Primary product / Agent_01

Fixed cost
AI Harness.

Agent_01 runs agentic workflows with local LLMs on your own hardware. It understands the machine, finds the right balance of quality and speed, tests the outcome, and optimizes the next run.

Put Agent_01 to work See how it works
LOCAL FIRSTHARDWARE AWAREMODEL AGNOSTICSELF-OPTIMIZING
AGENT_01 / AUTO CONFIG SYSTEM ACTIVE
RUNTIME LOCAL BALANCE QUALITY / SPEED RESULT TESTED

01 / THREE CORE ADVANTAGES

Built for the machine.
Accountable to the result.

Agent_01 turns the hardware you already control into an adaptive agentic runtime.
01 / LOCAL RUNTIME

Local LLM. Your hardware.

Run agentic workflows with local models on your workstation, server, or private infrastructure. Keep execution close to your data and inside the boundary you control.

02 / HARDWARE-AWARE

The right model for the machine.

Agent_01 inspects available compute and configures the model, quantization, context, and runtime settings that best balance quality and speed for the job.

03 / SELF-OPTIMIZING

Success becomes a signal.

Agent_01 tests its completed work against your success criteria, tracks the measured success rate, and retunes the harness to improve future runs.

02 / ADAPTIVE MODEL STRATEGY

Quality vs. speed
is a live decision.

The best local model is not one fixed answer. It depends on the available hardware, task complexity, latency target, context size, and proof required. Agent_01 evaluates those conditions and configures the runtime for the work in front of it.

MODEL PROFILE / AUTORECALCULATING
FASTER HIGHER QUALITY
AVAILABLE COMPUTEINSPECTED
WORKFLOW COMPLEXITYMEASURED
RESPONSE TARGETAPPLIED
SUCCESS CRITERIABOUND
SELECTED PROFILEBALANCED / LOCALCONFIGURATION READY

03 / CLOSED-LOOP EXECUTION

Configure. Run.
Prove. Improve.

  1. 01

    Inspect

    Read the available CPU, memory, accelerators, storage, and runtime constraints.

  2. 02

    Configure

    Choose a local model and settings for the quality-speed target the workflow requires.

  3. 03

    Execute

    Load context, use approved tools, and run the workflow inside explicit boundaries.

  4. 04

    Test

    Evaluate the result against objective checks and workflow-specific success criteria.

  5. 05

    Optimize

    Use measured results to improve the next configuration and execution strategy.

Optimization follows the success criteria you define. Agent_01 measures observable outcomes instead of guessing what “better” means.

LOCAL BY DESIGN

Your hardware remains
your control plane.

Choose where Agent_01 runs, which models it can load, what data it can see, and which tools it can use.

When a workflow needs specialist agents or remote handoffs, Agent_01 can communicate through Molten Hub—without making Hub the center of execution.

AGENT_01 / READY FOR WORK

Put your hardware
to work.

Run a local, self-configuring AI harness built to measure what succeeds and optimize what happens next.
Start with Agent_01 Discuss a custom deployment