Multi-LLM Agent Orchestration: Choosing the Right Model for Each Task

Multi-LLM Agent Orchestration

Running all AI agents on a single model is like using the same tool for every job. Different AI models have different strengths, and intelligent orchestration — selecting the right model for each task — produces better outputs at lower cost.

Model Strengths in Enterprise AI

GPT-4o

• Excellent at complex reasoning, code generation and structured outputs
• Strong JSON mode and function calling for tool use
• Best for: research synthesis, code review, complex analysis

Claude 3.5 Sonnet

• Outstanding at long-form writing, nuanced analysis and following instructions precisely
• Excellent at processing long documents
• Best for: report writing, compliance analysis, email drafting

GPT-4o Mini

• Fast, cost-efficient and capable for simpler tasks
• Best for: ticket triage, simple classification, quick Q&A

Llama 3.1 70B

• Open-source option for on-premise or air-gapped deployments
• Best for: environments with strict data residency requirements

Gemini 1.5 Pro

• Very long context window (1M tokens)
• Best for: processing large codebases, extensive document analysis

AzelaAIOS Auto Mode

Rather than requiring you to choose a model for every agent and workflow step, AzelaAIOS Auto mode automatically routes each task to the most appropriate model based on:

• Task complexity — simple tasks go to Mini, complex ones to full models
• Output format — structured JSON tasks prefer GPT-4o; prose tasks prefer Claude
• Cost budget — respects per-workspace token spend limits
• Latency requirement — time-sensitive steps use faster models

Practical Orchestration Patterns

Pattern 1: Parallel Analysis

Run the same prompt across two models simultaneously and use the one that returns first or produces a higher-confidence output.

Pattern 2: Cascade

Try a cheap fast model first. If confidence is below threshold, escalate to a more powerful (and expensive) model.

Pattern 3: Specialist Routing

Different steps in a workflow use different models: data extraction with GPT-4o Mini, analysis with Claude, final writing with Claude 3.5 Sonnet.

Managing Multi-Model Costs

In AzelaAIOS, every agent run logs the models used, token counts and estimated cost. The Monitoring dashboard shows cost breakdowns by agent, workflow and team — enabling accurate cost allocation and budget optimisation.

Start Multi-Model Orchestration →