KaiROS™Deterministic reasoning for network and data centre operations.
The neuro-symbolic platform for Intent-Based Operations
Operators state network intent and policy in natural language. KaiROS turns it into conflict-checked policy and deploys it through your existing policy-based management, orchestration, and automation systems.
LLM agents are easy to use. Workflow systems are safe but hard to scale.
Raw LLM agents hallucinate actions, skip conditions, and misread scope, so they can’t be trusted with live infrastructure. Runbook systems avoid that but become hard to maintain as policies, exceptions, topologies, and SLAs multiply. KaiROS pairs natural-language intent with a symbolic reasoning core that answers the real question: which policy or action follows from this intent, event, or scenario, and why.
Made safe for the AI-agent era
3GPP’s intent-driven management standard (TS 28.312) defines an intent as “a set of expectations including requirements, goals and constraints”. An operator’s obligations are constraints, so they sit inside that definition. KaiROS’s stages map onto the standard’s lifecycle: consultation is investigation, commit is activation, and the runtime is fulfilment with the decision trace as its report.
Before anything is enforced, everything can be asked
From day one KaiROS is a decision support system: it gives every decision its facts, rules, and proof, and leaves the decision with the engineer. In the 3GPP lifecycle this is the Investigation phase, with the network untouched.
Quick question
“What is the best time to do the UPS upgrade?”
KaiROS works out which rules the work touches: contractual notice periods, holiday-adjusted; the change-freeze calendar; other scheduled work that stacks risk. It returns the windows that survive, why the others fail, and tenant notices drafted per tenant.
Guided analysis
“Twelve racks, 40 kW each, liquid-assisted cooling, N+1 power, go-live in two weeks. Where can we place it?”
KaiROS reads current state from the systems that hold it and asks only for what no system holds. Anything unanswered holds the analysis; it never guesses. It narrows the candidates with reasons and shows the working: rules passed, facts used, change request pre-filled. The engineer decides.
Quick answers are grounded in the operator’s rulebook. Computations (deadlines, thresholds) and all guided analysis run on the deterministic reasoning path.
Beyond the network: the complete data centre
A fabric controller deploys and verifies declared network intent. A data centre is also governed by rules the controller never sees: power and cooling state, N+1 redundancy commitments, permits-to-work and lockout rules, change freezes, tenant SLAs, compliance boundaries. They live in documents, ITSM, GRC, and contracts, and none of them is checked automatically. In Uptime Institute’s 2025 Annual Outage Analysis, 85% of human-error outages come down to staff not following procedures, or the procedures being wrong.
KaiROS makes those rules executable. The operator’s documents become machine-checkable rules, verified by the operator’s engineers. Every proposed action, whether from an engineer, a fabric controller, or an AI agent, is checked against those rules and live facts from DCIM, BMS, ITSM, and GRC before it executes. KaiROS sits alongside the fabric controller and never executes changes itself.
Conversational at the edge. Deterministic at the core. Governed at execution.
Neural AI interprets operational language and captures intent. It never chooses actions on live infrastructure: the situation becomes symbolic facts and rules, and the symbolic layer derives the decision.
Neural understanding
Structures operator intent and incident language; explains decisions in plain English
Removes the rule-authoring bottleneck of old expert systems.
Symbolic reasoning
Derives applicable rules, obligations, prohibitions, exceptions, conflicts, and consequences
Makes the decision deterministic, conflict-aware, and traceable.
Governed enforcement
Implements approved policy through PBM, orchestrators, SDN controllers, and automation
Keeps every action bounded, authorized, and auditable.
The same Reasonex engine is in production reasoning over codified civil-procedure law in three jurisdictions as MikeROS™, a domain where a wrong answer carries professional sanctions.
Three operations modes
Reactive AIOps
Facts arrive from a live event. KaiROS derives which remediation rules apply and what response is permitted or required.
Intent-Based Operations
Facts come from a stated goal. KaiROS turns it into candidate policies, checks conflicts and consequences, and deploys through PBM or orchestration.
Proactive AIOps
Reasons about what would follow from a proposed state before it occurs. This mode is the roadmap; reactive and intent-based modes are available today.
From natural-language intent to safe enforcement
Humans define intent once. A reasoned, conflict-checked, committed policy then runs whenever its conditions apply. A human is involved only if the policy requires escalation or the facts are incomplete.
Capture
An operator states intent or a policy in plain operational language.
Clarify
KaiROS asks only what matters: classifier, scope, threshold, exceptions, enforcement target.
Structure
It converts the intent into explicit triggers, conditions, exceptions, permitted/prohibited actions, and fallbacks.
Reason
It derives where the policy applies, what it conflicts with, and what consequences follow.
Commit
The operator or process owner approves the symbolic policy; KaiROS versions it.
Enforce
At runtime the committed policy applies automatically, through your PBM, orchestrator, or automation systems.
Trace
Every decision records the rules applied, facts used, conflicts resolved, and the action taken or why it stopped.
One intent. Overlapping policies. A reasoned outcome.
At runtime: when congestion occurs, the committed policy applies automatically. No human approval unless the policy requires escalation.
Every decision lands on one of four outcomes
Every new policy is reasoned against the rulebase
Before a policy becomes enforceable, KaiROS reasons it against the existing corpus and shows the operator what to resolve.
| New policy says | Existing rule says | KaiROS derives |
|---|---|---|
| Reset customer-facing peer if down | Premium customers require L2 approval | Approval conflict |
| Prioritise Party A during congestion | Emergency services must not be degraded | Priority dominance |
| Auto-failover if primary degraded | Backup-path health is unknown | Missing evidence |
| Suppress alarm during maintenance | Critical customer alarms must still page | Exception conflict |
| Run rollback if upgrade fails | No rollback action defined | Incomplete branch |
No hallucinated infrastructure actions
The neural layer cannot invent executable actions. An action runs only if the symbolic reasoner derives that it is permitted or required under the governed rules and current facts.
Every action is explained: which rules applied, which facts satisfied them, which exception did not apply, and why it acted or stopped.
KaiROS stops or escalates when
- Required evidence is missing
- Telemetry is contradictory
- A higher-priority policy dominates
- Redundancy is unavailable
- No rollback path exists
- A change freeze is active
- Blast radius exceeds the threshold
- The action is outside the approved catalogue
The same alarm, the right decision
BGP neighbor down
It checks neighbor role, redundancy, route impact, customer tier, maintenance window, recent changes, and rollback before any reset. If the safe branch isn’t proven, it escalates with diagnostics.
Firewall high CPU
It separates a transient spike from a traffic surge, a process leak, or an unsafe failover state. Read-only checks run automatically; disruptive actions need HA, rollback, and approval conditions to hold.
Congestion & priority
When paths congest, it reasons over overlapping priority policies (emergency services, premium SLA, Party A), derives which dominates, and applies QoS or reroute only within compliant capacity.
KaiROS doesn’t jump from advice to autonomy
You decide how far up the ladder each policy may go.
Where KaiROS fits among the alternatives
Not a replacement for execution platforms. The reasoning layer that decides what should be enforced.
| Old expert systems | Current runbook automation | LLM agents | KaiROS™ | |
|---|---|---|---|---|
| Natural-language intent capture | Weak | Limited / emerging | Strong | Strong |
| Deterministic reasoning | Strong | Only inside prebuilt workflows | Weak | Strong |
| Conflict & priority reasoning | Weak / custom | Limited | Weak | Core capability |
| Avoids hallucinated actions | Yes, but rigid | Yes for prebuilt jobs | No guarantee | Closed action catalogue |
| Knows when to stop | Only if encoded | Only if encoded | Weak | Core capability |
| Decision trace | Rule trace | Execution log | Often post-hoc | Rule-derived trace |
IBN and PBM systems enforce. KaiROS reasons what should be enforced.
KaiROS hands approved policy, with the decision trace attached, to the systems you already run.
Policy & orchestration
Policy-based management, SDN controllers, slice managers, network orchestrators.
Monitoring & observability
Datadog, Splunk, Grafana, Prometheus, SolarWinds, Dynatrace, New Relic.
ITSM & ITOM
ServiceNow ITSM / ITOM, CMDB, change and incident records.
Automation & APIs
Ansible / AWX, AWS Systems Manager, Azure Automation, runbook tools, REST APIs.
One rule family, codified and run in observation mode
Consultation needs no integration. We codify one rule family (change windows and approvals, or power and redundancy safety) with your engineers, typically in weeks, from your existing runbooks, SOPs, and incident reviews. Your team consults it from day one. Then the same rules run in observation mode on live operations, blocking nothing and showing what they would have approved, held, or escalated, and why. Enforcement is a later setting, granted per rule class.
Questions buyers ask
What category is KaiROS in?
Is KaiROS aligned with the 3GPP intent standard?
How is this different from a policy validator or policy-as-code?
Does a human approve every action at runtime?
Does KaiROS replace my PBM, orchestrator, PagerDuty, or ServiceNow?
How does KaiROS prevent hallucinated actions?
See an intent-to-policy demo on your network
We take one of your real intents, reason it into a conflict-checked policy, and show why each action is permitted, blocked, or escalated.
