02 · Agentic AI Operations

Agents that fix it. Policy that decides what they're allowed to touch.

We put supervised AI agents into your operations stack. They collapse alert storms into one incident, trace the cause across layers, correlate it to the change that caused it, and execute the fix — inside limits you define. Every action is scoped, logged and reversible. You choose how much rope they get, one capability at a time.

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Who this is for

If two or more of these describe you, we should talk.

Operations teams drowning in alert volume, where the monitoring channel has been muted because it cries wolf.
Teams paged repeatedly for incidents they have already solved and documented — the disk fills, the pod thrashes, the certificate expires.
Organisations where out-of-hours on-call is a retention problem as much as an operational one.
Anyone whose mean time to resolution is dominated by diagnosis, not by the fix itself.

What's included

What Agentic AI Operations covers — 6 capabilities, all of them visible.

Nothing hidden behind a click. If we can’t describe it plainly here, we shouldn’t be charging you for it.

Triage agent

Collapses an alert storm into a single incident with one owner, suppresses the duplicates, attaches the affected services and dependencies, and opens the ticket with a timeline already written. Usually the first thing clients feel, because the noise reduction is immediate and needs no trust at all.

L1 from day one
Alert correlation
Deduplication

Root-cause agent

Traces the anomaly down through application, platform, network and infrastructure, correlates it against deployments and configuration changes in the window, and produces a ranked set of candidate causes with the evidence for each. Cuts the diagnosis phase, which is where most of the resolution time actually goes.

L1–L2
Change correlation
Dependency tracing

Remediation agent

Executes the runbook you already wrote: restart, scale, roll back a deployment, fail over, expand a volume, clear a queue, rotate a node. Starts at supervised approval and graduates capability by capability. This is the one that takes the 3am page away.

L2 → L3
Policy-gated
Auto-rollback

Network operations agent

Self-diagnosing, self-healing networks made concrete: interface and path monitoring, automatic failover between links, configuration drift detection against a golden template, and correlation of user-reported problems to the segment actually at fault.

L2 → L3
Config drift
Path failover

Cost agent

Watches spend against forecast hourly rather than discovering the problem in next month’s bill. Flags anomalies within hours, identifies the resource and the change that caused them, and raises rightsizing recommendations as pull requests against your infrastructure code.

L1–L2
Anomaly detection
Rightsizing PRs

Service desk agent

Handles the tier-one queue: password and access requests routed through your existing approval flows, common how-do-I answers grounded in your own documentation, and safe self-service actions for authenticated users. Escalates cleanly with full context instead of making the human start over.

L2 → L3
Ticket deflection
Authenticated actions

Run it in shadow mode for four weeks.

Seen enough? This is the smallest way to start.

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What makes us different

Two things we'll put in writing.

“You choose the autonomy level”

Nobody starts at the top. Every capability climbs the ladder independently and only after it has earned it — and the ladder is written down, on this page, so you always know exactly what the agents may do.

“Every action is a hypothesis with a rollback”

After acting, the agent watches the objectives it was trying to restore. If they do not recover inside the expected window it reverts and escalates. An action that cannot be automatically reversed does not get automated in the first place.

The autonomy ladder

You decide where you sit. Nobody starts at the top.

Every capability climbs this ladder independently, and only once it has earned it. This is written down so you always know exactly what the agents may do — and so you can buy the bottom of it without buying the top.

L0Manual
Where most teams are. Humans detect, diagnose and remediate. Runbooks exist as documents. This is the baseline we measure everything else against.
L1Assisted
Agents observe only. They correlate the alert storm into a single incident, assemble a timeline, and draft a root-cause analysis with the suspect change highlighted. A human still does everything. This is where every engagement starts, and it earns its keep on alert noise alone.
L2Supervised
The agent proposes a specific action and waits. Your engineer sees the diagnosis, the proposed command, the expected effect and the blast radius, and clicks approve or declines. Every decision becomes signal for what the agent may do unattended later.
L3Delegated
For named, well-tested classes of incident only, the agent acts without waiting — inside a policy that caps what it can touch, in which environments, within which change windows. It verifies the result against your objectives and rolls itself back if the numbers do not recover. You get the notification, not the page.
L4Autonomous
A small, deliberately narrow set of operations that have run correctly at L3 for long enough to stop being interesting. Restarting a stateless service. Expanding a volume. Draining a failing node. Nothing that touches identity, DNS, data or money ever reaches this level — by design, not by omission.

How it's built

Four layers, and the one everybody forgets.

Most agentic setups fail at the telemetry layer. Without a change feed from your pipelines, an agent can find a symptom but never a cause — which is the difference between a smarter alert and an actual diagnosis.

01 Telemetry

One stream, not six consoles

Logs, metrics, traces and events normalised into a single vendor-neutral pipeline using open instrumentation standards, plus a dependency graph of what calls what and a change feed from your CI/CD and infrastructure repositories. Without the change feed, an agent can find a symptom but never a cause.

02 Reasoning

Grounded in your incidents, not general knowledge

The agent reasons over your own runbooks, your past incidents and your architecture — retrieved and cited, so every conclusion points at the document or the deploy that supports it. Correlation against recent changes is what turns "latency is up" into "latency is up because of the config change merged forty minutes ago".

03 Action

Narrow tools, explicit permissions

Agents do not get a shell. They get a defined set of parameterised operations — scale this deployment, restart that service, expand this volume, fail over that database — each with its own least-privilege credentials and its own policy rule about when it may run. Policy is code, version-controlled and reviewed like anything else.

04 Verification

Every action is a hypothesis with a rollback

After acting, the agent watches the objectives it was trying to restore. If they do not recover inside the expected window, it reverts and escalates to a human with the full trace. An action that cannot be automatically reversed does not get automated in the first place.

Guardrails

What happens when it's wrong.

This is the real question, and most vendors bury it. Here is the whole answer, unprompted — because a buyer who can see the limits is a buyer who can say yes.

Least privilege per agent

Each agent gets its own credentials scoped to its own operations. The agent that restarts pods cannot read your database, and there is no shared administrative identity anywhere in the design.

Blast radius caps

Hard limits on how much can change at once — a maximum share of a fleet, one environment, one region, one action per incident before a human is required.

Policy as code

What each agent may do, where, and when is expressed in a version-controlled policy engine, reviewed by pull request. You can read the rules. You can also change them without asking us.

A permanent human gate

Identity and permission changes, DNS, certificate authorities, data deletion, production schema changes, anything affecting billing — these always require a human, at every level of the ladder, permanently.

Change windows and freezes

Agents respect your maintenance calendar and your release freeze. Nothing autonomous happens during a code freeze or a peak trading period.

Full audit trail

Every observation, decision, action and the reasoning behind it, retained and exportable. When your auditor asks what changed production last Tuesday, there is one answer and it is complete.

Staged validation

No capability reaches production autonomy without running in shadow mode against real incidents first, then supervised, with a documented accuracy record at each stage.

A kill switch

One control disables all autonomous action immediately and reverts to human operations. It is tested as part of onboarding, not described in a document.

Straight answers

The five questions everyone asks.

Including the one where the honest answer is uncomfortable.

What if it takes down production?

It can only perform operations we have explicitly defined, in environments we have explicitly allowed, within blast-radius limits you set. Every action is verified against your objectives and reverts automatically if they do not recover. And no capability runs unattended until it has been right, in shadow mode, on your own incidents, for an agreed period. The honest version: it will occasionally be wrong. The design assumes that, which is why every automated action is reversible.

Are you replacing our engineers with AI?

No — we are removing the part of the job they hate. Nothing in this model does design, architecture, judgement calls or anything novel. It handles the repeat incidents your team has already solved and documented. The measurable outcome is fewer 3am pages, not fewer people — and the engineers who would have taken those pages are usually the strongest internal advocates for buying it.

Our environment is too unusual for this.

Probably true for autonomous remediation on day one, and we would not propose it. Correlation and root-cause assistance work on any environment with decent telemetry and deliver value in weeks without touching anything. Remediation gets added only for the incident classes that turn out to be repetitive — and in every environment we have assessed, some of them are.

How do we audit what it did?

Every observation, decision and action is logged with its reasoning and retained for your retention period, exportable to your own SIEM. Agent actions are attributed to named agent identities, so they are distinguishable from human activity in your existing cloud audit trail. An agentic setup is generally more auditable than the human process it replaces, because nobody logs why they ran a command at 3am.

What happens to our data?

Telemetry is processed inside your own cloud tenant wherever the architecture allows it. Before anything is connected we agree the boundary in writing: which systems are read, where processing happens, which regions apply, what is retained and for how long, and what — if anything — leaves your tenant. That document is part of the engagement, not an appendix to it, and it is signed before the first agent is pointed at your estate. If any answer in it would fail your compliance review, you find that out before you have spent anything.

Engagement

Four weeks to first value, no autonomy required.

Weeks 1–2

We instrument, connect your telemetry and change feeds, and map your incident history to find what actually repeats.

Weeks 3–4

Agents run in shadow mode on live incidents — observing, correlating, drafting diagnoses — while changing nothing. You get a report on what they caught, what they got right, and which incident classes are worth automating.

Then

Only then do we discuss moving anything to supervised action — and only for the capabilities the report shows are worth it.

Proof

This practice, in the field.

Tri-PacDistributionHybrid

Taking over IT operations for a distributor with no internal IT team

Tri-Pac needed round-the-clock cover without hiring for it. We inherited an undocumented estate, stabilised it, and moved it onto a managed service with proactive monitoring and tested recovery.

Coverage, from Philadelphia and Hyderabad: 24×7Coverage, from Philadelphia and Hyderabad
IT staff Tri-Pac had to hire to get it: 0IT staff Tri-Pac had to hire to get it

Read the case study

Managed Cloud Services · Agentic AI Operations

Often bought alongside

Managed Cloud Services

Landing zone to steady state on AWS, Azure and Microsoft 365. We design the account structure, move the workloads, run the platform round the clock, and take the cost curve down while we are in there.

Explore 03

Platform Engineering & DevOps

The platform layer your product team keeps rebuilding badly: infrastructure as code, pipelines that deploy safely, Kubernetes that upgrades on schedule, observability that answers questions.

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Next step

Run it in shadow mode for four weeks.

It changes nothing and touches nothing. At the end you will have a report on what your alerting is costing you in noise, which of your incidents were repeats of earlier ones, and exactly how much of last month you could have got back.

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