Platform / RiGi Group
AI Harness
Bind model interactions into a defined runtime with shared controls and observable behavior.
WWAIOS / AI Harness
The role of ai harness in a governed workflow
Bind model interactions into a defined runtime with shared controls and observable behavior.
01 / 09
Capabilities
What the layer is designed to handle
Carry tenant, task, and authority context into each run.
Capture the inputs and outputs needed for evaluation and review.
Operating pattern
A control path you can inspect
- 01Define the input, context, and authorized purpose.
- 02Adapt model endpoints behind a consistent execution interface.
- 03Carry tenant, task, and authority context into each run.
- 04Capture the result, exception, and owner for review.
Questions for architecture review
What would change your decision?
Ask which evidence is current, which control actually executes, who owns an exception, and where a human must approve an action.
Explore
Adjacent layers
AI Decision EngineDescribe decision paths, policy conditions, and approval points as explicit workflow rules.↗AI GuardrailsApply runtime checks around sensitive data, unsafe requests, and output constraints.↗AI Knowledge BaseConnect retrieval to governed sources so answers can point back to what supports them.↗AI GovernanceManage policies, model inventory, ownership, and review throughout the AI lifecycle.↗
A closer look / AI Harness
From question to evidence to decision.
Use this framework to discuss the actual workflow and the proof needed to move forward.
01 / Situation
Each model provider exposes different interfaces, failure patterns, and telemetry.
02 / Approach
Define one task contract with tenant context, allowed models, input shape, output shape, timeout, and failure treatment; adapt providers to it.
03 / Evidence
Compare provider behavior on the same task corpus, preserving exact configuration and trace identifiers.
04 / Next decision
Route a real but bounded workload only after adapter behavior and failure handling are tested.