AI Inference Guide
Core Concepts
Deployment Options
Tools & Services
Advanced Topics
Agentic AI Inference Patterns
The Agentic Inference Challenge
Agentic AI systems exhibit fundamentally different inference patterns compared to traditional AI applications. They require multi-stage reasoning, tool orchestration, and dynamic resource allocation that can increase costs by 5-25x over simple query-response systems.
Unique Inference Patterns
Multi-Stage Reasoning Cycles
Plan → Reflect → Act loops that require multiple inference calls
Agentic: 1 query = 5-15 inference calls
Tool Invocation Cascades
Each tool call triggers new inference cycles for result interpretation
Context Accumulation
Growing memory requirements across interaction chains
Decision Tree Exploration
Multiple reasoning paths evaluated in parallel
Cost Impact Analysis
Traditional Systems
Agentic Systems
Optimization Strategies
Dynamic Resource Allocation
Route simple tasks to edge, complex reasoning to cloud
Context Compression
Intelligent memory management to reduce token overhead
Speculative Execution
Pre-compute likely next steps while current ones execute
Budget-Aware Reasoning
Dynamic quality-cost trade-offs based on inference budgets
Agentic AI Inference Patterns
The Agentic Inference Challenge
Agentic AI systems exhibit fundamentally different inference patterns compared to traditional AI applications. They require multi-stage reasoning, tool orchestration, and dynamic resource allocation that can increase costs by 5-25x over simple query-response systems.
Unique Inference Patterns
Multi-Stage Reasoning Cycles
Plan → Reflect → Act loops that require multiple inference calls
Agentic: 1 query = 5-15 inference calls
Tool Invocation Cascades
Each tool call triggers new inference cycles for result interpretation
Context Accumulation
Growing memory requirements across interaction chains
Decision Tree Exploration
Multiple reasoning paths evaluated in parallel
Cost Impact Analysis
Traditional Systems
Agentic Systems
Optimization Strategies
Dynamic Resource Allocation
Route simple tasks to edge, complex reasoning to cloud
Context Compression
Intelligent memory management to reduce token overhead
Speculative Execution
Pre-compute likely next steps while current ones execute
Budget-Aware Reasoning
Dynamic quality-cost trade-offs based on inference budgets