Activate relevant expertise.
Select a focused group of specialists for the task, instead of calling the entire agent pool.
A NEW DIRECTION FOR MULTI-AGENT AI
Less noise.
More intelligence.
Make every agent call count. LLM Booster explores a smarter path through your AI workflow—with focused agents and less repeated context.
Early-stage prototype • Built for multi-agent teams
THE FOCUSED WORKFLOW01 / ROUTING
↳ One task. The right expertise.
LLM BOOSTERFocus the flow.
▦DeviceSelected ●
◈BillingSelected ●
◎Other agentsStandby
Less broadcast. More focus.ILLUSTRATIVE FLOW
EARLY SIGNALS
Small experiment.
Meaningful direction.
50%
fewer model calls
4 → 2 · recorded local-model run66.26%
less counted text
412 → 139 lexical units · same runPURPOSEFUL BY DESIGN
More agents shouldn’t
mean more unnecessary work.
When every specialist receives every message, repetition adds up. LLM Booster explores a more selective way to put your agents to work.
Give context a purpose.
Explore smaller, role-specific context to reduce the repeated information passed between agents.
Find your opportunity.
Compare communication overhead against a baseline, then evaluate the quality tradeoff in your own workflow.
FROM BROADCAST TO FOCUS
One customer issue.
Two relevant specialists.
A failed eSIM activation after a plan change involves both device setup and billing. In the prototype’s example, LLM Booster selects those two specialists from a four-agent pool.
Explore the recorded selection below. This illustration does not run an AI model or establish that the issue was resolved.
CUSTOMER REQUEST
“My eSIM won’t activate after switching my SIM and billing plan.”
▦ Device◈ Billing◎ Support◇ Fraud
2 specialists selectedRole-specific, reduced context
EVIDENCE, NOT HYPE
Promising results.
Clear boundaries.
Early experiments show an opportunity to reduce communication overhead. Here is what was tested—and what the numbers mean.
Half the calls.
A more focused workflow.
4 → 2 model calls
Counted input + output412 → 139 lexical units
Recorded elapsed time45.28s → 12.30s
One four-agent support scenario using a local model. Lexical units are counted text, not provider-reported tokens. Timing comes from a single sequential run; it is not a general speed claim.
A larger pool.
A selective response.
100 → 3 emulated calls
Broadcast baseline100
Focused selection3
Deterministic simulation with three agents selected from a pool of 100. This is an emulated comparison, not a test of 100 live model agents or a measured customer bill.
Production cost savings and preserved answer quality have not yet been established. Your workload, baseline and quality requirements determine the opportunity.
BUILD WITH INTENTION
Your agents have specialties.
Your workflow should too.
Building an AI product with distinct specialist roles? Start with the workflow where repeated context and broad agent calls are easiest to measure.
Plan your evaluation ↗AI product teams
Explore communication overhead as your agent pool grows.
Customer support workflows
Bring the right domain specialists into each request.
Operational assistants
Investigate focused context across distinct business roles.
A LITTLE MORE CLARITY
Good questions.
Straight answers.
What is LLM Booster today?
An early Python prototype for agent selection and context-reduction experiments, with a local-model integration path and reproducible synthetic benchmarks. It is not yet a production hosted service.
Does it replace my models or orchestration framework?
No. The concept is an optimization layer before model calls. Dedicated framework adapters remain planned; native integrations are not available today.
Can I expect the same savings?
The results are specific to the tested scenarios. Real costs depend on actual model usage, your existing workflow and task-quality requirements. No production cost reduction is guaranteed.
Will the answers retain their quality?
Preserved answer quality has not yet been established. Context reduction can remove useful information, so a meaningful evaluation must measure task success alongside communication overhead.
What would an evaluation involve?
Choose a representative workflow, agree on task-success criteria, compare against your current baseline, and measure native model usage and repeated timings. This is a proposed evaluation approach, not an available self-service product.
LESS REPETITION. MORE POSSIBILITY.
Build a leaner path
to intelligent work.
Start with your workflow. Define what success looks like.
Find out where focused orchestration could make a difference.