Enterprise Automation
Intelligent, orchestrated automation that adapts to changing conditions and operates reliably across distributed environments.
Enterprise automation is evolving from brittle, rule-based workflows to intelligent, orchestrated systems that reason about context, adapt to changing conditions, and operate reliably across distributed environments.
QORIS enables automation driven by reasoning agents rather than static rules. Agents evaluate context, plan sequences of actions, and execute with awareness of system state, dependencies, and outcomes—enabling automation that operates reliably at scale without constant human oversight.
The Problem
Rule-Based Automation Breaks
Rule-based automation breaks under changing conditions because it cannot reason about context or adapt to new situations. These exceptions require manual intervention, workflow modifications, or extensive exception handling logic that becomes unmaintainable.
Static Workflows Fail
Static workflows fail in long-running processes because they cannot maintain state or handle interruptions. These workflows lose state, duplicate work, or fail silently, requiring manual investigation and recovery.
Human Babysitting Required
Humans end up babysitting automation systems because failures are frequent, exceptions are common, and recovery requires judgment. This defeats the purpose of automation—creating a new category of manual work: automation maintenance.
The QORIS Approach
Reasoning Agents, Not Static Rules
Automation in QORIS is driven by reasoning agents, not static rules. When an agent receives a task, it first evaluates the current context—what data is available, what systems are accessible, what has already been completed, and what conditions exist.
This reasoning layer is what makes automation resilient—agents can handle situations that were not explicitly programmed.
Orchestration with Persistent Context
Orchestration across systems with persistent context enables automation to operate reliably in distributed environments. The orchestration layer maintains state across systems, tracking what has been completed, what is in progress, and what remains.
This orchestration is what transforms individual automation steps into cohesive, reliable processes.
Adaptive Learning Over Time
QORIS enables automation that adapts over time through memory and learning. When an agent executes a process, it remembers the outcome—what worked, what failed, and what required manual intervention.
This adaptive capability is what makes automation sustainable at scale—systems improve over time rather than requiring constant maintenance.
What This Enables
Resilient automation flows
Automation that handles exceptions, adapts to changing conditions, and recovers from failures without manual intervention. Agents reason about context, evaluate alternatives, and route around problems rather than failing when conditions change.
Reduced manual intervention
Automation that operates reliably without constant monitoring or babysitting. Agents handle exceptions, maintain state, and recover from failures autonomously, reducing the maintenance overhead that makes traditional automation expensive.
Context-aware execution
Automation that evaluates current context before executing, considering system state, dependencies, and historical outcomes. Agents adapt their execution based on what they know, avoiding failures that static workflows cannot prevent.
Human-in-the-loop safeguards
Automation that escalates to humans when judgment is required, providing full context about what was attempted and why. Agents can pause execution, request approval, and resume with updated context, ensuring human oversight where needed.
Long-running, stateful processes
Automation that maintains state across days or weeks, handling interruptions, system restarts, and changing conditions. Processes can pause, resume, and adapt without losing context or duplicating work.
Cross-system coordination
Automation that coordinates actions across multiple systems, handling dependencies, retries, and consistency. The orchestration layer ensures that multi-system processes complete reliably even when individual systems are temporarily unavailable.
Adaptive improvement over time
Automation that learns from outcomes and improves without manual updates. Agents remember what works and what fails, adapting their execution to avoid known failure modes and optimize successful approaches.
Policy-driven execution limits
Automation that operates within defined boundaries, enforcing policies for data access, action limits, and approval requirements. This ensures automation remains compliant and controllable even as it scales.
How This Is Built on QORIS
Thinking Agent OS
The Thinking Agent OS provides the orchestration layer that enables intelligent automation. The OS manages agent lifecycle, coordinates multi-step processes, and provides the reasoning layer that enables agents to plan, adapt, and execute reliably.
Orchestration Engine
The orchestration engine manages execution across distributed systems, handling state, dependencies, and recovery. This orchestration is what makes automation reliable in distributed environments where individual systems may fail.
Persistent Memory
Persistent memory enables automation to maintain state across long-running processes and learn from outcomes. This memory layer is what makes automation sustainable—processes can operate reliably over long timeframes.
Policy & Governance
Policy and governance controls ensure automation operates within defined boundaries and remains compliant and controllable. This control is what makes automation acceptable in enterprise environments where compliance, security, and oversight are required.
Build Intelligent Automation
Deploy automation that adapts, learns, and operates reliably at scale.
Start Building Today
Get started with Enterprise Automation and build intelligent systems that adapt and scale.
No credit card required • Start building in minutes