Skip to main content

Overview

Voice and chat agents don’t crash — they fail quietly. A voice assistant can give the wrong policy, drift off task, or miss a step without triggering any exception. Traditional tracing and observability don’t capture what actually happened in the conversation.

Okareo is built agent-first: you simulate and evaluate real agent sessions (simulated callers/chat vs your agent), monitor live behavior, and run multi-turn tests so you know how your agents perform before and after production. The same platform supports voice pipelines, chat copilot, function-calling agents, agent meshes, multi-turn dialogs, and RAG pipelines — with behavior-level visibility, real-time detection, and scenario-based evaluations across edge cases, workflows, and user roles.

Move beyond code traces — ship voice and agent behaviors with confidence.


Okareo DiagramOkareo Diagram

Voice & Simulation​

Run voice-first, multi-turn simulations against your own voice agent. Okareo orchestrates full voice sessions — turn-by-turn spoken conversations between a simulated caller and your agent — so you can test and evaluate real conversational behavior end-to-end. Use the same Target → Driver → Scenario flow as other Okareo simulations, tailored for voice: configure your voice target (e.g. OpenAI Realtime, Deepgram), define a simulated caller (Driver) with personas and objectives, and run scenarios with checks that score task completion, policy adherence, and more.

Voice & Simulation helps you:

  • Test real voice conversations before users do — no manual calling required
  • Stress-test with configurable caller personas (e.g. frustrated customer, edge-case requests)
  • Evaluate with built-in and custom checks on full conversation transcripts
  • Run against OpenAI Realtime, Deepgram, or your own voice backend via custom endpoints

Real-Time Monitoring​

Agents and LLMs fail silently — your code runs fine, but your agent misfires. You don’t need another tracing tool — we track LLM behavior. Catch failures as they happen— scope violations, wrong tools, hallucinations, broken flows. Real-time detection maps where errors start, how they spread, and when they break trust.

Real-Time Monitoring helps you detect:

  • Unauthorized model output that flows past traditional observability
  • Broken agent decisions that tracing won't find
  • LLM workflows going off the rails that erode user trust before you notice

Agentic Evaluation​

Test your agents’ planning, memory, and decision-making, step-by-step. LLM agents don’t just generate text — they plan, call functions, and adapt. But when they go off-script, traditional evals can’t explain why. Okareo lets you simulate complex agent flows, test how they plan and remember, and catch decision-making flaws before users do.

Agentic Evaluation helps diagnose:

  • Agents using the wrong tools or failing to recover from function call errors
  • Agents forgetting key details from earlier turns, breaking task flow
  • Conflicting actions that cause the agents to stall
  • Tasks failing when agents act on incomplete or missing data from prior steps

RAG Evaluations​

Validate intent detection, retrieval, and generation end-to-end. RAG systems break at any step — misclassified intent, poor retrieval, or hallucinated answers. Okareo tests each stage of your RAG pipeline with real metrics, so you can trust the full flow from query to answer.

RAG Evaluations help prevent:

  • Queries being misrouted due to incorrect intent classification
  • Poor document retrieval leading to bad LLM answers
  • No measurable visibility of retrieval quality - recall and precision unknown
  • Hallucinated answers caused by missing source content

Synthetic Data & Scenario Copilot​

Generate test scenarios before real users break things. Real-world coverage is impossible with hand-crafted prompts. Okareo’s Scenario Copilot creates rich, diverse, edge-case scenarios—before failure hits production. Expand your test set with realistic data, fast, and power your simulations with synthetic inputs that expose hidden flaws. Use real examples of production failures to expand safety nets and catch similar issues early.

Synthetic Data & Scenario Copilot helps address:

  • Hand-written tests missing real-world edge cases
  • New features ship with no coverage or examples
  • Lack of edge case and stress testing leaves systems unproven under pressure
  • Generating test data manually is slow and incomplete