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RL Environment
A faithful τ-bench-style environment cloning Atlassian Jira (REST v3 + Agile) and Confluence (REST v2 + CQL): 102 agent tools, 18 multi-step tasks, and process-modulated, state-grounded rubrics (R = outcome × efficiency with per-step PBRS shaping).
Screenshots
Industry
Environment specs
Persona / role
Problem
Large language models often fail at long-horizon tasks that require complex, multi-step planning. To effectively evaluate and train agents on real-world enterprise workflows, they need more than standard static benchmarks; they require interactive environments where they can learn by performing concrete actions with real-world data and receive accurate feedback on their progress.
Solution
We built the Atlassian Cloud RL Environment, a faithful reinforcement learning sandbox built on Jira and Confluence. It equips the model with 102 distinct tools (actions) that it can use to modify the environment toward a desired state. Agents are given specific scenarios (tasks) to fulfill and are automatically graded by verifiers using a high-level, state-grounded rubric. This infrastructure allows us to create flexible, highly customizable reward models tailored to different dimensions like accuracy, latency, number of tool calls, and budget.
Impact
Across 18 multi-step Atlassian tasks (such as triaging bugs or managing sprint rollovers), the environment serves as both a robust training ground and a transparent benchmark. By comparing simulated model executions against a perfect "golden trajectory," we can benchmark state-of-the-art models on their actual ability to navigate and execute long-horizon enterprise tasks, revealing exactly where current models succeed and fail in real software ecosystems.
Security
Disciplined security and privacy practices aligned with global standards to protect sensitive data, intellectual property, and model assets throughout the AI lifecycle.
Centific applies rigorous security, access control, and auditability standards to safeguard enterprise data, human workflows, and AI systems at scale.
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