How Qoris Works Under the Hood
Qoris is the trust and governance layer that sits between your AI agents and the systems they act on. It works with any LLM, any agent framework, and any stack — connect via MCP in minutes. Here's exactly how it's built.
Connect Via MCP
Qoris connects to any agent framework via the Model Context Protocol (MCP) — the open standard for agent-to-infrastructure communication. The moment your agents connect to Qoris via MCP, Knox begins governing every action they take. No migration required. No framework lock-in. Your existing agents, now trusted.
Works With Any Framework
Connect OpenClaw, LangGraph, CrewAI, AutoGen, LangChain, or any custom agent framework via MCP. Qoris doesn't care what your agents are built on — it governs what they do.
What Happens When You Connect
Once connected via MCP, every agent action passes through Knox before execution. Memory starts persisting immediately. Orchestration coordinates across agents. The audit trail begins from the first action.
Native Integration Coming
Native SDK and CLI integration is on the roadmap for deeper platform embedding. MCP connection is available today — start governing your agents immediately while we build the deeper integration.
The Platform Stack
Interfaces
Apps, users, and agents that interact with the platform.
Reasoning Layer
Thinking Agent OS interprets goals and forms plans.
Control Plane
Memory & Context Engine plus Governance & Policies manage state and constraints.
Execution Layer
Agent Orchestration coordinates actions across tools and workflows.
Systems of Action
Enterprise tools, APIs, and external systems that execute work.
Architecture diagram placeholder
Core Primitives
Thinking Agent OS (Reasoning)
What it does: Interprets goals and context to form plans and decisions. Provides the reasoning abstraction that enables agents to evaluate options and construct execution strategies.
Why it exists: Reasoning must be separated from execution to enable model independence and long-term planning. This layer allows agents to think before acting, adapt to context, and operate intelligently rather than executing predefined scripts.
Agent Orchestration (Execution)
What it does: Coordinates actions across tools and workflows, managing dependencies, retries, and state transitions. Ensures reliable execution even when individual components fail.
Why it exists: Multi-step processes require coordination that static workflows cannot provide. Orchestration enables agents to work together, handle failures gracefully, and maintain state across long-running processes.
Memory & Context Engine (System of Record)
What it does: Persists context, decisions, and knowledge centrally, independent of models or tools. Provides the system of record that enables continuity across sessions and time.
Why it exists: AI systems without persistent memory cannot maintain consistency or learn over time. Centralized memory enables agents to retain context, build institutional knowledge, and operate with continuity that stateless systems cannot achieve.
Knox (Security & Governance)
What it does: Knox sits between your agents and every system they interact with. Before any action executes — API call, data access, record update, communication sent — Knox evaluates it against your defined policies. Approved actions run. Flagged actions pause for human review. Every action logged.
Why it exists: Autonomous agents operating without a governance layer will eventually do something wrong — and you won't know until after the consequences. Knox moves governance to the execution layer, intercepting before consequences occur rather than detecting after.
Request Lifecycle
A goal or task arrives at the platform through an interface—user request, API call, or agent trigger.
The Thinking Agent OS reasons over the goal, queries memory for relevant context, and constructs a plan.
Memory is read to retrieve relevant context, and new decisions or knowledge are written to the control plane.
Governance policies are evaluated to check permissions, constraints, and approval requirements before execution.
Orchestration coordinates execution across tools, managing dependencies, retries, and state transitions.
Results are recorded in memory, and all activity is logged for auditability and observability.
The system can resume later with full continuity, using persisted memory to maintain context across sessions and time.
Why a Control Plane
Model-centric systems are stateless and inconsistent. When memory and governance are embedded in individual models, each interaction resets context, decisions cannot persist across sessions, and behavior becomes unpredictable. Models cannot maintain consistency when they forget previous interactions, cannot learn when they cannot retain outcomes, and cannot operate reliably when they lack a system of record. This statelessness forces organizations to repeatedly provide context, repeatedly explain policies, and repeatedly resolve the same issues, preventing AI from becoming operational infrastructure.
Tool-centric automation is brittle. When orchestration and governance are implemented per-tool or per-application, systems become fragmented, dependencies become hard-coded, and failures cascade unpredictably. Each tool maintains its own state, its own logic, and its own boundaries, creating inconsistencies when tools change, gaps when tools fail, and complexity when tools multiply. This fragmentation prevents organizations from building cohesive AI systems, forcing them to manage disconnected tools that cannot coordinate, cannot share context, and cannot operate as a unified platform.
Centralizing memory and governance enables reliability and scale. When memory lives in a control plane, it persists independently of models and tools, enabling consistency across sessions, learning over time, and continuity across changes. When governance lives in a control plane, it enforces policies uniformly across all agents and operations, enabling compliance, auditability, and trust at scale. This centralization is what enables QORIS to function as long-running infrastructure rather than ephemeral applications, providing the reliability and consistency that organizations require for operational AI systems.
Security + Trust Boundaries
Least privilege is enforced at the control plane level, ensuring that agents and users only access the memory, tools, and operations they are authorized to use. Access controls are evaluated before memory reads, before tool execution, and before policy changes, preventing unauthorized access even when individual models or tools lack sufficient security boundaries. This enforcement happens independently of execution-layer components, ensuring consistent security regardless of which models or tools agents use.
Policy gates and approvals operate at the control plane level, providing checkpoints where human oversight or additional verification is required. When an agent attempts an action that exceeds its authority or requires approval, the control plane enforces the policy gate, blocking execution until the appropriate approval is obtained. These gates are defined in the governance layer and enforced uniformly across all agents, ensuring that policy violations cannot bypass controls regardless of which models or tools are used.
Auditability and observability are built into the control plane, providing visibility into all agent activity, memory access, and policy enforcement. Every action, decision, and access is logged with context about who performed it, when it occurred, and what policies were evaluated. This logging happens independently of execution-layer components, ensuring that audit trails persist even when models or tools change. The control plane provides a unified view of system activity that enables organizations to understand what happened, why it happened, and how to improve operations over time.