Agentic OS

AI Agents That
Execute, Not Assist

Brezel introduces AI agents as active system users — capable of planning, executing, and iterating on real tasks across tools, data, and workflows. Not chatbots. System users.

24/7 AvailabilityParallel ExecutionContext-AwareAutonomous Workflows
Not Chatbots

Active System Users,
Not Passive Assistants

Brezel's AI agents are not chatbots. They are designated system users with strict roles, rights, and fully autonomous workflows.

Chatbots
Brezel AI Agents
01 Role in System

Responds to prompts on request.

Operates as a designated system user with strict permissions.

02 Memory

Session-based only — forgets after every conversation.

Persistent context via VectorDB and RAG pipelines.

03 Tool Access

No system-level access or permissions.

APIs, headless browsers, CLI in secure sandboxes.

04 Execution

Assists users — humans still do the work.

Executes full workflows autonomously end-to-end.

Agent Architecture

Built for Agentic Execution

Every layer of a Brezel agent is designed for autonomous operation — from identity to memory to real-time tool access.

🧠
02Memory

Context-Aware Persistent Memory

Agent knowledge is stored in VectorDB, retrieved through RAG pipelines and kept relevant inside context windows — reloaded fresh for every task.

VectorDB storageRAG pipelinesPer-task context reload
03Availability

24/7, Event-Driven & Parallel

Agents are always active, triggered by events, and can run infinitely in parallel across every workflow simultaneously.

24/7Always on
🛠
04Tool Access

Secure Sandboxed Tool Access

Agents have access to a full suite of tools inside isolated, secure sandboxes — operating autonomously without human intervention.

REST APIsHeadless BrowserCLIGitDocsWeb
Execution Loop

Plan. Execute. Observe. Repeat.

Agents operate in continuous cycles — planning, executing, and refining until goals are achieved. Errors don't crash the system; they trigger re-evaluation.

Step 01
Task & Context
Receive input and load relevant knowledge.
RAG pipelines load context from VectorDB
Step 02
Planning
Analyze goals and reason next steps.
Goal reached? Output & log. If not, refine.
Step 03
Tool Use
Execute actions via APIs or environments.
Secure sandbox execution across tools
Step 04
Observe
Catch results or errors and iterate.
Results feed back into next planning cycle

Self-Healing Execution

Errors during tool execution do not cause system crashes. Instead, they automatically trigger the Observe phase — agents re-evaluate, adapt, and continue.

Agentic OS Model

One Interface.
The System Handles the Rest.

The user interacts through a simple ticket interface. Brezel handles everything else — routing logic, orchestrating agents, and executing workflows across all systems.

Human Layer
UserSets intent only
Ticket SystemStructured input
Orchestration

Orchestrator

Creates automatic data routes for configured agents. Plans, decomposes, and coordinates all workflows.

Goal decompositionIntent routingWorkflow planningError recovery
Digital Workforce
Git Agent
Web Agent
Docs Agent
Data Agent
Email Agent
Search Agent
Secure native access · Enterprise repository

Agents are not chatbots — they are active system users with designated roles, strict rights, and fully independent automated workflows.