ina IMS
An autonomous site reliability system that detects, diagnoses and remediates production incidents on its own, waking an AI model only once a fault is confirmed and asking a human before touching anything risky.
Platform Architecture Overview
ina IMS runs as a single fenced pipeline. Cheap deterministic code watches everything continuously, and the AI is woken only once to reason through a confirmed incident. Humans approve anything risky, and the AI can never grant itself new permissions.
Detection Engine
Incident Manager
Diagnosis Agent
Action & Approval Layer
Verification & Post-mortemDetection Layer
Diagnosis Layer
Action & Approval Layer
Integration Layer
End-to-End Platform Architecture
From the first signal to a verified fix and a closed ticket
Ingest
Logs, metrics, traces, Kubernetes events and CI/CD signals feed the engine continuously.
Detect
Pure thresholds decide whether something is actually wrong. No LLM is involved at this stage.
Incident Manager
Correlated symptoms collapse into a single incident. A ticket is opened and the team is notified, but only once a fault is confirmed.
Diagnose
The LLM is woken exactly once to work out the root cause. Two runs have to agree, every cited log reference has to resolve to something real, and the proposed action has to exist in the catalog.
Act, with a human in the loop
Safe fixes run automatically. Anything touching a stateful service or exceeding a retry cap waits for a person to approve it.
Verify and record
Recovery is confirmed against the specific fault, the incident closes cleanly, and the whole episode becomes part of the system's memory.
Core Technical Capabilities
Key capabilities powering autonomous incident management
No LLM in the Detection Loop
Pure thresholds, debounce and cooldown decide when something is genuinely wrong, keeping detection fast and free of hallucination.
Tiers Are Properties of Actions
The model only selects an action id. Code, not the model, decides whether that action runs automatically, needs approval, or gets escalated.
One Fault Equals One Ticket
Symptoms correlated across many services collapse into a single incident before any ticket ever gets opened.
Never Touches Monitored Data
No destructive database action is permitted at any tier, under any circumstances.
Never Diagnoses Its Own Fix
Every remediation is change logged, and detection logic is built to ignore the agent's own actions.
Silence on Healthy Systems
A full hour of a healthy system produces zero incidents, zero tickets and zero pages. This is tested automatically on every release.
Extended Capabilities
Advanced incident management features
Service Error Rate Recovery
Latency Regression Handling
Silent Background Failure Detection
Dependency Cascade Correlation
Stateful Component Approval Workflow
Deployment Rollback and Code Fix Pull Requests
Who Integrates With the Platform
Four primary integration personas
Engineering and SRE Teams
Cuts the manual triage that usually eats up MTTR, so engineers stop squinting at dashboards and greping logs at 2 a.m.
Platform and DevOps Teams
Connects to Kubernetes, CI/CD, ticketing and chat tools through swappable adapters, so onboarding a new system means configuration, not a rebuild.
Engineering Leadership
Gets a system where every action is change logged, tier gated and auditable, instead of a black box making decisions on its own.
IT and Security Teams
Runs under a tightly scoped identity, so the blast radius of any action is an auditable grant, not a matter of trusting the agent.
Platform Facts
Key characteristics of the ina IMS platform
500+ Automated Tests
A large regression suite validates the engine, including a null test that confirms zero output on a healthy system.
One Ticket Per Fault
The correlation guarantee, regardless of how many services light up from the same root cause.
Under 2 Minutes
Typical detect, fix and verify cycle for an autonomous incident, end to end.
Zero Tolerated False Positives
Treated as the highest severity bug in the system, since a tool that cries wolf gets turned off within a week.
Environment Agnostic
The same engine runs in cloud, on-prem or hybrid environments, with everything environment-specific handled by swappable adapters.
FAQ
How does ina IMS stop an autonomous agent from taking a destructive action against payment infrastructure?
What makes ina IMS different from pointing an LLM at logs or relying on static alerting rules?
How is a remediation decision made auditable enough to satisfy a payments compliance or risk team?
Can ina IMS plug into an existing observability, orchestration and CI/CD stack without a re-architecture?
What guarantee exists against false positives and alert fatigue in production?
Partner With Us
Customer Trust Built on Global Reach and Consistent Reliability
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