Operations that think, decide, and execute with governance.
A multi-agent AI engine that turns environmental signals into decisions, responses, and coordinated actions, with more speed, context, and traceability.
6 agents specialized across each stage
24×7 detection, decision, and execution
100% action traceability
Product capabilities
Six capabilities that turn signals into controlled action.
Signal correlation
Connects logs, alerts, and events from different sources into a single view, cutting through noise to surface what matters.
Impact-based prioritization
Ranks criticality based on business context (assets, dependencies, and vulnerabilities), not just technical severity.
Configurable decision-making
Suggests, routes for human approval, or executes automatically, based on the rule defined for each case and service.
Assisted or autonomous execution
Acts at the defined level of autonomy, always within the organization's controls and policies.
Continuous learning
Logs context, decisions, and outcomes to improve response quality with every operational cycle.
Ecosystem integration
Fits into the tools and environments you already have, extending the value of your existing stack without replacing it.
Use cases
From security threats to infrastructure degradation.
Autonomous AI operates in any scenario where signals need to become coordinated actions, with end-to-end governance and traceability.
Threats contained at the source, fully logged
Suspicious lateral movement detected by the Watcher, correlated by the Analyst, containment plan reviewed and executed — all tracked.
- Threat detection and correlation
- Containment plan validated against policies
- Autonomous or approval-based isolation
Less alert fatigue, more focus on what's critical
A massive volume of alerts collapsed into a handful of prioritized incidents. Analysts get only what truly needs attention.
- Correlation that cuts out the noise
- Queue prioritized by real impact
- Full context within the incident
Degradation handled before it causes downtime
Gradual drift detected early by the Watcher. The Analyst proposes a preventive action. The Executor acts, the Evaluator verifies and logs it.
- Early detection of degradation
- Preventive action before downtime
- Full cycle logged for audit
Where it operates
Where Autonomous AI fits in, and what the organization gains.
From the SOC to infrastructure and corporate areas, signals become coordinated responses, with end-to-end governance.
Cybersecurity
Threat detection, prioritization, and response with more context and speed.
NOC, SOC, and sustainment
Reduced alert overload and greater operational productivity.
Networks and integrated environments
Coordinated response across distributed operations with multiple sources.
Infrastructure and IT operations
Anticipating outages and accelerating incident response.
Critical corporate areas
Applied across legal, contracts, HR, compliance, and internal operations.
Gains for the organization
Results the operation feels every day.
Faster response times
Reduces the time between identification, analysis, and action.
More clarity to prioritize
Helps leadership and teams see what's critical.
Less effort wasted on noise
Cuts down irrelevant alerts and low-value repetitive work.
More resilience for the business
Strengthens continuity with faster, more consistent responses.
Policies and risk levels
every agent within the rulesEach agent acts within rules, limits, and autonomy levels defined by the organization, never going beyond the approved scope.
Human approval in the loop
sensitive decisions validatedSensitive decisions can require human validation before execution, keeping control where the risk demands it.
Decision traceability
full audit trailThe context, criteria, and outcome of every action are logged for audit, compliance, and continuous learning.
Multi-LLM, no lock-in
freedom of model choiceAgents can use different AI models depending on the task, the organization's policy, and its technology strategy.
Services
From project to sustained operation.
Central IT supports every stage: deploying, connecting to what you already have, defining governance, training teams, and keeping the engine evolving.
01
Deployment
Engine setup, connection to signal sources, and configuration of the first agents and operating policies.
02
Integration
Connection to your existing ecosystem, from monitoring and SIEM to ITSM and APIs, extending the value of your current tools.
03
Governance and policies
Designing autonomy levels, human approval rules, and risk limits, so AI evolves with control.
04
Training
Training teams to operate, tune, and trust the engine, moving from assisted to autonomous operation at the right pace.
05
Support and continuous evolution
Ongoing support, fine-tuning of agents, and continuous improvement of responses as the environment changes.
Autonomy is already the new standard
AI that acts is different from AI that only responds.
Organizations that adopt autonomous agents respond faster, with smaller teams and more consistency, regardless of time of day or event volume.
85%
of CISOs plan to expand AI use in security operations
Source · IBM 60%
reduction in incident response time with AI
Source · Gartner 3×
more incidents handled with the same team
Source · Forrester 70%
of companies will use autonomous AI in infrastructure by 2029
Source · Gartner Frequently asked questions
Common questions about Autonomous AI.
Do the agents operate without human supervision?
Only within the limits you define. For each type of action, you configure whether the agent notifies, suggests, waits for approval, or executes. No action goes beyond the scope approved by the organization.
How is Autonomous AI different from AIOps?
AIOps focuses on alert correlation and infrastructure remediation. Autonomous AI is a broader multi-agent engine: it spans cybersecurity, NOC, SOC, infrastructure, and corporate areas, with agents specialized for each stage of the cycle.
Does it work with the security tools we already have?
Yes. Autonomous AI integrates with your existing environment (SIEM, EDR, monitoring, ITSM), receiving events and acting on them without requiring you to replace any tools.
How long does it take for the agents to learn the environment?
The Watcher starts identifying patterns within the first few hours. Confidence in recommendations grows over the following weeks, as the Evaluator logs every cycle to improve future responses.
Next step
Autonomy with purpose. Governance with traceability.
See how CITSmart Autonomous AI's agents operate within your organization's context, with the controls you need.
Talk to a specialist