The KittyClaw approach: put the work at the center
Describe an outcome. Give it a workflow. Let AI do the work, check the result, and move it forward. Step in where your judgment matters.
The organizing unit in KittyClaw is a ticket moving through a durable process. Conversations help clarify it. Agents execute parts of it. Specialized missions improve it. The work itself holds everything together.
From operating AI to directing work
When AI becomes part of everyday work, generating an answer is only one step. Someone still needs to remember the objective, supply context, check the output, handle missing information, and decide what happens next. If that someone is always you, autonomy ends at the edge of each conversation.
KittyClaw moves that coordination into a process you can inspect and change. A project contains pipelines. Each pipeline describes how a kind of work progresses. Tickets carry individual requests through those stages, with their history and results attached.
You define what good work looks like and how it should advance. KittyClaw coordinates the executions needed to get there.
Three useful ideas, one different center of gravity
The conversation: a way to express intent
A chat session is a useful place to explore an idea, ask questions, and refine a request. But a conversation alone leaves the process implicit: which requests remain open, which outputs were checked, and which task should happen next?
In KittyClaw, the request becomes a ticket with an expected deliverable and acceptance criteria. Questions and answers stay connected to that work. You can return to the project through its current state instead of reconstructing progress from a series of chats.
The agent: an execution capability
An autonomous agent can use tools, reason through a task, and produce a result. KittyClaw uses that capability inside a defined stage. The stage supplies a mission, the ticket context, and the configured skills and model. The result feeds the next routing decision.
You organize around business questions such as “Does this proposal meet the brief?” or “Is this change ready to release?” An agent runs when the process calls for that work. Its execution is one part of the ticket’s lifecycle.
The team: specialization where it helps
Specialization is valuable: drafting and reviewing require different instructions. KittyClaw expresses those responsibilities as processing stages. A writing stage can focus on producing a draft; a checking stage can focus on finding unsupported claims; a correction route can send the ticket back with concrete findings.
This makes the handoff explicit. You can see what each stage receives, what it should produce, and where each outcome leads. A workflow can use several specialized missions without requiring you to manage an imaginary organization of AI colleagues.
How you actually work with KittyClaw
- Define the outcome. Create a ticket with source material, the requested deliverable, constraints, and acceptance criteria. “Prepare a proposal supported by this client brief” gives the process a result to aim for.
- Design the path. Configure columns for the business stages: qualify, prepare, check, correct, deliver. Give processing stages their missions and choose the skills and models they need. Add a human checkpoint wherever a decision needs you.
- Let the process execute. Eligible tickets are picked up by their configured processors. Each execution works on its current mission and records an outcome. Routing sends the ticket to the appropriate next stage.
- Handle exceptions in context. A missing fact can become a question. A failed check can lead to correction. A larger task can be split into child tickets, with the parent waiting for them. Configured retries handle retryable failures; unresolved obstacles stay visible.
- Inspect and improve. Review the output, recorded evidence, and ticket history. Accept, request a correction, or stop at a human checkpoint. Refine the workflow when you discover a recurring weakness, so future tickets benefit from the change.
Request → Qualify → Produce → Check → Deliver
Missing information → Clarify · Failed check → Correct → Check again
The board is the visible surface of this process. A column represents a business state and can have a processor attached. Moving through the workflow means progressing toward a result, with explicit rules for what happens along the way.
A concrete example: a client brief becomes a proposal
You add a brief and ask for a proposal. The qualification stage checks whether the scope and constraints are sufficiently clear. If essential information is missing, the ticket goes to clarification. Your answer becomes part of its history.
The preparation stage produces the proposal. A separate check compares it with the brief: are the requested deliverables covered, are assumptions identified, and are commitments supported? A failed check routes the work to correction. A passing result can go to your approval stage or continue through the delivery path you configured.
For the next brief, the same process is already available. You supply a new request. The workflow supplies the coordination. See the proposal workflow in detail →
Why this brings the best parts together
Flexibility inside a reliable structure. Natural language lets you express varied requests and stage missions. Explicit states and routes make the progression understandable. You can change the process as the work changes.
Autonomy with chosen points of control. Routine steps can advance automatically. Human checkpoints belong where you want them, including before consequential delivery. You choose how far a workflow runs on its own.
Specialization with visible handoffs. Different missions, skills, and models can serve different stages. The ticket keeps the common objective and history, so the process remains legible across executions.
Continuity beyond a single session. Tickets, execution records, questions, and dependencies preserve the state of the work. Processor memory can carry lessons into later executions. Progress has a place to live beyond the latest answer.
A process that improves across requests. When a review reveals a missing check, you can add it to the workflow. That improvement becomes available to subsequent tickets. You build a repeatable way of working as you deliver individual results.
What makes it trustworthy
A workflow does not make a model infallible. A second AI pass can miss the same error as the first. Confidence comes from checks suited to the task: tests for code, source references for factual claims, explicit acceptance criteria, and human judgment where necessary.
KittyClaw gives those checks a place in the process. Recorded evidence helps distinguish an agent’s statement from an observed result. Missing or contradictory evidence can inform a correction or an owner decision. The quality of the workflow still depends on the missions, tools, checks, and permissions you configure.
The best of these worlds, for work that needs to continue
A direct conversation remains useful for a quick question. A single execution can be enough for a bounded task. KittyClaw’s approach becomes especially useful when requests recur, work crosses several stages, or you need to know what happened and why.
You keep the expressiveness of chat, the execution power of agents, and the benefits of specialization. A durable workflow connects them to an outcome you can track.
The result is a different way to work with AI: define the result, organize how it gets done, and improve the process with every delivery.