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Adapt it to the work

Keep long-running agents within context limits

Control how much conversation, knowledge, memory, and tool material the turn planner tries to fit.

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Budget instructions, history, knowledge, memory, and tool schemas before every model call.

What this changes for your team.

The configured planning window gives Yekar.AI a concrete budget for assembling each model turn. The planner accounts for instructions and selected context before dispatch, while the provider's actual model limit remains the final hard ceiling.

How it works in practice.

  1. 01

    Set the context budget supported by the selected model and your cost expectations.

  2. 02

    Plan space across instructions, session history, knowledge, memory, tool schemas, and the response reserve.

  3. 03

    Trim or retrieve selectively when all candidate material cannot fit the planned turn.

What you can plan around.

The behaviour you can design against, stated concretely.

Context planning happens before the provider request and uses model-token estimates.

The configured window is versioned with agent settings rather than inferred anew from browser state.

Tokenizer estimates and provider accounting can differ, so provider limit errors remain possible and visible.

Complete the operating path

View all capabilities
Adapt it to the workTest changes against real past cases

Review a changed agent beside representative recorded runs before trusting the new release.

Adapt it to the workCarry useful context forward

Let an agent retain selected memory entries and working context beyond one conversation thread.

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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