Accuracy and correctness for governed AI agent workloads.
Oya makes correctness a property of the architecture, not something you observe after the fact: agent behavior compiles to a typed plan, and a deterministic runtime executes it the same way every run. It is deployed within your virtual private cloud, integrated with your approved model endpoints, and produces a complete, replayable audit record for every execution. Data remains within your security perimeter at all times.
Designed for environments governed by SEC Rule 17a-4, HIPAA, and DORA · Available directly or through systems integration partners
Correctness by construction, not by observation.
Conventional agent architectures permit the model to improvise execution, an open loop made durable by retries and recovery, and rely on observation to detect failures after the fact. A recovered loop is a resumed loop, not a correct one. Oya separates planning from execution: agent behavior compiles to a typed dataflow plan, which a deterministic runtime then executes. Non-compliant workflows are not detected at runtime. They are structurally unrepresentable.
Deployed within your perimeter
The complete runtime operates within your cloud environment. No data path crosses your security boundary. Execution, storage, and audit records remain under your organization's control, consistent with regulatory expectations for data residency.
Your approved model endpoints
Route planning and execution to Amazon Bedrock, Azure OpenAI, or self-hosted inference under your own credentials. Oya does not meter, intermediate, or access your model consumption. Model selection remains a per-workload decision under your governance.
Examination-ready execution records
Every execution produces a deterministic, replayable record with immutable retention options. Credentials and sensitive values are structurally isolated from model context. When examiners require an account of agent behavior, the evidence exists by default.
The economics of internal platform development.
Where compliance requirements preclude hosted agent platforms, many organizations assemble internal platforms on open-source components: an execution engine, a framework, and internally developed governance. The engine may be free; the governance layer above it is not. The total cost of ownership of that approach warrants examination.
- 75% of internally developed agentic systems are projected to fail (Forrester)
- Open-source engines provide durability; policy enforcement, credential isolation, and audit evidence remain internal engineering obligations
- Each model provider update introduces regression risk across internally maintained governance code
- Typical time to first production workload exceeds 18 months
- Deterministic runtime available at deployment. The planning and execution architecture is the platform
- Approximately 5× reduction in token consumption relative to ReAct-style approaches, with the method and measurements set out in the white paper
- 100% state preservation demonstrated across six frontier models
- Engineering capacity is directed toward agent development, not platform maintenance
Recoverability is not correctness.
Durable execution engines, including those deployable within your own environment, ensure that an interrupted agent loop resumes; they do not constrain what the agent is permitted to do within it. Agent frameworks accelerate development; they do not provide governance. Oya is the correctness and governance layer: agent behavior compiles to a typed plan before execution, and every decision is recorded as evidence.
Begin with a technical evaluation.
The initial engagement is a technical demonstration conducted against your own workflow: we execute it on Oya and on your current infrastructure, compare the resulting execution records, and review state integrity across both. Your architecture team defines the evaluation criteria.
90-day structured pilot
A scoped production workload deployed within your environment, with success criteria agreed in advance. The pilot fee is credited in full against the first-year platform license, ensuring executive sponsorship and defined ownership throughout the evaluation.
Discuss Pilot Scope