Stop Sending Every Agent Task to the Same Model

Using one model for every agent task is convenient.

It is also usually wrong.

Agent workflows contain different kinds of work. Classification is not deep reasoning. Retrieval cleanup is not policy review. A final customer answer is not the same thing as a tool plan.

If you route all of that to one model, you pay too much for easy tasks and under-spec the hard ones.

Route by job#

A real workflow should name the task before picking the model.

Good routes:

• Intent classification
• Tool selection
• Evidence synthesis
• Code reasoning
• Policy review
• Final answer writing
• Eval judging
• Summarization

Each route should have a preferred model, fallback model, reason, and cost expectation.

Cheap model where it belongs#

Use fast cheap models for:

• Is this billing or product?
• Which tool should run first?
• Which specialist agent should take over?
• Extract the customer ID from this thread.
• Summarize these five events.

These tasks need consistency more than genius.

Strong model where it matters#

Use stronger reasoning models for:

• Multi-source incident analysis.
• Code review.
• Architecture risk.
• Complex customer edge cases.
• Planning a risky action.
• Resolving conflicting evidence.

This is where model quality changes the result.

Policy route gets its own lane#

Policy decisions should not be buried inside the same call that writes the answer.

Create a separate route for:

• Does this require approval?
• Is this user allowed to access this data?
• Is this tool call high risk?
• Does this response expose private data?
• Is this action reversible?

That route can use a model and prompt tuned for structured judgment.

BYOK makes routing visible#

If users bring their own OpenAI, Anthropic, Gemini, or OpenRouter keys, the workflow should make provider choices visible.

The user should be able to see:

• This route uses this provider.
• This model handles this task.
• This fallback runs if the first model fails.
• This key is required.

Hidden model routing is fine for a toy. For production, it should be inspectable.

What to log#

Every route should log:

• Task
• Provider
• Model
• Token usage
• Latency
• Error
• Fallback used
• Output validation result

This is how you debug cost and quality later.

Build it in Codelit#

Try this:

Build an agent workflow with model routing for classification, tool planning, evidence synthesis, policy review, final response, and eval judging. Include OpenAI, Anthropic, Gemini, and OpenRouter BYOK support with fallbacks.

Design model routing in Codelit

Good routing is not over-engineering. It is how agents stop being expensive blobs.