Feishu CodeM Workflow is officially here! Reserve your spot for today's live session and unlock new features of Feishu CodeM
Fixing a bug involves more than just modifying code—it requires clarifying requirements, identifying the issue, making revisions, conducting validation, and ultimately obtaining manual approval. A complete R&D task relies on a well-established process familiar to the team.
Previously, these steps were driven by prompts to AI, but ensuring compliance, tracing failures, and troubleshooting bottlenecks still required manual follow-up from developers.
Now, Feishu CodeM Workflow has officially launched. Through a visual harness, task steps, inspection criteria, and exception handling can be orchestrated into workflows, enabling Feishu CodeM to systematically perform analysis, searching, coding, and verification—transforming your team’s R&D expertise into testable, reusable, and traceable workflows.
Visual Orchestration:
Turn best practices into executable processes
Developers can arrange stages such as requirement analysis, code fixing, validation, and manual acceptance within a workflow, clearly defining the responsible role, input requirements, and expected output for each step.
You can predefine which parts are handled by Feishu CodeM and which require human intervention. Requirements previously scattered across prompts are now consolidated into a clear execution sequence and handoff logic.
When teams accumulate efficient methods, they can reuse them across more projects. Knowledge captured once becomes the starting point for future tasks.
Smart Validation:
If it doesn't pass, send it back according to the rules
Does the code change actually resolve the problem? In the bug-fixing workflow, you can set verification conditions (such as running tests) at the fix stage—if the check fails, the system automatically rolls back and retries.
Feishu CodeM Workflow supports setting entry and exit criteria for each node, using Prompt, Bash, or Node.js scripts to validate inputs and outputs. There is clear justification for when to start and what standards must be met before moving forward.
On failed checks, the workflow can terminate, escalate to a human, or return to a specified node for retry. Retry limits and timeout policies are configurable, ensuring rework follows defined rules and boundaries.
Developers can encode repetitive instructions for AI checks and re-execution into the workflow, with manual judgment triggered at critical nodes.
Execution Management:
Know exactly how AI is progressing
Is the AI analyzing the issue or modifying code? What are the test results? Where does human involvement come in?
In Feishu CodeM Workflow, simply open a workflow to view progress, inputs, outputs, and check results for each node. When a task stalls, you can review logs and failure reasons; after completion, deliverables can be traced, ensuring every step remains transparent and auditable.
Workflows validated through real tasks and published can also be integrated into Feishu Projects to trigger automatically, embedding mature R&D methodologies into daily collaboration.
There are still a few steps from awareness to implementation.
Join today’s live session to explore Feishu CodeM Workflow together.
You can scan the QR code or click the original link to access “Live Session Reservation”, helping Feishu CodeM Workflow integrate seamlessly into your real-world R&D environment!