AI INTEGRATION
Self-healing infrastructure AI that acts before you wake up. Agents watch your systems, diagnose failures from logs, traces and recent changes, apply the fixes you have already approved, verify recovery, and escalate anything genuinely new - with every action logged and reversible.
What a self-healing agent does at 3 a.m.
A self-healing infrastructure agent is an autonomous worker on your operations stack: it detects a failure, gathers the evidence a responder would gather, forms a probable cause, applies a remediation from a set you have pre-approved, verifies the system recovered, and escalates when the failure is not one it knows.
The category is often called AIOps, and much of what has been sold under that name is anomaly detection that produces more alerts than it resolves. The distinction we care about is whether the agent acts. Detection without remediation moves work from finding problems to reading notifications about them, which is not the same as absorbing the load.
What makes acting safe is that the remediation set is finite, agreed in advance and reversible. The agent is not reasoning its way to novel infrastructure changes at three in the morning. It is executing the runbook your team already trusts, faster, and stopping the moment it leaves familiar ground.
You probably need this if you recognize these
- On-call is woken for things a runbook already solves.
- The first twenty minutes of every incident is gathering the same context.
- A nightly job fails, self-recovers, and nobody investigates until it does not.
- Alert volume is high enough that people have started filtering it.
- Recovery time depends on which engineer happened to be paged.
- The same incident class has recurred more than three times this quarter.
What you get
Detection tuned to symptoms
Alerting on what users experience rather than on every metric that can cross a threshold.
Automated diagnosis
Logs, traces, recent deployments and dependency health gathered and correlated into a probable cause before a human is involved.
An approved remediation set
The fixes your team already trusts, encoded: retry, restart, roll back, scale, clear a cache, fail over, pin a dependency.
Verification
Confirming the system actually recovered, not merely that the command exited zero.
Escalation packages
What happened, what was tried, what it means and what is recommended, attached to the page.
Post-incident records
A timeline assembled automatically, which is the part everyone intends to write and rarely does.
A recurrence report
Which incident classes keep returning, so the underlying fix gets prioritized.
How an incident is handled
Self-healing infrastructure AI agents
Detect
A symptom crosses a threshold or a synthetic check fails. The agent claims the incident.
Self-healed - retried with fallback tool. Human not required.
Gather
Logs, traces, recent deploys, dependency status and comparable past incidents are collected.
Diagnose
Evidence is correlated into a probable cause with a confidence score attached.
Remediate
If the cause matches an approved remediation, it is applied within its blast-radius limit.
Verify
Recovery is confirmed against the original symptom before the agent stands down.
Escalate or close
Unknown causes page a human with everything already gathered. Known ones close with a timeline.
The operations department, staffed
Self-Healing Infra Agent
Detects, diagnoses and repairs within its approved set, and escalates what it cannot.
Incident Response Agent
Coordinates the response: comms, context and the responder handover.
Dependency & Patch Agent
Removes a recurring incident cause by keeping dependencies current before an advisory becomes an outage.
Where these agents work
These agents work through your existing operations tooling with scoped credentials. We are not asking you to change your monitoring stack.
- Observability and monitoring
- Logging and tracing
- Alerting and on-call
- Cloud platforms and orchestrators
- CI/CD
- Incident management
- Chat
- Status pages
Platform names are shown as examples of the categories agents connect to. They are not partnerships or endorsements.
The blast radius, defined in advance
Autonomy boundary
A finite set of approved remediations, each with a blast-radius limit: how many instances, which environments, how many attempts before it stops.
Approval gates
Anything destructive, anything touching data, anything outside the approved set, and anything in a protected environment waits for a person.
What stays human
Novel failures, judgment about customer impact, and the decision to declare a major incident.
Logging
Every observation, inference, action and verification recorded with timestamps, which is both the audit trail and the post-incident timeline.
How the work runs
Typical ranges from our engagement model (doc 04 §5), not a quote.
| Stage | Typical | What happens |
|---|---|---|
| Pilot | 3-10 days audit, then 2-4 weeks | Incident history is analyzed to find the recurring classes, and diagnosis runs in observe-only mode against live incidents. |
| Build | 3-8 weeks | Detection tuning, diagnosis correlation, the approved remediation set, verification and escalation packaging. |
| Release | 1-2 weeks | Diagnosis-only first, then remediation in non-production, then production class by class. |
| Managed | ongoing, optional | New incident classes added to the approved set, recurrence reported, thresholds tuned. |
What we measure
- Observe first
- Every remediation runs in diagnosis-only mode before it is allowed to act (Target)
- Bounded
- Every action carries a blast-radius limit and an attempt ceiling (Target)
- 100%
- Of agent actions logged, timestamped and reversible (Target)
- Your baseline
- Out-of-hours pages and time-to-recovery, measured before and after (Yours)
“ Until then these are design targets and engineering commitments, not results.
What this looks like in practice
Self-healing hive
Reference scenario · SaaS and infrastructure. Nightly incident volume absorbed before the on-call phone rings.
Reference scenario - a composite build illustrating our method. Figures are modeled and the model is shown.
Frequently asked questions
Are you really letting an AI change production at 3 a.m.?
Within a finite set of remediations your team approved in advance, yes, each with a blast-radius limit and an attempt ceiling. It is not reasoning its way to novel changes under pressure; it is executing your runbook faster and stopping the moment it leaves familiar ground. Anything destructive or unfamiliar pages a human instead.
How is this different from the AIOps tools we already evaluated?
Most of that category detects and notifies. The difference here is remediation and verification: the agent acts within an approved set and then confirms the symptom actually cleared. Detection alone converts finding problems into reading about them, which rarely reduces the number of times somebody is woken up.
What if it makes an incident worse?
Every action is bounded and reversible, verification runs against the original symptom, and repeated failure stops the agent rather than escalating its attempts. The full timeline is logged, so a bad decision is diagnosable and becomes a rule change. We also start in diagnosis-only mode precisely so you can see what it would have done before it does anything.
Does this replace our on-call rotation?
No, and we would be cautious about anyone claiming otherwise. It absorbs the recurring, runbook-shaped incidents so the rotation is woken for genuinely novel problems, with context already gathered. The rotation still exists; it is paged less and better informed when it is.
How does it know what "recovered" means?
It is defined per remediation against the original symptom rather than the command's exit status. A restart that returns zero while the service still fails its health check is not a recovery, and treating it as one is how automated remediation earns a bad reputation.
Do we need clean runbooks before starting?
No. The pilot works from your incident history to find the recurring classes, and the approved remediation set is built from what your team already does. For many teams this is the first time those responses have been written down, which is a side benefit worth having on its own.
Related services
AI Integration & Implementation
Connecting agents to your operations tooling.
Agent Orchestration
Coordinating several agents during an incident.
Software Development Agents
Preventing the incidents that start as unpatched dependencies.