KaiROS™Deterministic reasoning for network and data centre operations.
The neuro-symbolic platform for Intent-Based Operations
KaiROS lets operators express network intent, policies, and conditional actions in natural language. It turns them into reasoned, conflict-aware policies and deploys them through your existing policy-based management, orchestration, and automation systems.
Natural language for humans. Symbolic reasoning for decisions. Autonomous enforcement when the rule applies.
LLM agents are easy to use. Workflow systems are safe but hard to scale. Networks need both.
Network operations should be as simple as telling an operations agent what outcome is required. But raw LLM agents can’t be trusted with live infrastructure — they hallucinate actions, skip conditions, and misread scope. Traditional workflow and runbook systems avoid that, but they’re complex to build and hard to maintain as policies, exceptions, topologies, vendors, and SLAs multiply.
KaiROS fills that gap: natural-language operational intent with a symbolic reasoning core. The hard question was never “can this run?” It is “which policy or action follows from this intent, event, or scenario — and why?”
Made safe for the AI-agent era
Intent-Based Networking lets operators define the desired outcome rather than every low-level step. KaiROS extends that principle across operations: operators define intent and policy in human language; KaiROS reasons over the applicable rules; your existing systems enforce the resulting policy.
This is not a private definition. 3GPP’s intent-driven management standard (TS 28.312) defines an intent as “a set of expectations including requirements, goals and constraints” — constraints are inside the standard’s own definition, which is exactly where an operator’s obligations live. The standard’s lifecycle runs from intent investigation — feasibility checks and best-value exploration, with the network untouched — through generation and activation to fulfilment with continuous reporting. KaiROS’s stages map onto that lifecycle: consultation is investigation offered as a service; commit is activation; the runtime is fulfilment with a decision trace as its report.
Operators state the outcome. KaiROS reasons the policy. Existing systems enforce it.
Before anything is enforced, everything can be asked
From day one, KaiROS is a decision support system — and we use the term deliberately. It does not take decisions from engineers; it gives every decision its facts, its rules, and its proof. In the 3GPP intent lifecycle this is the Investigation phase — feasibility checks and exploration of best values, with the network untouched — offered not as a step before the product, but as a standing service of it.
Quick question
“What is the best time to do the UPS upgrade?”
Five words in. The system works out which rules the work touches — including the ones nobody mentioned: who must be notified and how much notice each contract requires, holiday-adjusted; the change-freeze calendar; other scheduled work that stacks risk. Two windows survive, each with the reason the others fail — and tenant notices come back drafted, per tenant, ready to send.
Guided analysis
“Twelve racks, 40 kW each, liquid-assisted cooling, N+1 power, go-live in two weeks — where can we place it?”
The system reads the current state from the systems that hold it, asks only for what no system holds — anything unanswered holds the analysis; it never guesses — narrows the candidates with reasons, and answers with the working: which rules each option passed, which facts were used, change request pre-filled. The engineer decides.
Quick answers are grounded in the operator’s rulebook; computations — deadlines, thresholds — and all of guided analysis run on the deterministic reasoning path.
Why earlier approaches fell short
The old expert-system dream — capture expert operational knowledge and apply it consistently — was correct. It failed on the interface and maintenance model, not the concept. KaiROS brings expert systems back with natural-language policy management, symbolic reasoning, and enforcement through your existing PBM and orchestration.
Runbook platforms scale execution. KaiROS scales operational knowledge.
It derives what follows — it doesn’t just check a shape
KaiROS does not merely check whether a proposed change matches a pre-written constraint. It derives which rules apply to the current event, stated intent, or hypothetical scenario, then determines what follows: what is required, permitted, prohibited, conflicted, incomplete, or safe to enforce.
“Does this proposal match or violate a known shape?”
Schema checks, policy-as-code, graph validators, workflow guards — useful, but a closed question against a pre-written constraint.
“Given these facts, which rules apply, what follows, and what action is permitted, required, blocked, or unresolved?”
Deductive reasoning over a governed rule corpus. Validation and conflict checks are outputs of that reasoning — not the reasoning itself.
Beyond the network: the complete data centre
In the data centre, intent-based tooling already exists for one thing: the network. A fabric controller takes declared network intent, deploys it, and continuously verifies the fabric against it. That part works.
But a data centre is governed by a second set of rules the controller never sees, because they are not part of the network: power and cooling state and N+1 redundancy commitments; permits-to-work and lockout rules; change freezes; tenant SLAs; compliance boundaries. Each states what must always be true — and none of it is checked automatically. It lives in documents, ITSM, GRC, and contracts, enforced by attention. The industry’s own data says how that ends: 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 that second set of rules executable. The operator’s own documents are turned into machine-checkable rules, verified by the operator’s engineers, and every proposed action — from an engineer, a fabric controller, or an AI agent — is evaluated against those rules and live facts from DCIM, BMS, ITSM, and GRC before it executes. It sits alongside the fabric controller, not in place of it, and it never executes changes itself. The layer decides; the stack executes.
Conversational at the edge. Deterministic at the core. Governed at execution.
KaiROS uses neural AI to interpret messy operational language and capture intent — but it never lets a neural model freely choose actions on live infrastructure. Before anything is enforced, 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 burden that made old expert systems brittle.
Symbolic reasoning
Derives applicable rules, obligations, prohibitions, exceptions, conflicts, and consequences
Makes the decision deterministic, conflict-aware, and traceable — not a probable guess.
Governed enforcement
Implements approved policy through PBM, orchestrators, SDN controllers, and automation
Keeps live-infrastructure action bounded, authorized, and auditable.
Reasonex is already in production: the same engine reasons over codified civil-procedure law across three jurisdictions as MikeROS™ — the same deterministic decisions and the same verifiable record, proven in a domain where a wrong answer carries professional sanctions.
Three operations modes
The same symbolic reasoner works from a live event, a stated goal, or a future scenario.
Reactive AIOps
Facts arrive from a live event. KaiROS derives which remediation rules apply and what response is permitted or required, then enforces through your tools.
Intent-Based Operations
Facts come from a stated goal. KaiROS turns operator intent into candidate policies, reasons over conflicts and consequences, and deploys through PBM / orchestration.
Proactive AIOps
Proactive operation — designed to reason about what would follow from a proposed state before it occurs — is the direction of the platform; the reactive and intent-based modes are available today.
From natural-language intent to safe enforcement
KaiROS separates human policy judgment from machine-speed rule application. Humans define intent once, in natural language. Once a policy is reasoned, conflict-checked, and committed, it runs autonomously whenever its conditions apply — a human is involved only if the policy itself requires escalation or the facts are incomplete.
Capture
An operator states intent or a policy in plain operational language.
Clarify
KaiROS asks only the questions that matter — 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, action taken — or why it stopped.
One intent. Overlapping policies. A reasoned outcome.
A validator can’t resolve this cleanly — it needs priority hierarchy, consequence chaining, and fallback derivation.
At runtime: when congestion occurs, the committed policy applies automatically — no human approval unless the policy itself requires escalation.
Every decision lands on one of four outcomes
Every new policy is reasoned against the rulebase
Unsafe automation usually appears when policies drift apart. KaiROS reasons each new policy against the existing corpus before it becomes enforceable — surfacing conflicts, priority dominance, missing evidence, exception clashes, and incomplete branches, then showing 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
KaiROS makes AI-agent interaction safe for live networks. The neural layer interprets language, but it 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.
KaiROS can explain every action as: these rules applied, these facts satisfied them, this exception did not apply, this approved action was rule-supported — and this is why it acted, or stopped.
KaiROS stops or escalates when
- Required evidence is missing
- Telemetry is contradictory
- A higher-priority policy dominates
- Service impact is unknown
- Redundancy is unavailable
- No rollback path exists
- A change freeze is active
- Blast radius exceeds the threshold
- Customer tier requires approval
- The action is outside the approved catalogue
- No compliant fallback can be derived
- The operator’s answer introduces new uncertainty
The same alarm, the right decision
The same technical symptom can require different actions depending on role, redundancy, priority, and blast radius. KaiROS reasons over context before it acts.
BGP neighbor down
KaiROS doesn’t blindly reset the session. It checks neighbor role, redundancy, route impact, customer tier, maintenance window, recent changes, and rollback. If the safe branch isn’t proven, it escalates with diagnostics instead of guessing.
Firewall high CPU
It distinguishes a harmless transient spike from a traffic surge, a process leak, or an unsafe failover condition. Read-only checks run automatically; disruptive actions are blocked unless HA, rollback, and approval conditions hold.
Congestion & priority
When paths congest, KaiROS 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
Each policy earns autonomy step by step. You decide how far up the ladder each one is allowed to go.
Deployments begin at consultation and observation; the upper rungs are the roadmap, and each policy climbs only as far as the operator grants.
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 |
| Derives which rules apply | Only if encoded | Workflow triggers only | Unreliable deductively | Core capability |
| Conflict & priority reasoning | Weak / custom | Limited | Weak | Core capability |
| Reasons over future scenarios | No | No | Pattern-based only | Proactive mode (design target) |
| Avoids hallucinated actions | Yes, but rigid | Yes for prebuilt jobs | No guarantee | Closed action catalogue |
| Scales operational knowledge | Poor | Partial | Poor | Core promise |
| 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 |
Old expert systems were deterministic but hard to maintain. LLM agents are easy to talk to but unsafe to trust with live infrastructure. Current automation scales execution, but not operational knowledge. KaiROS combines natural-language interaction with a symbolic reasoning core, so operators state intent in ordinary language, KaiROS reasons the policy, and your existing systems enforce it. It runs on the Reasonex engine.
IBN and PBM systems enforce. KaiROS reasons what should be enforced.
KaiROS doesn’t need to become your enforcement platform. It supplies the reasoning and hands approved policy to the systems you already run — with the decision trace attached. The layer decides; the stack executes.
PBM, orchestrators & SDN
Translate and enforce intent and policy across the network.
KaiROS: Reasons what should be enforced — which policy is rule-supported, conflict-free, and safe — then hands it over.
PagerDuty
Makes runbook execution scalable — incident management, AI agents, and approved automation jobs.
KaiROS: Makes operational knowledge scalable — reasons whether a job is justified before it runs.
ServiceNow ITOM
System of record for incidents, change, CMDB, and service mapping.
KaiROS: Supplies the deterministic trace explaining why a remediation is valid, unsafe, or ready for approval.
Observability stack
Datadog, Splunk, Grafana, Prometheus, SolarWinds tell you what happened.
KaiROS: Decides what response is safe — then enforces or escalates with the evidence.
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
Start by asking it questions. Consultation needs no integration at all: we codify one rule family that matters to your operation — change windows and approvals, or power and redundancy safety — with your engineers, typically in weeks, and your team consults it from day one: quick questions answered against the rulebook, changes checked before they run. From there, run the same rules in observation mode on live operations — deciding on every relevant action, blocking nothing, showing what it would have approved, held, or escalated, and why. Your existing runbooks, SOPs, and past incident reviews are the source material. Enforcement is a later setting, granted per rule class — not a precondition.
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?
Is KaiROS just a modern expert system?
Does KaiROS replace my PBM, orchestrator, PagerDuty, or ServiceNow?
How does KaiROS prevent hallucinated actions?
Who decides what is safe?
See an intent-to-policy demo on your network
We’ll take one of your real intents, reason it into a conflict-checked policy, and show exactly why each action is permitted, blocked, or escalated.