LLMFly AI

AGENT USE CASES

LLM APIs for agents

Call application functions, operate browser tools, or coordinate a multi-step workflow. Start with the action boundary closest to your product.

AI agent workflow connecting model decisions to application tools
LLM APIs for agents

Production recipe

API, primary model, and failover configuration

What to measureWhat to record
Correct tool selectionResult, latency, token use, and reason for accepting or rejecting the output
Schema complianceResult, latency, token use, and reason for accepting or rejecting the output
Recovery after tool errorsResult, latency, token use, and reason for accepting or rejecting the output
Cost across the full loopResult, latency, token use, and reason for accepting or rejecting the output
01

Choose what the agent needs to do

Call application functions, operate browser tools, or coordinate a multi-step workflow. Start with the action boundary closest to your product.

  • LLM API for function calling
  • LLM API for browser agents
  • LLM API for workflow automation
02

Test tools and recovery together

Check tool selection, arguments, permissions, retries, confirmation, and the final application state. A correct-looking message is not enough if the tool action is wrong.

  • Correct tool selection
  • Schema compliance
  • Recovery after tool errors
  • Cost across the full loop
03

Measure the cost of a usable result

Include retries, tool calls, validation, escalation, and manual correction. Compare the total cost of results your application can actually accept, not token price alone.

Continue from here

LLM API for function callingLet an LLM select application functions, produce valid arguments, recover from tool errors, and stop for confirmation when required.LLM API for browser agentsBuild a browser agent that understands the current interface, chooses safe actions, recovers from changes, and verifies the final state.LLM API for workflow automationAdd LLM decisions to multi-step business workflows without losing explicit state, idempotency, approvals, or auditability.

Frequently asked questions

Which workflow should I start with?

Start with the highest-volume or highest-risk workflow for which you already have representative inputs and a clear definition of success.

How many models should I test first?

Start with the recommended model and one fallback. Add another only if neither meets the required quality, latency, or cost boundary.

What must stay the same during comparison?

Use the same inputs, context, tools, output limits, validation rules, and stopping conditions for every model.

Choose a model for this workflow

Compare a primary route and fallback using the same application inputs and validation rules.

Browse models