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Choose its intelligence

Tune models job by job

Use a deliberate model choice for every agent instead of forcing one workspace-wide default.

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Pin a model independently for each agent.

What this changes for your team.

Model selection belongs to the agent setup and is published with it. A high-reasoning agent and a fast classification agent can therefore use different routes while remaining independently testable and auditable.

How it works in practice.

  1. 01

    Open an agent's model setting and select an enabled route.

  2. 02

    Test that route with the agent's own tools, knowledge, and output requirements.

  3. 03

    Publish the draft so new sessions pin the updated setup version.

What you can plan around.

The behaviour you can design against, stated concretely.

The selected model is stored in the agent setup rather than resolved from a mutable global default each turn.

Published session records retain the agent version used for the model decision.

Changing one agent's model does not rewrite another agent's configuration.

Complete the operating path

View all capabilities
Choose its intelligenceRun on your provider account

Keep model usage on the provider relationship your organization already controls.

Choose its intelligenceChoose a model that fits the job

Match each job to the provider, model support, latency, and cost profile your team prefers.

Build the agentBuild agents with AI

Turn a working brief into a reviewable agent draft without starting from a blank setup.

Bring one real process

See how Yekar.AI fits the way you work.

Start with a job your team already owns, plus the tools and decisions around it.

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