AI Agents in Tensor One provide a powerful no-code interface to create, deploy, and manage autonomous AI agents. Whether you need an academic research assistant, a web-browsing agent, or a custom solution for your specific use case, our Agent Builder makes it simple to bring your AI vision to life.
Quick Start with Pre-built Templates
Tensor One offers an extensive library of pre-built agent templates designed for specific use cases. These templates provide instant deployment while still allowing full customization of behavior, voice, and capabilities.Featured Templates
AI Scholar Agent
The AI Scholar is an academic-focused agent that assists with research, papers, and scientific writing across disciplines. Perfect for students, researchers, and academics who need intelligent research assistance. Key Features:- Research paper analysis and summarization
- Citation management and formatting
- Cross-disciplinary knowledge base
- Scientific writing assistance
- Literature review support
Internet Agent
A dynamic agent that navigates the web in real-time to retrieve up-to-date information, monitor trends, and extract relevant content from across the internet. Key Features:- Real-time web browsing and data extraction
- Trend monitoring and analysis
- Content aggregation from multiple sources
- Current events and news tracking
- Market research and competitive intelligence
Complete Template Library
Research & Analysis:- AI Scholar Agent - Academic research and writing
- Market Research Agent - Competitive intelligence and analysis
- Data Analyst Agent - Statistical analysis and insights
- Literature Review Agent - Academic paper discovery and summarization
- Patent Research Agent - IP and patent landscape analysis
- Executive Assistant Agent - Calendar, email, and task management
- Sales Prospect Agent - Lead generation and qualification
- Customer Support Agent - Multi-language support and troubleshooting
- Content Creator Agent - Blog posts, social media, and marketing copy
- Project Manager Agent - Task coordination and team communication
- Code Review Agent - Automated code analysis and suggestions
- DevOps Assistant Agent - Infrastructure monitoring and deployment
- Technical Writer Agent - API documentation and technical guides
- Bug Tracker Agent - Issue identification and resolution tracking
- System Monitor Agent - Performance analysis and alerting
- Portfolio Manager Agent - Investment tracking and analysis
- Risk Assessment Agent - Financial risk modeling and reporting
- Crypto Trading Agent - Market analysis and trading signals
- Financial Advisor Agent - Personal finance recommendations
- Compliance Monitor Agent - Regulatory compliance tracking
- Content Curator Agent - Social media management and scheduling
- Brand Monitor Agent - Social listening and reputation management
- Video Editor Agent - Automated editing and post-production
- Graphic Design Agent - Design asset creation and optimization
- Music Composer Agent - AI-generated compositions and arrangements
- Medical Assistant Agent - Symptom analysis and health tracking
- Fitness Coach Agent - Workout planning and progress monitoring
- Mental Health Support Agent - Wellness check-ins and resources
- Nutrition Advisor Agent - Meal planning and dietary recommendations
- Clinical Research Agent - Medical literature analysis and trials
- Tutor Agent - Personalized learning and assessment
- Language Learning Agent - Conversation practice and grammar
- Skills Assessment Agent - Competency evaluation and development
- Course Creator Agent - Educational content development
- Student Advisor Agent - Academic guidance and career counseling
Agent Architecture Overview
Tensor One agents operate on a sophisticated multi-layered architecture that ensures optimal performance, scalability, and resource management:Agent State Management
Each agent maintains a sophisticated state management system that tracks conversation context, user preferences, and execution history:Getting Started
Begin your agent creation journey by selecting “Create New Agent” from the AI Agents dashboard. This will launch the Agent Builder interface where you can customize every aspect of your AI agent.Agent Configuration Steps
1. Select MCP Server
MCP (Model Context Protocol) servers provide your agent with specific data sources and functionality. Choose the servers that align with your agent’s intended purpose. Available MCP Servers:-
News Aggregator MCP (10 cu)
- Real-time news aggregation from global sources
- Categorized news feeds and trending topics analysis
- Sentiment scoring for news content
-
Social Media Insights MCP (15 cu)
- Monitor social media trends across platforms
- Brand mentions and sentiment analysis
- Influencer identification and viral content detection
-
Financial Data MCP (15 cu)
- Real-time financial markets data
- Cryptocurrency prices and economic indicators
- Portfolio tracking and risk assessment tools
-
Browser MCP (12 cu)
- Automated browser interaction capabilities
- Web scraping and DOM element extraction
- Form filling and navigation scripting
-
Data Analytics MCP (20 cu)
- Advanced analytics engine for large-scale data processing
- Statistical modeling and machine learning pipelines
- Structured and unstructured dataset analysis
2. Select Framework
Your framework determines how your AI agent is built and what it’s optimized for. Each framework has specific strengths and ideal use cases.Advanced Framework Comparison
Framework Selection Matrix:| Framework | Setup Time | Scalability | Tool Support | Learning Curve | Best Use Case |
|---|---|---|---|---|---|
| LangChain | Medium | High | Excellent | Moderate | Tool-heavy workflows |
| CrewAI | Low | Very High | Good | Low | Multi-agent collaboration |
| Microsoft Autogen | High | Very High | Excellent | High | Research & complex systems |
| Haystack | Low | Medium | Specialized | Low | Document retrieval & RAG |
3. Select Model
Choose the language model that will power your agent’s reasoning and text generation capabilities.Model Performance Benchmarks
Model Selection Guide:| Provider | Model | Strengths | Ideal For | Performance Tier |
|---|---|---|---|---|
| OpenAI | GPT-4o | Reasoning, coding | Complex analysis | Premium |
| OpenAI | GPT-3.5 Legacy | Speed, cost | High-volume tasks | Standard |
| Anthropic | Claude 3.7 Sonnet | Analysis, safety | Enterprise use | Premium |
| Anthropic | Claude 3.5 Haiku | Speed, efficiency | Real-time apps | Standard |
| Gemini 2.5 Pro | Multimodal, reasoning | Content creation | Premium | |
| Gemini 2.0 Flash | Speed, cost | Quick responses | Standard | |
| Moonshot | Kimi K2 | Context length | Long documents | Specialized |
| Moonshot | Kimi K1.5 | Balanced performance | General purpose | Standard |
4. Build Knowledge Base
Equip your agent with domain-specific knowledge by uploading files or entering raw data. This creates a custom knowledge base that your agent can reference during conversations.Knowledge Base Architecture
Knowledge Processing Pipeline:| Stage | Function | Technology | Performance Impact |
|---|---|---|---|
| Chunking | Text segmentation | Semantic boundaries | Context coherence |
| Embedding | Vector generation | Model-specific encoders | Search accuracy |
| Indexing | Vector organization | FAISS/Pinecone | Retrieval speed |
| Ranking | Relevance scoring | Cosine similarity | Response quality |
5. Configure Voice
Give your agent a unique speaking voice with advanced customization options for natural conversation experiences.Voice Synthesis Pipeline
Voice Configuration Matrix:| Category | Voice | Characteristics | Use Cases | Emotional Range |
|---|---|---|---|---|
| Masculine | Apex | Authoritative, clear | Professional, leadership | High |
| Masculine | Felix | Warm, approachable | Customer service, education | Medium |
| Masculine | Isaac | Technical, precise | Documentation, tutorials | Low |
| Feminine | Avarice | Confident, dynamic | Sales, presentations | High |
| Feminine | Archive | Scholarly, measured | Research, analysis | Medium |
| Feminine | Rose | Gentle, empathetic | Healthcare, support | High |
| Machine | Prometheus | Robotic, systematic | Technical systems | None |
| Machine | Caesium | Futuristic, crisp | Gaming, sci-fi | Low |
| Machine | Sumatra | Industrial, powerful | Announcements, alerts | None |
6. Character Details & Personality
Define your agent’s identity, personality traits, and memory capabilities to create authentic and consistent interactions.Memory Architecture & Context Management
Memory Configuration Schema:| Memory Type | Retention | Access Pattern | Use Case | Storage Cost |
|---|---|---|---|---|
| Working | Current session | Immediate | Active conversation | RAM |
| Short-term | 24 hours | Recent context | Follow-up questions | Cache |
| Long-term | Persistent | Semantic search | User preferences | Vector DB |
| Episodic | Permanent | Temporal queries | Historical interactions | Archive |
7. Agent Behavior Configuration
Fine-tune how your agent thinks, acts, and responds using comprehensive behavioral parameters that shape its decision-making process. Behavioral Dimensions: Reasoning Patterns:- Bold vs Safe: Risk tolerance in decision-making
- Heuristic vs Logical: Problem-solving approach preference
- Flexible vs Structured: Adaptability to changing contexts
- Inductive vs Deductive: Learning and inference methodology
- Controlled vs Autonomous: Level of independent operation
- Rigid vs Adaptive: Response to unexpected situations
- Manual vs Tool-based: Preference for direct or tool-assisted actions
- Exact vs Approximate: Precision requirements for outputs
- Passive vs Active: Information gathering approach
- Surface vs Deep: Analysis depth preference
- Narrow vs Wide: Scope of environmental awareness
- Skeptical vs Accepting: Information validation strictness
- Short-term vs Long-term: Planning horizon preference
- Perfect vs Fast: Quality vs speed trade-offs
- Active vs Passive: Proactive vs reactive planning
- Single-path vs Multi-path: Strategy complexity
- Leader vs Team Member: Role preference in group settings
- Negotiator vs Listener: Communication approach
- Transparent vs Minimal: Information sharing tendency
- Fixed vs Dynamic: Adaptability in team contexts
- Stable vs Agile: Learning rate and adaptation speed
- Explicit vs Implicit: Learning methodology preference
- Conservative vs Curious: Exploration vs exploitation balance
- Preventative vs Reactive: Problem anticipation approach
8. Review & Finalize
Before deployment, review all configuration settings and make final adjustments to ensure optimal agent performance. Configuration Summary: Core Components:- MCP Server: News Aggregator MCP (10 cu)
- Framework: LangChain (5 cu)
- Model: OpenAI GPT-3.5 Legacy (5 cu)
- Voice: Apex, Normal tempo, Friendly style (4 cu)
- Name: Tensor’s Agent
- Bio: Test Agent
- Tone: Friendly
- Memory: Reference Chat, Medium frequency
- Your Name: Tensor
- Data Source: Pasted data about AI and elections
- Content: Structured information from BBC news sources
- Processing: Vectorized and indexed for retrieval (10 cu)
- Reasoning: Bold, Heuristic, Structured, Inductive
- Acting: Controlled, Rigid, Manual, Approximate
- Observing: Passive, Deep, Narrow, Skeptical
- Planning: Short Term, Fast, Active, Single Path
- Collaborating: Team Member, Negotiator, Transparent, Fixed
- Self-Refining: Stable, Explicit, Curious, Preventative
- Review all configuration settings
- Test agent responses with sample queries
- Adjust behavioral parameters if needed
- Deploy to production environment
- Monitor initial performance metrics
Interacting with Your Agent
Voice Conversations
Once deployed, interact with your agent through natural voice conversations. The integrated voice interface provides a seamless, hands-free experience. Voice Features:- Real-time speech recognition
- Natural conversation flow
- Voice response with emotional inflection
- Multi-turn dialogue support
- Voice command recognition
Agent Monitoring & Analytics
Comprehensive Monitoring Dashboard
Track your agent’s performance, usage patterns, and system health through detailed analytics and real-time monitoring.Performance Analytics Dashboard
Real-time Metrics: System Health Monitoring:| Metric | Current Value | Threshold | Status | Trend |
|---|---|---|---|---|
| Response Time | 440ms | < 1000ms | ✅ Optimal | ↓ Improving |
| Success Rate | 98% | > 95% | ✅ Excellent | ↑ Stable |
| Memory Usage | 128 MB | < 512 MB | ✅ Efficient | → Stable |
| CPU Usage | 24% | < 80% | ✅ Optimal | ↓ Decreasing |
| Active Users | 5 | < 100 | ✅ Normal | ↑ Growing |
Code & Development
Access your agent’s underlying code structure and configuration files for advanced customization and debugging. Available Files:agent.js- Main agent logic and executiontensorone_client.py- TensorOne AI SDK integrationconfig.json- Agent configuration settingsREADME.md- Documentation and setup instructionstypes.ts- TypeScript definitionslogger.js- Logging and monitoring utilitiestest.py- Testing and validation scripts
Compute Resources & GPU Infrastructure
Tensor One provides scalable compute resources through high-performance GPU infrastructure. Compute units are allocated based on your rented GPU capacity.GPU Rental Tiers
GPU Performance & Compute Units:| GPU Model | VRAM | Compute Units/Hour | Model Support | Concurrent Agents |
|---|---|---|---|---|
| RTX 4090 | 24GB | 50-75 cu/hr | All standard models | 3-5 agents |
| A4000 | 16GB | 40-60 cu/hr | Efficient models | 2-4 agents |
| A5000 | 24GB | 60-80 cu/hr | All models | 4-6 agents |
| A6000 | 48GB | 80-120 cu/hr | Large models | 6-10 agents |
| Tesla A100 | 40-80GB | 120-200 cu/hr | All models + training | 10-15 agents |
| Tesla H100 | 80GB | 200-300 cu/hr | Latest models | 15-25 agents |
Cost Structure
GPU Rental Costs:- Paid in cryptocurrency via Coinbase Commerce
- Hourly billing with per-minute granularity
- Auto-scaling based on demand
- Reserved instances for cost optimization
- Generated automatically based on rented GPU capacity
- Real-time monitoring and allocation
- Overflow protection and queue management
- Usage analytics and optimization recommendations
- MCP Server fees: 10-20 cu per server
- Model inference: 2-15 cu per request (model dependent)
- Voice synthesis: 1-4 cu per minute
- Knowledge base processing: 5-10 cu per MB
- Memory operations: 0.1-1 cu per query
Best Practices
Agent Design
- Start with a clear use case and target audience
- Choose the appropriate framework for your needs
- Optimize knowledge base for relevant, high-quality content
- Test thoroughly before public deployment
Performance Optimization
- Monitor resource utilization regularly
- Optimize knowledge base size and structure
- Use appropriate models for your complexity requirements
- Implement proper error handling and fallbacks
User Experience
- Design consistent personality and communication style
- Provide clear instructions and capabilities
- Implement helpful error messages and guidance
- Regularly update knowledge base and capabilities