Docs/Llmops

LLMOps Framework

Comprehensive operations management for Large Language Models and AI agents at enterprise scale.

What is LLMOps?

LLMOps (Large Language Model Operations) is the overarching discipline of managing, deploying, monitoring, and optimizing LLM-based applications and AI agents in production environments. It encompasses the entire lifecycle of LLM operations from development to production.

AgenticAnts provides enterprise-grade LLMOps capabilities through our integrated platform that implements three critical pillars: AI Cost (FinOps), AI Resilient (SRE), and AI Governance specifically designed for AI operations.

LLMOps Framework Architecture

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Key LLMOps Capabilities

1. Model Lifecycle Management

Model Selection - Choose optimal models for specific use cases
Version Control - Track model updates and rollbacks
A/B Testing - Compare model performance systematically
Model Registry - Centralized model inventory and metadata

2. Prompt Operations

Prompt Versioning - Track and manage prompt iterations
Prompt Testing - Automated testing and validation
Prompt Optimization - Performance and cost optimization
Template Management - Reusable prompt templates

3. Performance Optimization

Latency Monitoring - Track response times across models
Throughput Analysis - Monitor requests per second
Token Efficiency - Optimize token usage for cost and performance
Caching Strategies - Implement intelligent caching for common queries

4. Model Governance

Access Control - Role-based access to models and prompts
Usage Policies - Define and enforce usage guidelines
Quality Gates - Automated quality checks before deployment
Compliance Monitoring - Ensure adherence to regulations

Getting Started with LLMOps

Quick Setup

Install the SDK with npm install ants-platform. Tracing requires Node.js 20+. Set up the tracer once at startup in an instrumentation.ts that is imported before any instrumented code runs:

typescript

// instrumentation.ts — import this first, at process startup const provider = new NodeTracerProvider({ spanProcessors: [ new AntsPlatformSpanProcessor({ publicKey: process.env.ANTS_PLATFORM_PUBLIC_KEY, secretKey: process.env.ANTS_PLATFORM_SECRET_KEY, baseUrl: "https://api.agenticants.ai", }), ], }) provider.register() setAntsPlatformTracerProvider(provider)

For prompts, datasets, and scores, use the REST client:

typescript

const client = new AntsPlatformClient({ publicKey: process.env.ANTS_PLATFORM_PUBLIC_KEY, secretKey: process.env.ANTS_PLATFORM_SECRET_KEY, baseUrl: "https://api.agenticants.ai", })

Basic Model Monitoring

Install the SDK with pip install ants-platform. The OpenAI drop-in replacement traces every call automatically — just swap the import, and wrap your logic in a span:

python

# Monitor model performance from ants_platform import AntsPlatform from ants_platform.openai import openai client = AntsPlatform( public_key="pk_...", # or set ANTS_PLATFORM_PUBLIC_KEY secret_key="sk_...", # or set ANTS_PLATFORM_SECRET_KEY host="https://api.agenticants.ai", ) # Track model usage with client.start_as_current_span(name="customer-support-query"): response = openai.chat.completions.create( model="gpt-4", messages=[{"role": "user", "content": user_query}], ) # AntsPlatform automatically captures: # - Model used # - Token consumption # - Response time # - Cost attribution # - Quality metrics client.flush() # flush before the process exits

LLMOps Best Practices

1. Start with Observability

Implement comprehensive monitoring from day one
Track both technical and business metrics
Set up alerts for cost and performance thresholds from the dashboard at https://app.agenticants.ai (alerts and metrics/trends are dashboard features, not SDK calls)

2. Implement Cost Controls

Set budgets and alerts for each model
Track costs per customer, team, or use case
Optimize token usage through prompt engineering

3. Ensure Security and Compliance

Implement PII detection and redaction
Set up content filtering and guardrails
Maintain audit trails for compliance

4. Plan for Scale

Design for multi-model architectures
Implement proper versioning and rollback strategies
Plan for model updates and migrations

Integration with Existing Workflows

AgenticAnts integrates seamlessly with your existing AI development workflows:

LangChain - Automatic tracing and monitoring
LlamaIndex - Performance and cost tracking
OpenAI - Direct API integration
Custom Models - Universal monitoring support

Get started with LLMOPs →