Canvas
Building AI systems that improve from experience
Canvas Coworker is an AI coworker for long-running go-to-market and knowledge-work tasks. It is designed to gather context from multiple systems, manipulate data and files, act through tools, check results, and recover from errors across extended trajectories. Users give it an objective rather than a predefined sequence of steps, and it can research companies and people, query business systems, write and execute code, manipulate files and spreadsheets, return a finished artifact, or trigger the next step in a workflow.
The product runs on an agent runtime supporting the Claude Agent SDK and Codex SDK, with a harness that manages context, skills, permissions, memory, and control logic. Each session runs in an isolated cloud workspace with its own filesystem and code execution environment, where files and intermediate state persist across steps. Tasks can run on demand, on a schedule, or as recurring automations, with support for parallel sessions. Canvas MCP and Canvas Data MCP connect the agent to authenticated business systems and expose company, people, and contact-enrichment data through a tool interface. A tracing system records model context, tool calls, files, errors, and final output for each run, and an LLM judge reviews these traces to identify recurring failures that inform harness updates.
Canvas Coworker is packaged with a free plan that includes monthly credits, paid Pro and Scale editions with higher credit allowances, and a custom Enterprise edition for large organizations.
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