Plato Raises $16 Million Series A to Build Simulated Workplaces for AI Agents

Plato has raised $16 million in Series A funding to build simulated workplaces where AI models can practice using software, navigating business decisions, and completing complex jobs before they operate in live systems. Nava Ventures led the round, with participation from Gradient Ventures, Transpose Platform, and Mustafa Suleyman, CEO of Microsoft AI.

The financing brings Plato’s total funding to $19 million, following a seed round led by Gradient Ventures. Freddie Martignetti, Nava Ventures’ founder, is joining the board. Founded in 2025 and based in San Francisco, Plato will use the capital to expand its library of work environments and the experience available to AI developers.

The investment targets a practical limitation in the move from chatbots to agents: producing a convincing answer and carrying out a job are different capabilities. An agent handling a purchase, a support case, or an accounting workflow must use the right tools, reconcile information, respect constraints, and leave the underlying records in the right state.

A Place to Practice Without Changing Live Records

Training an agent inside a real business system can create consequences before the agent is ready. A purchasing task might spend money, while an administrative workflow could change an actual customer or employee record. Plato recreates those workflows in contained environments that developers can reset and reuse.

“Mastering real complex work comes from experience doing those tasks,” said Robert Farlow, Plato’s co-founder and CEO, in the announcement. The company’s simulations are intended to capture the tools, decisions, and unexpected situations that make professional work difficult.

Plato works with domain experts to recreate jobs in customer support, people operations, finance, accounting, and healthcare administration. AI labs can use the resulting environments for reinforcement learning, in which models attempt tasks and learn from feedback across repeated experience, as well as for evaluating their performance.

From Software Simulators to Verifiable Workflows

The company’s website describes Plato as an applied research lab turning real-world software and data into environments for training and evaluating agents. Its illustrative developer workflow creates a session with multiple simulators, loads a task, runs an agent, and evaluates the resulting rollout. The example combines GitHub, Jira, and Stripe simulators, showing how one task can span several tools.

That structure gives developers a repeatable unit of experience: an initial workspace, a task to complete, the agent’s actions, and checks on the result. Resetting the workspace makes it possible to attempt the same problem again without accumulating changes in a live account.

Plato’s Computer Work Val v1.0 research report, dated October 27, describes a validation set of 300 tasks spanning 10 domains and 36 roles, running across 210 simulated applications. The company says contributors turn pieces of their own jobs into environments built from software replicas and mock data.

One example asks an agent to prepare a quarterly profit-and-loss statement using simulated spreadsheet, email, payments, and banking tools. Separate verifiers check requirements such as accounting treatment, correct currency conversion, and delivery of the final file. That approach can expose a consequential mistake inside an otherwise completed workflow: a report may reach the right recipient while still overstating revenue.

The report also describes review by experts from the relevant occupation and checks for environment defects, mismatches between prompts and grading, and reward hacking. Its emphasis is on tasks that contain the information needed to succeed while still requiring reasoning across multiple sources.

Why the Grading System Matters

A simulation’s feedback shapes what a model learns. If an evaluation rewards a completed purchase without checking the budget, it may teach the agent to prioritize completion over the instructions that made the task meaningful.

Plato says it evaluates the outcome and how the model reached it, including whether it followed instructions and respected constraints. It also examines whether models exploit weaknesses in an environment to obtain a score that does not reflect correct work.

“We build evaluations that reflect what the job actually requires, so developers can distinguish a high score from work done correctly,” said Pranav Putta, Plato’s co-founder and CTO.

The company’s separate Web Clone Bench examines models’ ability to recreate web applications from reference environments. Plato describes functionality checks and human quality assurance as part of building high-fidelity replicas. For training infrastructure, fidelity is a technical requirement: missing behaviors or unrealistic shortcuts can change which strategies an agent learns.

Funding More Real-World Experience

Farlow previously built an annotation platform at Yutori supporting computer-use model training. Putta’s background includes computer vision, robotics, reinforcement learning, and work on computer-use models at MultiOn.

The new funding will pay professionals to recreate more of their jobs in simulated environments. Plato also plans to deploy AI agents on real tasks to collect additional experience and is developing richer simulations with longer workflows and interactions with simulated people.

The round follows wider investment in agent-training infrastructure, including Labelbox’s financing for reinforcement-learning environments and AI agents. Plato’s bet is that models need an expanding supply of realistic practice, paired with evaluations that preserve the rules of the work. The value of that infrastructure depends on how well experience inside a simulation prepares an agent for the decisions it will face outside it.