builds its own. Parallel stacks, overlapping problems, no single way to govern any of it.

  4. Workflows & orchestration

    Config Mgmt & IaC

    Ansible | Terraform

    Infrastructure becomes code. Real progress, and a codebase somebody now has to test, version and patch.

  5. Workflows & orchestration

    Orchestration

    Integrated Day-1 & Day-2 workflows

    Steps join into workflows spanning build and run. Works while the environment matches what the workflow expects.

  6. Workflows & orchestration

    AIOps

    ML-driven metrics & predefined remediation

    Machine learning surfaces problems earlier. The fix it fires is still one somebody wrote in advance.

  7. Autonomous operations

    Autonomous Operations

    AI agents, with guardrails

    Agents write and run the workflows themselves, reading topology, dependencies and incident history to decide what to do. A human holds the approval.

infraBOT sits at that last stage. Underneath, it is still running scripts, Ansible and Terraform — your team is just not the one writing them.

Why it's needed

In regulated industries, most of the operations team ends up doing compliance work.

Compliance generates exactly the kind of work scripted automation handles badly: repetitive, evidence-heavy, and different enough each time to break a script.

Highly regulated industries

Where you have to prove control, not just have it.

Sectors

  • Banks
  • Exchanges
  • Insurance
  • Hospitals

Regulators

  • RBI
  • SEBI
  • IRDAI

Heavy compliance requirements

Not a yearly audit. Continuous work.

  • Hardening to a benchmark
  • Patching, on a cycle
  • Upgrades and migrations
  • Evidence and reporting

Manual, repetitive & fragile operations

How it gets done in practice.

  • Change windows, out of hours
  • People on bridge calls, watching
  • Records assembled after the fact
  • Repeat for every environment
  • Legacy scripts break on drift
  • Rework when things fail

Most of IT's capacity goes to maintenance

What that costs across an estate.

of IT time spent on compliance maintenance

Little room left for innovation

What is left for the work the business asked for.

of IT time left for anything new

Figures as presented in CitiusCloud's infraBOT briefing material. Indicative of regulated-enterprise estates, not a measured benchmark.

How the agents work

What happens between finding a problem and fixing it.

Eight stages, running against OpenShift Container Platform today. Agents handle seven. The fifth is a person.

  1. Scan & detect

    Connected to the cluster and watching, not polling.

    Live on OpenShift (OCP)

  2. Diagnose & propose

    Agents collaborate on cause and remedy.

    • Knowledge check
    • Root-cause diagnosis
    • Fix proposal

  3. Issue confirmed?

    A signal is not automatically an incident.

    Audit trail only

  4. Human in the loop

    1. Raise ticket

      Into your change process, automatically.

      • Built-in
      • ServiceNow
      • Jira

    2. Human approval

      An engineer approves, amends or rejects.

      Hardcoded gate

    3. Execute fix

      The remediation runs, with the approval recorded against it.

  5. Generate RCA

    Cause, timeline, evidence, resolution — written from what happened.

  6. Write back to knowledge base

    The incident becomes something the next diagnosis can search.

Institutional memory grows with every incident.

Human in the loop

Nothing executes without approval.

Ticketing, approval and execution sit inside a boundary a person controls. The approval is written into the loop rather than configured on top of it, which is the difference between automation that runs on its own and automation that runs unattended.

Not confirmed → Audit trail only

Nothing was wrong. No ticket, no remediation, just an entry recording that the check ran. A false positive costs a log line, not a change.

Institutional memory grows with every incident.

Stage eight feeds stage two, so the same fault on a different cluster gets recognised instead of investigated from scratch.

The first recording below is this loop, running on a broken namespace.

See it in action

Three operations, recorded end to end.

Same shape each time: a structured request, an approval, automated execution, evidence you can drill into.

Kubernetes operations

The loop above, on a broken namespace.

Diagnosis, approval, remediation, automatic RCA.

Two faults in one namespace: a Service selector matching no pod, and a Deployment pointing at a ConfigMap that was never created. The agent finds both and writes out the fix.

1:31Kubernetes operations agent — diagnosis through RCA

What you’re seeing

  1. Diagnosis names both faults, down to the wrong selector value.
  2. Proposed Commands shows the two remediations as reviewable manifests.
  3. On approval, remediation runs and the RCA opens at P2 — High.
Namespace
agent-test
Faults found
2
Severity
P2 — High
Release gate
Human approval

Outcome

Nothing moved until a person approved it.

Linux hardening scan

Compliance scanning, traceable to the terminal line.

Rule-level execution and evidence generation.

A STIG hardening scan scoped control by control and run against selected hosts, reported at four depths: summary, host, rule, and the raw Ansible output behind each finding.

4:28Linux hardening scan — request, execution, compliance evidence

What you’re seeing

  1. All 22 hardening controls included by default, each one excludable.
  2. Execution visible per endpoint and per control as it runs.
  3. 52.4% compliance across 42 rules, then drill from a finding to its terminal log.
Benchmark
CIS Ubuntu STIG
Endpoints
2
Total rules
42
Result
22 compliant / 20 findings

Outcome

Evidence comes out of the execution rather than being assembled afterwards. An Ansible task can report OK while the control it tested comes back as a finding.

AWS VPC provisioning

From a filled-in form to a VPC that exists.

Orchestration from structured intent to verified infrastructure.

A network request captured as parameters, approved, provisioned with Terraform, and reported back with the resource IDs it created.

1:57AWS VPC setup — request, provisioning, console verification

What you’re seeing

  1. CIDR block, subnets, availability zone and tags captured as a request.
  2. After approval, each resource appears as it is created.
  3. The recording leaves infraBOT to confirm the VPC in the AWS console.
VPC name
infrabot-vpc
CIDR block
10.50.0.0/16
Availability zone
ap-south-1a
Verified in
AWS console

Outcome

Structured input in, real infrastructure out, checkable in AWS itself.

Enterprise-ready

The rest of what an enterprise needs before this goes near production.

Fifteen capabilities. Most of them are unglamorous. All of them come up in procurement.

Configuration & experience

Configured from the interface, not from a release.

Dynamic Form Builder

Request forms shaped to business requirements.

Report Builder

Automation reports configured per customer.

Dynamic Dashboard

Personalised home dashboard, widgets and metrics.

Script & Use-Case Onboarding

New automation onboarded from the UI, without a new build.

Multi-Cluster Onboarding

Multiple infrastructure clusters, managed centrally.

Infrastructure & operations

What the automation runs against, and how work gets approved.

Infrastructure Inventory

The target inventory automation runs against.

Custom Workflow Builder

Customer-specific workflows with multi-level approvals built in.

ITSM & Ticketing

Built-in ticketing, plus connectors for ServiceNow and other ITSM tools.

Your data stays yours

Credentials and documents stay in your environment.

Secret Manager Integration

Customer-managed secrets stay in the customer environment.

Customer Storage Integration

Documents remain in customer storage.

Security & governance

The controls sit inside the automation, not around it.

Granular RBAC

Fine-grained roles across users, teams and capabilities.

Secure & Compliant by Design

RBAC, approvals, audit trails and policy guardrails, built in.

Intelligence & improvement

Why the service is worth more a year in.

Open & Extensible

Integrates with enterprise tools, APIs and external services.

Context Aware

Understands dependencies, topology and business impact.

Continuous Improvement

Learns from every action, outcome and remediation.

Continuous improvement

The part that gets better on its own.

The catalog is the catalog and the guardrails are the guardrails. The knowledge base is the exception: an incident diagnosed this quarter becomes context for one diagnosed next year, by an engineer who was not there the first time.

An incident happens

An agent finds the cause and remediates once somebody approves.

Evidence is generated

The RCA is written from what actually happened.

Memory is written

It goes back as something searchable, not an attachment on a closed ticket.

The next diagnosis is better

The knowledge check now has this incident to draw on.

Each cycle leaves the next diagnosis better informed.

From automation that reacts, to operations that run themselves under supervision.

Before you ask

The questions infrastructure teams open with.

Will the agents change things in production without us?
No. Human approval is a hardcoded stage in the loop rather than a configurable one. infraBOT detects, diagnoses and proposes, then stops until one of your engineers approves, amends or rejects the remediation.
What happens when the agent is wrong about there being a problem?
Nothing gets remediated. If the issue is not confirmed at the decision gate, the loop stops before the ticket stage and logs an audit-trail entry instead.
Does it work across on-premise and multiple clouds?
Yes. infraBOT is built for hybrid estates spanning on-premise data centres, public clouds and edge locations, and it attaches to what you already run rather than requiring a migration.
Where do our credentials and documents live?
In your environment. Customer-managed secrets stay in your secret manager and documents stay in your storage; infraBOT integrates with both rather than taking custody of them.
Which environments does the agentic loop run against today?
OpenShift Container Platform is live now. The loop is being extended from that implementation.

See infraBOT on your estate

We'll walk the loop against an environment that looks like yours.

Book a demoTalk to the team