Agents can’t be tested like traditional code. Real-world interactions are dynamic, multi-turn, contextual, and unpredictable.
Most existing solutions rely on static datasets or LLM-as-a-judge approaches that don’t scale, lack consistency, and rarely reflect real production complexity. Manual dataset collection is slow and almost never results in true production readiness.
At Plurai, we build high-fidelity synthetic datasets for you, tailored to your product, personas, and edge cases. These simulations include complex multi-turn scenarios and authentic artifacts such as emails, documents, and images.
We group evaluations into structured, runnable experiments, so you can consistently test new versions, measure regressions, and validate improvements before release.
The result: your agent is production-ready before it ever meets a real user, with CI/CD integration, continuous regression testing, and an optimization loop that keeps enriching your datasets as your product evolves.
Plurai integrates directly with your agent as a black box, interacting with it exactly like a real user would. We execute structured simulation scenarios and run relevant evaluations on specific turns or across full multi-turn sessions.
We can also integrate with your RAG pipeline and underlying databases to test grounding, retrieval quality, and other RAG-specific behaviors. Our simulation engine can ingest documents such as PRDs, policies, requirements, and past conversation samples to expand domain knowledge and increase scenario depth and realism.
Additionally, we can mock selected tools to fully control and stress specific flows within a scenario.
Our simulation engine is designed to adapt to your specific product and business use case. We own the customization process end to end, ensuring a smooth integration and a setup tailored precisely to your environment.
You can build a basic simulation framework in-house if your goal is limited coverage. But building a production-grade simulation system is far more complex than it initially appears.
Creating high-fidelity synthetic datasets, diverse and realistic personas, challenging edge cases, authentic artifacts, multi-turn consistency, and reliable evaluation logic requires significant time, iteration, and specialized expertise. Getting to a point where simulations truly reflect real production complexity, and not just “happy path” flows, typically takes much longer than teams anticipate.
If the cost of failure is low, internal tooling may be sufficient. But if diversity, depth, and production readiness matter, and the cost of error is high, it’s usually better to rely on a purpose-built platform designed specifically for this level of rigor and scale.
No. Plurai can work with whatever you already have, even if it’s minimal or unstructured.
We don’t require large historical datasets. Our system can generate high-quality synthetic data tailored to your use case, expand sparse inputs into diverse scenarios, and build meaningful evaluations from scratch. Whether you have thousands of conversations or just a PRD and a few examples, we can get you to production-grade coverage effectively.
Plurai supports a wide range of AI agents and agentic workflows, not just chatbots. Whether your agent handles customer conversations, internal copilots, RAG-based assistants, multi-step workflows, or tool-using agents, our framework can adapt to it.
If you have a specific use case in mind, we’re happy to discuss it and tailor the setup to your workflow and architecture.
No. Plurai is designed to work with your existing stack.
We integrate with a wide range of architectures, frameworks, and infrastructure setups, and customize the integration to fit your environment. As long as there’s a way to run your agent and communicate with it, which you already have, we can plug into it without requiring you to re-architect your system.
Plurai provides a full platform experience, including an SDK, CLI, and user interface for dataset and scenario generation, exploration, experiment management, and results analysis. You can run structured experiments, review detailed reports, analyze sessions visually turn by turn, and receive actionable fix suggestions before deployment.
The entire solution is deployed within your VPC, ensuring maximum security, data control, and compliance with your infrastructure requirements. It can also be connected directly to your CI/CD pipelines to enable automated regression testing and continuous validation with every release.
Both chat agents (support bots, assistants, copilots) and task or workflow agents (classifiers, extractors, triage, report generators). It works with Python agents built on LangGraph, CrewAI, the OpenAI Agents SDK or LangChain, with plain Python, and with TypeScript/JavaScript agents on Node.
No. If you can run a command in your coding agent, you can use Plurai. A single /simulate command runs the whole flow on its own: it maps your agent, builds the digital twin, generates scenarios and evals, runs them, and proposes fixes. It only stops to ask what it can’t decide for you. You don’t need to write test cases, design evals or learn any commands. To change something, just ask in plain language, for example "add a scenario where the customer is angry" or "mock the CRM". The results come as a report written for engineers, not researchers.
Almost none. One command does it all. Plurai reads your agent’s code, maps every tool and dependency, decides what to mock, and generates the harness that runs your agent, all automatically. You don’t refactor anything, add decorators or touch your agent’s source.
No. Every write to your systems is mocked by default, so your agent can act freely without affecting real customers or data. A live write happens only if you explicitly ask for one.
A digital twin is a mocked copy of the systems your agent calls, like your CRM, database or payment service. It’s seeded with a realistic world of records and state, and it changes as your agent acts on it, just like the real thing. Plurai mocks these systems for three reasons:
You need a coding agent (Claude Code, GitHub Copilot CLI or Codex CLI, with Cursor coming soon) and a browser to sign in once. Install the plugin, run /simulate on your agent, and the plugin handles the rest. There’s nothing to install by hand and no credentials to save.
No. Scenarios and evals are generated automatically for your agent’s domain. If you’d rather be hands-on, you can work interactively: tell it what you want in chat, for example "add a scenario where the user changes their mind", and the suite updates.
Plurai learns it from your agent itself, in two ways:
If you have production traces, they add a third signal: the real failures and behavior your users have already hit.
Yes. Plurai builds everything from your agent’s code: the digital twin, the scenarios with their simulated users or task inputs, and the evals. It doesn’t need real traffic, so you can test an agent before its first user ever sees it. If you have production traces later, you can connect them to ground the suite in real behavior, but that’s optional.
Yes. Connect LangSmith, Langfuse, Arize or Braintrust, or use a saved trace export. Real failures become scenarios that resume at the exact turn where they broke, so you can reproduce them reliably.
The final run plays every scenario three times. Each scenario is then labeled consistent pass, consistent fail or inconsistent, so you know where to start.
Yes, if you choose. Each gap comes with a diagnosis that points to the code causing it and a proposed fix with evidence. Pick the fixes you want, and Plurai applies them one at a time, showing you each change. It then re-runs the scenarios that exposed each gap to verify the fix held, and marks it fixed or still failing. Nothing is called fixed until it’s verified, and nothing touches your code without your approval. A gap you confirm can be kept as a regression test, so it gets caught if it comes back.
Your agent runs locally, on your machine, and its code and credentials stay there. Only the mocked systems run remotely, in an isolated sandbox dedicated to you. If you turn on anonymization, personal data is scrubbed on your machine before anything is uploaded. Every upload is covered by your Data Processing Agreement (DPA) and recorded in a local audit log.
Yes. You can run the simulation where nobody is there to answer questions, in a CI job or a headless coding-agent session. You declare the session unattended and answer the few questions that need you in advance. Every other question takes a safe default.
Yes. Each agent gets its own suite, saved as a named environment you can switch between without regenerating anything. You can also test a single sub-agent, tool or step on its own, with everything around it mocked.
Post-training any open model into a task-specific model from a task description or a few examples. Plurai generates the training data, post-trains the model, benchmarks it against the frontier model you use today, and serves it. The whole pipeline takes about 20 minutes, with no labelled data required.
Any high-volume task with a clear job: pulling structured fields out of documents or conversations, classifying tickets, routing requests, detecting intent, tagging and scoring content, summarizing to a fixed format, or turning free text into JSON. If your task is well defined and runs thousands of times a day, it is a candidate. Not sure? Talk to us and we’ll tell you whether post-training fits.
Look at what your agent calls a frontier model for on every turn. The router that decides which skill handles the request, the classifier that tags intent, the extractor that reads the ticket, the step that turns a reply into structured fields, the check that scores the answer before it goes out. Each of those is a post-training candidate: swap the frontier call for a model trained on that one job, and the agent gets faster and cheaper without changing how it behaves.
According to our benchmarks, post-trained open models match or beat the frontier models they replace. In one enterprise workload, a post-trained Nemotron 3.5 Lightning scored 88.8 on real production conversations versus 78.4 for Claude Haiku-5.5, and matched GPT-6 Luna (88.6). On the held-out set it led both by 10 to 18 points.
It depends on the workload. In the same benchmark, the post-trained model ran at $0.0078 per 1,000 calls, 44x lower than Claude Haiku-5.5 and 40x lower than GPT-6 Luna. A different base model on the same task came in at 5x lower.
Yes. Plurai generates the training set from your task description, using its published method for synthetic training data (BARRED, ICML 2026). Your own examples, if you have any, calibrate the data further.
Through the Plurai web UI at app.plurai.ai or the Plurai Claude Code plugin. Either way, you describe the task, review the generated test set, and get a post-trained endpoint with its benchmark. If your organization needs it, the same workflow runs on-prem or in your VPC.
On Plurai’s managed infrastructure, with production-grade serving, dedicated endpoints and zero-retention inference, or in your own VPC or on-prem.
Every run includes a benchmark against your current production model and the frontier models you choose, scored on your real production data and a larger held-out set. You see the numbers before anything changes in your pipeline.
Re-run. Post-training starts from the task description, so a change to the task is a change to the description and a new 20-minute run, benchmarked against the previous model.
Many. Activated LoRA, token compression and smart decoding let task-specific models share a single GPU. In Plurai’s published benchmark, 100 adapters evaluated a 5,000-token conversation in 285 ms on one GPU, 32x faster than standard LoRA.
Yes, and they are two of the most common tasks. An eval model scores agent outputs for grounding, policy compliance, task completion and similar criteria; a guardrail model enforces them in real time, under 100 ms. Both are post-trained the same way as any other task.
Keep the frontier model for open-ended reasoning and move the high-volume, well-defined workloads to post-trained models you own. Most teams run both.
The post-trained model is served on a dedicated endpoint with the same input and output contract as the model it replaces. In the benchmark, the customer’s pipeline did not change.
Go to app.plurai.ai or install the Claude Code plugin, describe the task, and review the generated test set. Your post-trained endpoint and benchmark are ready in about 20 minutes. For an enterprise workload, book a demo and we’ll run it on your data.