Dataplanedataplane
    Agent Harnesses

    Get more from your agents by asking less of the model

    The harness is where you handle the predictable work and see what the agent actually did. High quality harnesses let agents run cheaper, stay observable, and leave the model free for what only it can do.

    The Problem

    When the model does everything, it costs more and shows less.

    It's tempting to push everything through the model: checking access, maintaining state, enforcing rules. But that's the work it's worst at. Each task burns tokens, drifts in accuracy, and leaves no trace of what happened. The harness is where that work belongs and where the reliability of every agentic system comes from.

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    The model does work code should. Rules, permissions, and formatting run as expensive, error-prone generation instead of plain code.

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    Behavior is a black box. Without harness-level logging, you can't see what context an agent has or why it answered as it did.

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    Nothing closes the loop. Outcomes aren't captured, so the system never learns from what worked and what didn't.

    Dataplane Approach

    From a do-everything prompt to a workflow you can trust.

    01

    Define hooks and gates

    Wrap the model in permission checks, validation gates, and hooks that enforce your rules as code, not hope.

    permissions · gates · validate
    02

    Inject context

    Pull the exact data and state each step needs into context, instead of making the model fetch and remember it.

    retrieve · state · serve
    03

    Close the loop

    Log every call, then route outcomes and feedback back to refine tools, context, and the ontology.

    observe · feedback · refine
    Built on:Data OntologyData LineageData QualityMaster Data

    Outcomes

    Where You Feel It

    A real harness makes agents cheaper to run and far easier to trust.

    Accuracy

    Gates and validation catch errors the model would otherwise wave through.

    Cost

    Predictable work runs as code, so you stop paying the model to do it.

    Latency

    Offloading lookups and checks keeps the model's job small and fast.

    Iteration speed

    Logged outcomes show what's failing, so every run feeds the next fix.

    Benchmark your agents with a real harness.

    We'll rebuild a slice of your workflow with proper hooks, gates, and logging, and run it both ways, so you can see the lift in cost, accuracy, and visibility for yourself.