Ruflo AI · Hosted workspace for software teams
Ruflo AI helps a software team turn a real delivery goal into a clear loop: plan the work, assign the right agent, review the result, and retain the decisions that make the next run better. It is a hosted workspace for teams using Codex, Claude Code, and complementary tools who need more than isolated chats or a pile of local scripts.
Give the first run a real definition of done: a migration plan, a tested feature, a review of a pull request, or a release checklist. A bounded goal makes it easier to decide which context is needed, which agent should do the work, and where a human approval belongs.
Planning, implementation, and review have different jobs. Ruflo keeps those jobs connected to the same workspace so a reviewer can see the original goal, the relevant repository context, the proposed change, and the decision that follows.
Useful project memory is not an unfiltered transcript. It is the durable information that helps the next workflow begin well: architecture constraints, accepted tradeoffs, review notes, and links to the outputs a team already trusts.
A productive workflow begins before an agent is asked to write code. Start with the outcome a customer or teammate can verify. Describe the affected repository area, the constraints that cannot be broken, the expected evidence, and the person who will approve the result. That gives a planning agent a useful brief instead of a vague request to “look into it.”
Next, assign implementation work in pieces that can be reviewed. One agent may map the codebase and list risks; another may make the smallest viable change; a third may check tests, edge cases, and regressions. The point is not to multiply activity. It is to make each handoff explicit, so the team can tell what was tried, what changed, and what still needs a decision.
Review is the place where a team protects quality. A reviewer should compare the output to the original acceptance criteria, inspect the files or artifacts that changed, and decide whether the work is ready, needs revision, or should be parked. When a workflow has an obvious review point, agents can move quickly without asking a human to reconstruct the entire conversation from scratch.
After approval, preserve the few facts that will help future work: why a route was chosen, which test demonstrated the behavior, which source of truth applies, and which constraints are still open. This is how multi-agent workflow coordination becomes compounding operational knowledge instead of a series of disconnected sessions.
This short walkthrough follows a 72-second delivery loop from a defined goal through implementation, review, and durable memory. It is a useful starting point for teams deciding where to add agent roles and human checkpoints.
Ruflo is a good fit when several people or agents touch the same delivery loop and the important context should survive the first run. Common starting points include planning a repository change, checking a release candidate, triaging a backlog, or building a small feature that needs an implementation and review pass. It is less useful for a one-off question with no follow-up, or for work that cannot safely share any project context with a hosted environment.
Before launching a workspace, decide who owns the goal, what data or repository access is appropriate, which coding entrypoint the team already uses, and what counts as a successful first output. Then select a plan, confirm the billing cycle, and use the workspace to run one small loop. The first loop should prove the operating model, not attempt to automate an entire engineering organization.
Growth is the common team path when several operators need reusable memory and review loops. The pricing page shows current plan amounts and the checkout path; the resource guides explain how to evaluate the product, set up a first workflow, and compare a hosted workspace with self-hosting. Support is available for questions about checkout, provisioning, and getting the initial workspace ready.
Ruflo AI does not promise that every agent output is correct. It gives the team a more legible way to operate the work: clear goals, scoped roles, visible evidence, and a decision point before release. That is the practical foundation for using Codex and Claude Code together without losing the context that makes software delivery safe and repeatable.
See how Ruflo can hand off longer reasoning, drafting, and review tasks to Kimi K3 on the Kimi K3 AI workflow page.
When a Ruflo workspace result needs extended reasoning, source comparison, or a customer-ready decision note, K3nova is a natural place to continue that review.
When that workspace needs a private assistant operating frame, Clauxel gives builders a reference for agent memory, tool scope, model routes, and review habits.
Ruflo readers comparing agent workspace assumptions can also review MiroFish AI Simulator, a companion reference for simulation-style product reasoning.