2. Coding Agent (Claude Code-style)

Reads codebases, writes code, runs tests, fixes bugs. Complex multi-step workflows.

3. Research Agent

Searches the web, reads documents, synthesizes findings. Long context, detailed outputs.

4. Data Processing Agent

Extracts data from documents, classifies text, generates summaries. Repetitive, high-volume.

The Multi-Model Strategy

The smartest agent builders don't use one model for everything. They route tasks to the cheapest model that can handle them:

• Simple classification/routing: Gemini 2.5 Flash-Lite ($0.075/$0.30) — under $2/mo
• Medium complexity: GPT-4o mini or DeepSeek V4 Flash — $5-15/mo
• Complex reasoning: Claude Sonnet 4.6 or GPT-5 — $80-150/mo
• Critical decisions: Claude Opus 4.7 — only when quality matters most

The best AI agents aren't the ones using the most expensive model. They're the ones that know when to use a cheap model and when to upgrade.

Hidden Costs Developers Forget

• Retries and error handling: Budget 10-20% extra for failed requests
• Context building: Each step sends previous context, so token counts grow with each step
• Tool use overhead: Function calling adds tokens for tool definitions and results
• Long context fees: Some providers charge more for inputs over 128K tokens
• Storage: If you're caching responses or storing conversation history

Real Monthly Budgets

— See if you're overpaying for AI APIs

How to Cut Agent Costs by 60%

1. Start with the cheapest model that works. Most tasks don't need GPT-5. Start with Flash-tier models.
2. Implement prompt caching. Send the same system prompt repeatedly? Cache it. Up to 90% savings on input tokens.
3. Use batch processing. Non-urgent tasks can use batch APIs at 50% discount.
4. Optimize your prompts. Remove unnecessary context. A 30% smaller prompt = 30% lower input cost.
5. Set token limits. Don't let the model generate 3,000 words when 500 will do.
6. Monitor and alert. Set up cost alerts so you catch runaway agents before the bill arrives.