Recursion Managed Agents: AI agents that get better every run

Labelbox introduces Recursion Managed Agents, a platform for autonomous AI agents that can start from schedules, alerts, messages, webhooks, or APIs and work across business applications without continuous human prompting. Agents can coordinate specialists, form teams for parallel tasks, or choose their own approach, using different models such as Claude, GPT, Gemini, and open-weight models while operating with scoped permissions and approval requirements for sensitive actions. Each session produces an auditable record of model activity, tool calls, artifacts, costs, and rubric-based grading, with failed work revised or paused for human review. The platform emphasizes a learning loop in which graded session reports update an agent-specific, inspectable memory, allowing future runs to use accumulated knowledge without retraining the underlying model; Labelbox reports that one internal pull-request review agent reduced median successful-review costs by 32% over 39 runs while maintaining similar completion times. Labelbox describes uses across engineering, reliability, security, research, finance, sales, strategy, and procurement, including incident investigation, flaky-test fixes, vendor reviews, forecasting, and account research. Security measures include isolated sandboxes, vaulted short-lived credentials, allowlisted hosts, action approvals, spend and time limits, zero data retention at LLM providers, and audit logs, while pricing is based on agent-session hours with compute and model costs combined.

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