Clanker CloudA practical Workspace guide
An AI transformation roadmap you can act on.
Build an AI transformation roadmap around one workflow and evidence from its results. In Clanker Cloud, define the job, prepare its Project sources, test an Agent and inspect the saved output. Expand into team handoffs and recurring work only after the pilot meets your acceptance criteria.
Start with $20 in included credits. See usage and plans.
Use four stages, at your team’s pace
This is a planning framework, not a promised completion time. A small pilot may move quickly; integration or data requirements may need more preparation.
| Stage | Do the work | Keep the evidence |
|---|---|---|
| 1. Choose | Name a recurring job, its owner and the expected output. | A brief, current baseline and acceptance checklist |
| 2. Prepare | Select sources, permissions and a Workspace or external-agent route. | A source inventory and an approved access decision |
| 3. Test | Run a bounded task, inspect the file and request a correction. | Saved outputs, review notes and actual usage |
| 4. Expand | Reuse the Agent; add a handoff or recurring setup when useful. | An owner, escalation rule and stop procedure |
Sources: How Clanker Cloud works · Pilot evaluation starter
Make success observable before starting
Choose a job with a useful artifact: a report, evidence table, spreadsheet or reply draft. Write down who will use it and how they will check it. Include an example the reviewer already accepts.
Record the current process: preparation, execution, checking and correction. Do not call a faster first draft a saving if checking or rework consumes the difference.
Prepare the source and access decisions
Put approved inputs and instructions in a Project. Record who owns each source, the reporting cutoff and what the Agent should do when information is missing. Check whether a connector copies data, queries it or keeps it synchronized.
Choose the actual connection and permissions for the pilot. A Project name in an instruction is not an access-control boundary. Review provider handling and the current Workspace data controls; discuss dedicated or sovereign requirements before choosing that deployment path.
Sources: Work with your preferred AI agents · Current Workspace data controls · Supported data routes
Test a correction, not only a first draft
Run a task with known facts. Check the saved output, then change one source fact and request a correction. The current file and any related summary should agree after the update.
Include a missing date, conflicting total or unavailable connection in the trial. Decide whether the Agent should stop, flag the gap or ask a reviewer. Record the actual result and usage; keep failed attempts in the evaluation.
Expand the method with clear ownership
Save the accepted method as an Agent’s instructions or a reusable Skill. Try a handoff: one authorized agent saves a brief and open questions, then another reads those files to prepare the next deliverable. Ask the reviewer whether the handoff preserves enough context.
For recurring work, configure a Loop or Schedule separately. For email, choose a reply mode and test recipients, exclusions and escalation. Name the person who checks failures and credit use and who can pause the workflow.
Sources: Create a reusable Agent · Test an agent handoff · Configure recurring work
Make an explicit continue, revise or stop decision
Compare quality, review effort and actual usage with your baseline. Continue when the output meets the criteria; revise when a fix is clear; stop when the workflow cannot meet its data or review requirements.
Bring the task, evidence, data needs and expected volume to a business or enterprise discussion. That gives the team a concrete basis for plan and environment choices.
Sources: Plan credits, runtime and review costs · Book a business or enterprise meeting · Email the enterprise team
Before you use the result.
- The owner and reviewer agree on the result and acceptance criteria.
- The selected sources and permissions match the pilot.
- A source correction reaches the current saved output.
- Review effort and usage are observed, not assumed.
- Recurring work has an owner, escalation path and stop control.
Common questions.
How long does AI transformation take?
There is no fixed timeline in this roadmap. Begin with a bounded workflow and move to the next stage when its data, output and review requirements are met.
What should we measure in the pilot?
Measure whether the saved result meets its criteria, preparation and review time, corrections, failures and actual model, tool or runtime usage. Compare the same task and include human effort.
Should the pilot include multiple agents?
Only when a handoff serves the job. Start with one Agent, then test whether another authorized agent can continue from the saved sources, output and open questions. Shared files do not imply shared private memory.
Sources: How Clanker Cloud works · Work with your preferred AI agents · Plan credits, runtime and review costs · Current Workspace data controls
Content reviewed against the Workspace Guide & FAQ. Features and access can depend on your account.