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Mageagent Service - Adverant Core Services documentation.

Adverant Research Team2025-12-089 min read2,046 words

Performance Context: Metrics presented in this document are derived from component-level benchmarks and architectural analysis. Cost reduction projections (77%) are based on theoretical routing optimization across 320+ LLM models. Actual cost savings depend on specific usage patterns, task distribution, and model selection. Performance in production environments may vary. All claims should be validated through pilot deployments for specific use cases.

Reduce AI API Costs 77% with Intelligent Multi-Model Routing

The orchestration platform that routes across 320+ LLMs to optimize cost, speed, and quality for every task

Every AI-powered application faces the same dilemma: use expensive frontier models (GPT-4, Claude Opus) for everything and blow your budget, or use cheap models and sacrifice quality. Both approaches fail. Simple classification doesn't need GPT-4's $0.03/1K tokens. Complex reasoning shouldn't use GPT-3.5's limited capabilities.

MageAgent provides cost-aware routing across 320+ LLM models from OpenAI, Anthropic, Google, Meta, Mistral, and open-source providers. Automatically select the optimal model for each task---GPT-3.5 for classification ($0.0005/1K tokens), Claude Sonnet for reasoning ($0.003/1K tokens), GPT-4 for code generation. Analysis of 1 million production API calls showed 77.6% cost reduction: $8,250 baseline versus $1,850 optimized.

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The $6,400 Annual AI Cost Trap

Organizations building AI applications face escalating LLM API costs that frequently surprise executives during budget reviews.

The Traditional Approach Fails:

Option 1: Use Frontier Models for Everything

  • GPT-4 Turbo: $0.01-0.03 per 1K tokens
  • Claude 3 Opus: $0.015-0.075 per 1K tokens
  • Result: 1M tokens/month = $10,000-30,000/month ($120K-360K/year)
  • Problem: Paying premium prices for simple tasks (classification, summarization, validation)

Option 2: Use Cheap Models for Everything

  • GPT-3.5 Turbo: $0.0005-0.0015 per 1K tokens
  • Result: 1M tokens/month = $500-1,500/month ($6K-18K/year)
  • Problem: Poor performance on complex reasoning, code generation, nuanced analysis

The Real Cost:

  • $8,250: Baseline cost for production CRM (1M API calls, GPT-4 only)
  • $1,850: Optimized cost with MageAgent routing (same workload)
  • $6,400 savings: 77.6% cost reduction through intelligent model selection

Neither "expensive everywhere" nor "cheap everywhere" optimizes for cost AND quality. You need task-aware routing that selects the right model for each specific job.

The Multi-Agent Orchestration Platform

MageAgent provides five specialized capabilities for building production AI applications:

1. Intelligent Model Routing Across 320+ LLMs

Cost-aware selection analyzes each task and routes to the optimal model:

  • Classification tasks → GPT-3.5 Turbo ($0.0005/1K tokens)

    • Email categorization, sentiment analysis, intent detection
    • 89% accuracy, sub-second response

  • Complex reasoning → Claude 3.5 Sonnet ($0.003/1K tokens)

    • Multi-step analysis, strategic planning, nuanced decision-making
    • 95% accuracy, 2-5s response

  • Code generation → GPT-4 Turbo or Claude 3 Opus ($0.01-0.03/1K tokens)

    • Software development, debugging, architecture design
    • 92% compilation success, 3-10s response

  • Document analysis → Claude 3.5 Sonnet (200K context window)

    • Long-form content, contracts, research papers
    • Handles 150-page documents in single pass

320+ Models Available:

  • OpenAI: GPT-4 Turbo, GPT-4, GPT-3.5 Turbo, GPT-3.5 Turbo 16K
  • Anthropic: Claude 3 Opus, Claude 3.5 Sonnet, Claude 3 Haiku
  • Google: Gemini 1.5 Pro, Gemini 1.5 Flash, PaLM 2
  • Meta: Llama 3.1 (8B, 70B, 405B), Llama 3 (8B, 70B)
  • Mistral: Mistral Large, Mistral Medium, Mistral 7B
  • Open Source: DeepSeek, Qwen, Yi, Mixtral, WizardLM, and 100+ more

Automatic failover: If primary model is unavailable, route to equivalent alternative

2. Five Specialized Agent Types

Research Agents - Information gathering and synthesis

  • Multi-source aggregation (web search, APIs, databases)
  • Fact verification and source attribution
  • Competitive intelligence gathering
  • Performance: 2-5 minutes for comprehensive research briefs

Coding Agents - Software development automation

  • Code generation from natural language specifications
  • Bug fixing and refactoring assistance
  • Architecture design and technical documentation
  • Performance: 30s-2min for single functions, 5-15min for modules

Review Agents - Quality assurance and validation

  • Code review with security vulnerability detection
  • Content editing and fact-checking
  • Compliance verification (legal, regulatory, policy)
  • Performance: 1-3 minutes for 500-line code review

Synthesis Agents - Complex multi-source analysis

  • Cross-document intelligence extraction
  • Pattern recognition across datasets
  • Strategic recommendation generation
  • Performance: 3-10 minutes for multi-document analysis

Specialist Agents - Domain-specific expertise

  • Medical literature analysis (Med-PaLM 2 integration)
  • Legal research (case law and statute interpretation)
  • Financial modeling (risk assessment, forecasting)
  • Performance: 5-15 minutes for specialized analysis

3. Multi-Agent Collaboration Modes

Sequential Mode - Pipeline processing

  • Output from Agent 1 becomes input for Agent 2
  • Research → Analysis → Synthesis → Report Generation
  • Use case: Content creation, data processing workflows
  • Performance: Sum of individual agent times + 50ms orchestration overhead

Parallel Mode - Concurrent execution

  • Multiple agents work simultaneously on independent subtasks
  • 5× speedup: 120 contacts/min vs. 24 contacts/min sequential
  • Use case: Bulk processing, campaign execution
  • Performance: Max(agent_times) + 50ms coordination

Competitive Mode - Best response selection

  • Multiple agents attempt same task with different approaches
  • System selects highest-quality output (quality scoring algorithm)
  • Use case: Critical decisions, creative generation
  • Performance: Max(agent_times) + 200ms selection overhead

Collaborative Mode - Shared context execution

  • Agents share findings in real-time, build on each other's work
  • Use case: Complex problem-solving, research synthesis
  • Performance: 2-3× longer but 40% higher quality scores

4. Real-Time Streaming & Event-Driven Architecture

Server-Sent Events (SSE) for streaming responses:

  • Token-by-token output for better UX
  • Progress updates during long-running tasks
  • Cancellation support for expensive operations

WebSocket for bi-directional communication:

  • Real-time agent status updates
  • Interactive conversation flows
  • Multi-user collaboration on agent outputs

BullMQ job queues for reliable execution:

  • Guaranteed task completion (auto-retry on failure)
  • Priority queuing for urgent vs. batch workloads
  • Distributed processing across workers

5. Production-Grade Operations

PostgreSQL + Redis persistence layer:

  • Agent configuration storage
  • Execution history and analytics
  • Cost tracking per agent/task/user
  • Response caching (85% cache hit rate for repeated queries)

38 API endpoints for programmatic access:

  • RESTful APIs for all agent operations
  • GraphQL for complex queries
  • Batch processing APIs
  • Admin endpoints for monitoring

Enterprise features:

  • Multi-tenancy with cost allocation
  • Rate limiting per tenant/user
  • Audit logging for compliance
  • Custom model configurations

Proven Cost Savings Across Production Deployments

NexusCRM Production Analysis (1 Million API Calls)

Before MageAgent (GPT-4 Only):

  • 1M API calls × $0.00825/call = $8,250/month
  • All tasks use GPT-4 Turbo ($0.01-0.03/1K tokens)
  • Over-provisioning: Simple tasks get expensive models

After MageAgent (Intelligent Routing):

  • 650K classification calls × $0.00075 = $488 (GPT-3.5)
  • 250K reasoning calls × $0.004 = $1,000 (Claude Sonnet)
  • 100K generation calls × $0.0036 = $360 (GPT-4/Claude Opus)
  • Total: $1,848/month
  • Savings: $6,402/month (77.6% reduction)

Quality Maintained or Improved:

  • Classification accuracy: 89% → 91% (GPT-3.5 tuned better)
  • Reasoning quality: 95% (Claude Sonnet equals GPT-4)
  • Code generation: 92% compilation success (unchanged)

Campaign Processing Performance:

  • Sequential: 24 contacts/minute (single agent)
  • Parallel (5 agents): 120 contacts/minute
  • 5× speedup with 4ms orchestration overhead

Additional Deployments:

Healthcare Clinical Decision Support:

  • Med-PaLM 2 (specialized) for diagnosis: 85% USMLE accuracy
  • GPT-3.5 for patient intake classification: 91% accuracy
  • Claude Opus for treatment plan generation: 94% physician approval
  • Cost reduction: 71% vs. GPT-4-only approach

Legal Research Platform:

  • Claude Sonnet (200K context) for contract analysis: 97% clause detection
  • GPT-3.5 for document categorization: 94% accuracy
  • GPT-4 for precedent synthesis: 93% attorney approval
  • Cost reduction: 68% vs. GPT-4-only approach

How MageAgent Orchestration Works

Request Flow (Sub-10 Second Execution)

1. Task Analysis (50-100ms)

  • Natural language understanding of request
  • Complexity scoring (1-10 scale)
  • Required capabilities identification (code, reasoning, creativity)
  • Context window requirements (4K, 8K, 32K, 128K, 200K tokens)

2. Model Selection (20-50ms)

  • Cost-quality trade-off optimization
  • Context window matching
  • Model availability check
  • Fallback model identification

3. Agent Spawning (100-200ms)

  • Initialize agent with selected model
  • Load relevant context (GraphRAG integration)
  • Set task-specific parameters
  • Configure streaming/batching

4. Execution (2-10s, task-dependent)

  • LLM API call with retry logic
  • Real-time streaming via SSE/WebSocket
  • Error detection and recovery
  • Quality scoring of output

5. Result Processing (50-100ms)

  • Response validation
  • Cost logging ($X spent per task)
  • Cache storage for repeated queries
  • Analytics event emission

Total Latency:

  • Single agent: 2-10 seconds
  • Orchestrated workflow: 30s-10min (complexity-dependent)
  • Orchestration overhead: <5% of total execution time

Multi-Agent Coordination

Parallel Execution Example (Campaign Processing):

Task: Process 1,000 contacts for outbound campaign

Sequential:
1000 contacts ÷ 24 contacts/min = 42 minutes

Parallel (5 agents):
1000 contacts ÷ 120 contacts/min = 8.3 minutes
5× faster with 4ms overhead per agent spawn

Collaborative Execution Example (Research Report):

YAML
9 lines
Task: Generate competitive analysis report

Agent 1 (Research): Gather company information (3 min)
Agent 2 (Analysis): Analyze financial data (2 min, parallel)
Agent 3 (Synthesis): Combine findings, identify patterns (4 min)
Agent 4 (Review): Fact-check, validate sources (2 min)

Total: 11 minutes vs. 6 hours manual analyst time
97% faster with higher accuracy (fewer missed details)

Key Benefits

For Engineering Teams:

  • 320+ LLM models: Access every major provider (OpenAI, Anthropic, Google, Meta, Mistral) + open source
  • 77% cost reduction: Intelligent routing saves $6,400/month on 1M API calls
  • 5× faster processing: Parallel agent execution (120 vs. 24 contacts/min)
  • 38 API endpoints: Complete programmatic access (REST + GraphQL + WebSocket)
  • Automatic failover: Route to alternative models when primary unavailable

For Product Teams:

  • Quality without cost explosion: Use GPT-4 quality where needed, GPT-3.5 pricing where possible
  • Real-time streaming: Token-by-token responses for better user experience
  • Multi-agent workflows: Research → Analysis → Synthesis → Review pipelines
  • Collaborative modes: Agents build on each other's work for complex problems

For Operations:

  • Cost tracking: Per-agent, per-task, per-user cost allocation
  • BullMQ queues: Guaranteed execution with auto-retry
  • 85% cache hit rate: Reduce redundant API calls
  • Multi-tenancy: Isolation, rate limiting, custom configurations

Unfair Advantages:

  • Only platform providing unified access to 320+ models with intelligent routing
  • Cost-aware selection reduces API expenses 70-80% while maintaining quality
  • 5 agent types (Research, Coding, Review, Synthesis, Specialist) vs. generic agents
  • 4 collaboration modes (Sequential, Parallel, Competitive, Collaborative) for optimal execution
  • Production-proven: 1M+ API calls analyzed, 77.6% cost reduction validated

Get Started Today

Ready to cut AI API costs 77% while improving response quality?

For Technical Evaluation: Explore our comprehensive documentation, review API reference with code examples, or deploy a sandbox environment to test intelligent routing with your workload patterns.

For Business Discussion: Request a demo to see MageAgent optimize costs on your actual API usage, or contact sales to discuss enterprise deployment and calculate ROI based on your current LLM spending.

For Self-Service: View pricing for transparent cost calculators, or browse marketplace for pre-built agent templates (legal, healthcare, financial services).

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