Contents

Enterprise-Grade AI Capabilities

Agent Party provides a comprehensive set of technical capabilities designed for production use in enterprise environments. Our platform goes beyond experimental AI to deliver reliable, scalable, and secure collaboration between AI agents.

Communication & Streaming

Real-Time Streaming

Continuous, bidirectional communication between agents and users:

  • WebSocket-Based: Maintain persistent connections for instant updates
  • Incremental Updates: See results as they’re generated, not just at completion
  • Streaming Tokens: Observe agent thought processes in real-time
  • Live Feedback: Provide guidance during processing, not just afterward
// Example client-side streaming implementation
const socket = new WebSocket('wss://api.agentparty.ai/stream/task-1234');

socket.onmessage = (event) => {
  const update = JSON.parse(event.data);
  
  if (update.type === 'thinking') {
    // Display agent thought process
    appendToThinkingDisplay(update.content);
  } else if (update.type === 'partial_result') {
    // Update UI with incremental results
    updateResultDisplay(update.content);
  } else if (update.type === 'completed') {
    // Handle task completion
    finalizeDisplay(update.content);
  }
};

// Send feedback during processing
function sendUserFeedback(feedback) {
  socket.send(JSON.stringify({
    type: 'user_feedback',
    content: feedback
  }));
}

Structured Data Exchange

Standardized formats for reliable communication:

  • Schema Validation: Ensure data consistency between agents
  • Strongly Typed: Prevent data type mismatches and errors
  • Versioned Schemas: Support backward compatibility
  • Event-Driven: Enable reactive processing patterns
// Example agent event schema
{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "type": "object",
  "required": ["event_id", "event_type", "timestamp", "agent_id", "payload"],
  "properties": {
    "event_id": {
      "type": "string",
      "format": "uuid"
    },
    "event_type": {
      "type": "string",
      "enum": ["task_started", "task_progress", "task_completed", "task_failed"]
    },
    "timestamp": {
      "type": "string",
      "format": "date-time"
    },
    "agent_id": {
      "type": "string"
    },
    "payload": {
      "type": "object"
    },
    "correlation_id": {
      "type": "string"
    }
  }
}

Multi-Modal Support

Exchange and process diverse content types:

  • Text Processing: Natural language understanding and generation
  • Image Analysis: Visual content recognition and generation
  • Audio Processing: Speech recognition and synthesis
  • Video Understanding: Motion and context analysis
  • Seamless Transitions: Switch between modalities as needed

Long-Running Operations

Task Persistence

Reliable execution of complex, time-consuming operations:

  • Checkpointing: Save progress at defined intervals
  • Resumability: Continue from interruptions without data loss
  • State Management: Maintain consistent context throughout execution
  • Resource Tracking: Monitor and optimize resource consumption
# Example checkpoint-based processing
class LongRunningTask:
    def __init__(self, task_id):
        self.task_id = task_id
        self.state_manager = StateManager()
        
    def process(self):
        # Restore from checkpoint if available
        checkpoint = self.state_manager.get_checkpoint(self.task_id)
        if checkpoint:
            current_step = checkpoint.get('current_step', 0)
            intermediate_results = checkpoint.get('results', {})
        else:
            current_step = 0
            intermediate_results = {}
            
        # Process remaining steps
        steps = self.get_processing_steps()
        for step_num in range(current_step, len(steps)):
            step = steps[step_num]
            result = step.execute(intermediate_results)
            intermediate_results[step.name] = result
            
            # Create checkpoint after each step
            self.state_manager.save_checkpoint(self.task_id, {
                'current_step': step_num + 1,
                'results': intermediate_results
            })
            
        return intermediate_results

Progress Tracking

Comprehensive visibility into task execution:

  • Percentage Complete: Accurate progress estimation
  • Stage Awareness: Current processing phase identification
  • Time Remaining: Predictive completion time estimates
  • Milestone Notifications: Updates at significant stages

Asynchronous Execution

Background processing with notification patterns:

  • Fire-and-Forget: Submit tasks and receive results when completed
  • Polling: Check status at intervals
  • Callbacks: Receive notifications on completion
  • Webhooks: Integrate with external systems

Memory & Knowledge Management

Semantic retrieval based on meaning, not just keywords:

  • Multi-Modal Embeddings: Create vectors from text, images, and audio
  • Semantic Similarity: Find conceptually related content
  • Contextual Search: Retrieve information relevant to current context
  • Real-Time Indexing: Add new information to searchable knowledge base
# Example vector search implementation
from agent_party.memory import VectorStore

# Initialize vector store
vector_store = VectorStore(
    dimensions=1536,  # OpenAI embedding dimensions
    metric="cosine"   # Similarity metric
)

# Index document with metadata
def index_document(doc_id, content, metadata=None):
    embedding = embedding_service.embed(content)
    vector_store.add(
        id=doc_id,
        vector=embedding,
        metadata=metadata
    )

# Search for similar content
def semantic_search(query, filters=None, limit=5):
    query_embedding = embedding_service.embed(query)
    results = vector_store.search(
        vector=query_embedding,
        filters=filters,
        limit=limit
    )
    return [
        {
            "id": match.id,
            "score": match.score,
            "metadata": match.metadata
        }
        for match in results
    ]

Multi-Context Memory

Sophisticated memory structures for nuanced understanding:

  • Short-Term Memory: Immediate conversation context
  • Working Memory: Active task-related information
  • Episodic Memory: Historical interactions and events
  • Semantic Memory: Factual knowledge and concepts
  • Memory Consolidation: Automatic migration between memory types

Knowledge Graph

Structured representation of interconnected information:

  • Entity Extraction: Identify key concepts automatically
  • Relationship Mapping: Connect related concepts
  • Ontology Management: Organize by hierarchical categories
  • Inference Engine: Derive new knowledge from existing facts
  • Graph Queries: Retrieve complex patterns and paths

Orchestration & Workflow

Agent Collaboration

Coordinate multiple agents working together:

  • Team Formation: Assemble optimal agent combinations
  • Role Assignment: Designate responsibilities based on capabilities
  • Workflow Coordination: Manage hand-offs between agents
  • Conflict Resolution: Resolve contradictory agent outputs
  • Performance Optimization: Improve team effectiveness over time

Complex Workflows

Sophisticated process management capabilities:

  • Conditional Branching: Different paths based on conditions
  • Parallel Execution: Multiple simultaneous processes
  • Join Patterns: Combine results from parallel branches
  • Sub-Workflows: Nested processes for complex tasks
  • Error Handling: Graceful recovery from failures
# Example workflow definition
name: Document Analysis Pipeline
version: 1.0
input:
  document_url: string
  analysis_depth: string

workflow:
  - id: retrieve_document
    agent: content_retriever
    input:
      url: ${input.document_url}
    output:
      document_content: result.content
      
  - id: analyze_content
    agent: content_analyzer
    input:
      content: ${workflow.retrieve_document.document_content}
      depth: ${input.analysis_depth}
    output:
      analysis_result: result
      
  - id: branch_on_content_type
    type: branch
    condition: ${workflow.analyze_content.analysis_result.content_type}
    branches:
      text:
        next: generate_text_summary
      code:
        next: analyze_code_quality
      financial:
        next: extract_financial_metrics
        
  - id: generate_text_summary
    agent: summarizer
    input:
      content: ${workflow.retrieve_document.document_content}
      analysis: ${workflow.analyze_content.analysis_result}
    next: final_report
    
  # Additional branch definitions...
  
  - id: final_report
    agent: report_generator
    input:
      document: ${workflow.retrieve_document.document_content}
      analysis: ${workflow.analyze_content.analysis_result}
      summary: ${branch_result}
    output:
      final_report: result.report

Human-in-the-Loop

Seamless integration of human judgment:

  • Approval Workflows: Request human verification for critical decisions
  • Intervention Points: Allow human guidance at predefined stages
  • Exception Handling: Escalate complex cases to human experts
  • Feedback Incorporation: Learn from human corrections

Security & Governance

Authentication & Authorization

Enterprise-grade security controls:

  • OAuth/OIDC Integration: Standard authentication protocols
  • Role-Based Access: Permissions aligned with organizational roles
  • Fine-Grained Policies: Control access at the resource level
  • API Key Management: Secure machine-to-machine authentication
  • Single Sign-On: Seamless integration with identity providers

Audit & Compliance

Comprehensive tracking for regulatory compliance:

  • Immutable Audit Log: Tamper-proof record of all operations
  • Access Tracking: Complete history of who accessed what
  • Data Lineage: Trace information flow through the system
  • Compliance Reporting: Pre-built reports for common standards
  • Retention Policies: Configurable data retention periods

Prompt Security

Protection against prompt manipulation and data extraction:

  • Prompt Injection Protection: Detect and prevent malicious prompts
  • Input Sanitization: Clean potentially harmful content
  • Context Boundaries: Isolate execution contexts
  • Output Filtering: Prevent sensitive data disclosure
  • Continuous Monitoring: Detect abnormal behavior patterns

Integration & Extensibility

API-First Design

Comprehensive interfaces for integration:

  • REST API: Standard HTTP-based integration
  • GraphQL API: Flexible query capabilities
  • Webhooks: Event-based notifications
  • SDKs: Client libraries for popular languages
  • OpenAPI/Swagger: Complete API documentation

Custom Agent Development

Create specialized agents tailored to your needs:

  • Agent SDK: Framework for building custom agents
  • Template System: Start from tested agent templates
  • Testing Framework: Validate agent behavior
  • Deployment Pipeline: Streamlined release process
  • Capability Registry: Publish agent capabilities
# Example custom agent using the Agent Party SDK
from agent_party import Agent, register_capability

class MarketingCopywriterAgent(Agent):
    def __init__(self, config):
        super().__init__(name="marketing_copywriter", config=config)
        self.tone_analyzer = ToneAnalyzer()
        self.brand_db = BrandGuidelineDB(config.brand_db_path)
        
    @register_capability(
        name="generate_marketing_copy",
        description="Creates marketing copy that aligns with brand guidelines",
        input_schema={
            "product_name": "string",
            "target_audience": "string",
            "key_benefits": "array",
            "tone": "string?",
            "max_length": "integer?"
        },
        output_schema={
            "headline": "string",
            "body_copy": "string",
            "calls_to_action": "array"
        }
    )
    def generate_marketing_copy(self, input_data):
        # Implementation details
        brand_guidelines = self.brand_db.get_guidelines()
        tone = input_data.get("tone", "professional")
        
        # Generate the copy using LLM with brand guidelines as context
        result = self.llm.generate(
            prompt=self.create_copywriting_prompt(input_data, brand_guidelines),
            max_length=input_data.get("max_length", 500)
        )
        
        # Analyze and adjust the tone
        adjusted_copy = self.tone_analyzer.adjust_to_tone(result, tone)
        
        # Format the response
        return {
            "headline": self.extract_headline(adjusted_copy),
            "body_copy": self.extract_body(adjusted_copy),
            "calls_to_action": self.extract_ctas(adjusted_copy)
        }

Third-Party Integration

Connect with your existing tools and services:

  • Pre-built Connectors: Integrations with popular platforms
  • Custom Integrations: Framework for building connectors
  • Data Import/Export: Bulk data transfer capabilities
  • Authorization Delegation: Secure access to third-party services
  • Event Bridging: Translate between event systems

Observability & Analytics

Comprehensive Monitoring

Full visibility into system health and performance:

  • Real-Time Metrics: Current system state and performance
  • Historical Trends: Performance patterns over time
  • Alerting: Proactive notification of issues
  • Distributed Tracing: End-to-end request tracking
  • Log Aggregation: Centralized logging across components

AI Performance Analytics

Measure and optimize AI agent effectiveness:

  • Quality Metrics: Accuracy, relevance, and coherence
  • Efficiency Metrics: Response time and resource usage
  • Usage Patterns: Most valuable agent capabilities
  • Improvement Tracking: Performance trends over time
  • A/B Testing: Compare different agent configurations

Enterprise Readiness

High Availability

Reliable operation for mission-critical applications:

  • Multi-Region Deployment: Geographic redundancy
  • Automatic Failover: Seamless recovery from failures
  • Load Balancing: Distribute traffic for optimal performance
  • Disaster Recovery: Comprehensive backup and restore
  • SLA Monitoring: Track compliance with availability targets

Scalability

Handle growing workloads with ease:

  • Horizontal Scaling: Add capacity as needed
  • Auto-Scaling: Adjust to changing demand
  • Resource Governance: Fair allocation across users
  • Rate Limiting: Protect against traffic spikes
  • Queue Management: Handle backpressure gracefully

Multi-Tenancy

Serve multiple organizations from a single deployment:

  • Tenant Isolation: Strict separation between organizations
  • Customization: Tenant-specific configurations
  • Resource Allocation: Dedicated or shared resources
  • Tenant Analytics: Usage tracking by organization
  • White-Labeling: Customized branding options

Getting Started

Ready to leverage these capabilities in your organization?