Full details for Labelbox
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Recursion is a managed agent platform for enterprise work. Users set a goal, a coordinator plans it, specialists execute tasks in parallel across systems, and a grader scores the outcome. Each graded run becomes memory, skills, and training signal, enabling the fleet to improve over time. Recursion supports persistent, long-horizon, asynchronous agents, multi-agent workflows, and works with models such as Claude, GPT, Gemini, and open-weight models. The platform connects environments, evaluation, and training into a closed-loop reinforcement learning system, turning enterprise execution into continuous model improvement.
Review
At a glance
Recursion by Labelbox is a managed agent platform designed for enterprise work, enabling organizations to automate complex workflows using fleets of persistent, long-horizon, asynchronous agents. The platform is built to handle large-scale, multi-system tasks by coordinating specialist agents, each optimized for specific models and tasks. Every outcome is graded, and the system learns from each run, improving over time without requiring manual retraining. Recursion integrates with a range of models and supports reinforcement learning from real enterprise execution.
Capabilities
Recursion supports the orchestration of multiple agents working in parallel across enterprise systems. It allows users to set high-level goals, which are then decomposed and executed by specialist agents. The platform supports persistent sessions that can run for hours or days, pausing and resuming as needed. It is compatible with various models, including Claude, GPT, Gemini, and open-weight models, and enables fine-tuning and reinforcement learning based on graded outcomes. The system is designed to handle complex, long-horizon tasks and supports continuous improvement through closed-loop learning.
Workflow
The typical workflow in Recursion begins with a user setting a goal. A coordinator agent plans the task, spawning specialist agents as needed to execute subtasks in parallel across different systems. Each agent operates on the model it is best suited for, and a grader agent evaluates the outcome of each session. Sessions are persistent, allowing for long-running tasks that can pause and resume. The results of each session are graded and stored as memory, which informs future runs and enables the fleet to improve over time. The platform also supports the creation of RL environments from real enterprise workflows, enabling continuous training and evaluation.
Strengths
Recursion's strengths include its ability to manage complex, multi-agent workflows across enterprise systems, persistent and long-horizon agent sessions, and a closed-loop learning system that enables continuous improvement without manual retraining. The platform's compatibility with multiple models and its ability to fine-tune and reinforce learning from real execution signals provide organizations with a powerful tool for automating and optimizing enterprise tasks. The grading and memory features ensure that the system learns from each run, compounding expertise over time.
Limitations
Recursion is designed for enterprise-scale tasks and may require significant infrastructure and expertise to deploy effectively. The platform's complexity and focus on long-horizon, multi-agent workflows may present a learning curve for teams unfamiliar with reinforcement learning or agent-based systems. Additionally, while Recursion supports integration with various models, the specifics of integration with third-party systems or custom workflows may require additional configuration and support.
Best for
Recursion is best suited for large organizations and enterprises that need to automate complex, long-running workflows across multiple systems. It is particularly valuable for teams working on reinforcement learning, model fine-tuning, and continuous improvement of AI agents in production environments. Organizations with unique workflows, domain expertise, and a need for persistent, multi-agent automation will benefit most from Recursion's capabilities.
Value and verdict
Recursion offers a robust platform for enterprises seeking to leverage managed agents and reinforcement learning to automate and optimize complex workflows. Its closed-loop learning system, persistent agent sessions, and compatibility with leading AI models make it a compelling choice for organizations aiming to achieve continuous improvement and operational efficiency. While the platform may require significant investment in infrastructure and expertise, its potential to transform enterprise automation and learning loops positions it as a valuable tool for forward-thinking AI teams.
Key features
- Managed agent orchestration
- Persistent, long-horizon asynchronous agents
- Goal-based task planning and execution
- Graded outcomes and memory
- Supports Claude, GPT, Gemini, and open-weight models
- Closed-loop reinforcement learning from enterprise execution
- Multi-agent workflows across enterprise systems
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