Building an Agentic AI System: Model Selection and Cost Optimization

What is agentic AI?

Agentic AI systems use LLMs to:

• Take actions: Make API calls, write code, interact with systems
• Use tools: Call functions, access databases, search the web
• Complete tasks: Break down complex tasks into steps
• Learn from feedback: Adjust behavior based on results

Key cost factors for agentic systems

Agentic systems have unique cost considerations:

• Multi-step workflows: Each task requires multiple LLM calls
• Tool use: Function calling adds overhead
• Reasoning: Complex reasoning requires more tokens
• Error handling: Failed steps may need retries

Cost estimation example

Let's estimate costs for a typical agentic workload:

Note: This is a simplified estimate. Actual costs may vary based on task complexity, model choice, and error rates.

Model selection for agentic systems

What to look for

• Tool use: Support for function calling
• Reasoning: Strong reasoning capabilities
• Reliability: Consistent, accurate outputs
• Cost: Balance quality with cost

Top agentic models by cost

Cost optimization tips

• Use smaller models: For less complex steps
• Cache results: Avoid re-computing unchanged data
• Batch processing: Process multiple tasks together
• Error handling: Implement retries and fallbacks
• Monitor usage: Track token usage to optimize costs

Architecture patterns

Simple agentic pipeline

For most applications, a simple agentic pipeline works well: plan, execute, observe, and iterate.

Advanced agentic pipeline

For complex applications, consider: multi-agent systems, human-in-the-loop, and hierarchical planning.

Compare agentic models

Ready to compare agentic models side by side? Use our tools: