Nimbus

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Autonomous Execution Revolution

Intelligence that acts

Transform validated insights into coordinated action with a hierarchical framework of intelligent agents that execute with precision and learn from every interaction.

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Hierarchical Agent Framework

From simple tasks to complex workflows, agents work together to achieve strategic goals

Hierarchical Agent Framework

Design patterns for intelligent agents

From simple tasks to complex workflows, agents work together to achieve strategic goals

Pattern Categories
REASONING Patterns
Pattern Detail: ReAct
Medium
Use Case

Core logic loop for Conversational Agents breaking down complex queries

Implementation Example
Think: "I need sales data" → Act: Query database → Think: "Now alert manager"
Agent Tier Mapping
Tier 1: Perception
Graph RAG, Prompt Chaining
Tier 2: Task
Tool Use, RAG
Tier 3: Workflow
Orchestrator, Planning, Routing
Tier 4: Conversational
ReAct, Multi-Agent System
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Policy-Governed Execution

Every action is pre-checked against enterprise policies for compliance and safety

Governed Autonomy

Freedom within structured guardrails

Teams act autonomously while policy-driven systems ensure compliance and safety

Autonomy Metrics
247
Auto Approvals
23
Human Review
98.5%
Compliance Rate
1.2h
Avg Approval Time
Governance Rules
financial Rule
active

Budget Approval

Threshold
$10,000+
Approvers
• CFO
• VP Marketing
Recent Actions3 actions
quality Rule
active

Data Quality Check

Threshold
<85% confidence
Approvers
• Data Team Lead
Recent Actions7 actions
compliance Rule
active

Brand Guidelines

Threshold
All content
Approvers
• Brand Manager
Recent Actions12 actions
Recent Decisions
Campaign budget approved2m agoBudget Approval
Content review required5m agoBrand Guidelines
Data quality passed8m agoData Quality Check
91%
Auto Approval Rate
0
Policy Violations
15s
Avg Decision Time
03 / 04

Multi-Layered Memory

Agents learn and remember from every interaction, improving performance over time

Multi-Layered Memory

Agents learn and remember from every interaction

Three-layer memory architecture enabling continuous learning and performance improvement

Memory Layers
SHORTTERM Detail
Technology Stack
High-Speed Cache

Sub-millisecond access to active context - agents stay focused on the task at hand without losing thread.

Performance Metrics
<1ms
Latency
100MB
Capacity
1-24 hours
Retention
Capabilities
Conversation context retention
Intermediate step storage
Real-time variable management
Session-based data persistence
Data Types
User utterances
Intermediate calculations
Temporary variables
Session context
Memory Architecture Overview
Short Term
Immediate Context
Task-specific variables and conversation state
Episodic
Experience Learning
Past actions, outcomes, and performance patterns
Semantic
Knowledge Graph
Business context, relationships, and domain expertise
3
Memory Layers
1TB+
Total Capacity
<1ms
Min Latency
94%
Recall Accuracy
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Enterprise Integration

Seamless connection with existing tools: Jira, Salesforce, Slack, and more

Jira
Jira
connected
Salesforce
Salesforce
connected
Slack
Slack
connected
HubSpot
HubSpot
connected
Snowflake
Snowflake
connected
GitHub
GitHub
pending
Jira

Jira

Enterprise Integration

connected

Automated ticket creation, assignment, and status updates

Agent Capabilities

Auto-create tickets from signals
Smart assignment to team members
Status synchronization
Custom field population

API Endpoints

POST /rest/api/3/issue
PUT /rest/api/3/issue/{issueId}
GET /rest/api/3/project

Connection Status

✓
Status
2 minutes ago
Last Sync
Test Connection
✓ Connection established and verified
Usage Example
Signal: "New product opportunity detected"
→ Creates Jira ticket with market context

Intelligent Architecture

Four Tiers of Intelligent Execution

A composable framework where different classes of agents work in concert, each optimized for specific roles and responsibilities.

Agent Hierarchy Framework

Four specialized tiers working in concert, each optimized for specific roles from perception through execution to human interaction.

Execution Flow
Tier 1
The Senses
Perception Agents
Tier 2
The Reflexes
Task Agents
Tier 3
The Muscle Memory
Workflow Agents
Tier 4
The Conscious Interface
Conversational Agents

Tier 1: Perception Agents

The Senses

Tier 1

Perform mass, programmatic prompting of external LLMs to simulate market curiosity at scale

Primary Patterns

Graph RAGPrompt ChainingParallelization

Core Capabilities

Client context profiling
Dynamic prompt generation
Multi-LLM orchestration
Scalable query processing

Architecture Overview

Position in Hierarchy
Level: Tier 1 of 4
Role: The Senses
Scope:Market-wide perception
Interaction Patterns
→ Triggers Tier 2 Task Agents
→ Feeds Dual Analysis Core
→ Generates market signals
Example Agent
PerceptionAgent
Queries 1000+ LLM conversations about Ferrari's track programs

Real-World Applications

Agents in Action

See how intelligent agents transform business operations across industries and use cases.

Real-World Applications

See how intelligent agents transform business operations across industries, delivering measurable results and competitive advantages.

Product Opportunity Automation
Automotive
15min
Response Time
Campaign Intelligence Pipeline
Consumer Goods
+31%
Campaign Uplift
Customer Success Automation
Technology
23%
Churn Reduction

Product Opportunity Automation

Automotive

Challenge

Automatically identify and act on new product opportunities from market intelligence

Solution

Workflow agents coordinate market analysis, stakeholder notification, and project initiation

Outcome

✓ Reduced time-to-action by 85%, improved opportunity capture rate

Impact Metrics

15min
Response Time
94%
Accuracy Rate
85%
Manual Work Reduced
Implementation Approach
Agents Used: PerceptionAgent → WorkflowAgent → TaskAgents
Timeline: 15 minutes from signal to action
Integration: Jira, Slack, Salesforce
ROI Analysis
Time Savings: 85% reduction in manual work
Accuracy Gain: 94% vs 67% manual accuracy
Revenue Impact: 3x faster opportunity capture