Agent Workflows
Build autonomous AI agents that plan, execute, and adapt multi-step tasks
Design AI agents that can plan complex tasks, use tools, browse the web, execute code, and adapt their approach based on intermediate results. Requires strong reasoning, function calling, and reliability.
The job to be done
Build agents that can decompose complex tasks, call external tools and APIs, process intermediate results, handle errors gracefully, and produce a final deliverable — all with minimal human intervention.
Key tradeoffs
Reasoning quality is paramount — weak reasoning leads to cascading errors in multi-step workflows. Function calling reliability determines tool-use success. Cost compounds across steps (5-20 LLM calls per task).
When to switch models
Use a frontier reasoning model for the planner/orchestrator. Use faster, cheaper models for individual tool calls and simple transformations within the workflow.
Recommended models
Related guides
Frequently asked questions
Which models support function calling?
Most frontier and mid-tier models support function calling. Claude, GPT-5.4, and Gemini Pro all have robust function calling. Open-weight models vary — check the model detail page.
How do I handle agent errors?
Implement retry logic with exponential backoff, fallback to simpler approaches, and set maximum step limits. Log each step for debugging.
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