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
Vector Search
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?