OyaAI
OyaAI provides a deterministic runtime platform for operational AI agents, ensuring reliable, repeatable execution by keeping most compute off the LLM. The platform preserves critical data byte-for-byte, reduces token usage by 4.7x, and records every run for full auditability. It's designed for businesses running agents on behalf of customers, such as SDRs or support bots.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Software Only
Funding
Founders
Product
Problem
Most AI agents are non-deterministic prompt wrappers around an LLM, leading to silent state corruption, ordering drift, and excessive token costs in production. These failures—where critical values are altered or steps run out of sequence—make agents unreliable for real operational work and difficult to audit.
Solution
OyaAI is a runtime platform for OAgents, which are agents a business runs for someone else, like an SDR or support agent. The platform uses a planner that emits a typed plan, which a deterministic runtime executes, passing values by reference so the model only reads what it needs. This architecture keeps roughly 80% of compute deterministic, reducing token usage by ~4.7x and ensuring critical values are preserved byte-for-byte. Every run is fully recorded and replayable, providing a complete trace of actions for auditability and customer accountability.
Target Audience
Primary customers are businesses that deploy AI agents on behalf of their own customers, such as sales development, customer support, and onboarding teams, who need reliable, auditable, and cost-effective automation.
Features
- Deterministic runtime that executes a typed dataflow plan, preventing state corruption and ordering drift.
- Skills are versioned, sandboxed Python integrations with typed input/output schemas, reusable across agents.
- Secrets and sensitive data are kept in the deterministic layer, never reaching the model, preventing prompt injection.
- Full execution trace, including LLM calls, tool calls, and sandbox output, is recorded and replayable step-by-step.
- One agent identity works across multiple channels, including web chat, Slack, Telegram, webhooks, and scheduled jobs.
- Benchmarks show ~4.7x fewer LLM tokens per task and 100% critical value preservation versus ReAct loops.