Answers, fast.
Real humans on the other end.

A typical AI agent is a single LLM doing three jobs in one prompt: thinking, deciding what to do, and doing it. Oya splits those into separate layers. (1) An orchestration LLM reasons about your request and picks which skill to run — it never touches your data directly and never executes actions. (2) A skill-interpreter LLM translates the request into a typed tool call, constrained by a schema so it can't invent values. (3) The skill itself runs as deterministic code in an isolated sandbox — real APIs, real responses, captured in your audit trail. A whole class of "the AI claimed it did X but actually didn't" errors becomes structurally impossible because the LLM doing the thinking is not the layer doing the doing.

Even with reasoning and execution separated, the orchestration LLM still chooses tools and frames language. It can pick the wrong skill if your system prompt or skill descriptions are ambiguous. It can summarize a tool result inaccurately when explaining what it did. And it has the usual LLM weaknesses for recent events, niche domains, and anything outside its training data. Separation kills a category of errors; it doesn't kill all of them.

Four high-leverage moves, in roughly this order of impact: 1. Ground answers in the knowledge base. Upload your source documents (PDFs, CSVs, URLs) and the agent retrieves exact passages — no LLM hop in the lookup. 2. Use the fact-check skill before sending. For any output going to a human or external system, have the agent call fact_check on its draft, passing the raw tool outputs as evidence. Returns {passed, unsupported_claims}; the agent rewrites and re-checks if it fails. Costs about $0.0005 per check. 3. Insert approval gates in your routines. For high-stakes actions (money movement, public posts, destructive operations), pause the routine for human confirmation in Slack or Discord before executing. 4. Tighten your skill schemas. Each skill defines a typed tool schema; enum fields can only hold listed values, integer fields can't hold strings. This kills a whole class of hallucinations at the API boundary.

When the cost of being wrong is high: money movement, public posts, destructive actions (deletion, kicking, canceling), legal/medical/safety/financial advice, recent events or post-training-data info, or anything that touches customers directly. Treat AI output as a draft for these cases — Oya's audit trail makes it cheap to verify, but you still have to do it.

Not automatically — verification across every conceivable workflow isn't a feature any platform can guarantee. What we give you is a workflow: • Full audit trail: every run records which model was called, what prompt it received, which skills fired, every input and output, and the cost. You can replay any run and check what actually happened vs. what was claimed. • fact-check skill: a separate model that compares claims in a draft against the raw evidence the agent gathered. • Approval gates: pause execution before any irreversible step. • Knowledge base grounding: retrieval over your trusted sources surfaced as exact snippets. • Typed skill schemas: structural blocks at the skill boundary. Verification is a workflow you compose from these building blocks; we don't hide it inside a black box.