Limits on each step
Each step can use only the tools it's given and follows your team's guidance. Each stage has to meet its completion criteria before the operation moves on. Decisions that belong to a person wait for them.
Governance
Governance built into every operation.
Each step of an operation can use only the tools it's given, and a step that needs a person waits for their decision. Every run records what the AI did and what people decided. On Enterprise, IT also chooses which apps and models teams can use.
For example
In an employee onboarding operation, the step that reads the offer letter can't create accounts. The step that creates them can, but only after IT approves, and the run records what the AI did and who approved it.
Why structure matters
Governance needs more structure than a prompt.
A standalone agent may have broad instructions and broad tool access, then improvise the whole task in one go. That makes it hard to predict where a policy applies, to limit what the agent is allowed to do, or to reconstruct why it took an action.
Malleable breaks an operation into steps. Each step has a goal, the guidance your team wrote, only the tools it's allowed, and criteria it has to meet before the operation moves on. The AI still uses judgment, inside limits your team can see and change.
Layered controls
How Malleable keeps the AI in bounds.
Malleable combines limits on each step, a record of every run, and a regular review afterward.
A record of every run
Every run records what the AI did and what people decided, so a review starts from a record instead of a reconstructed chat session.
A regular review of real runs
Malleable reviews each operation and its real runs on a schedule and suggests specific fixes when something went wrong. Nothing changes until the owner approves it.
- Which account it uses
- Each operation acts with the account of its owner, or of the person who started it.
- Which apps and modelsEnterprise
- Admins choose which integrations and models teams can use.
- Roles and recordsEnterprise
- Custom roles, SCIM provisioning and audit log export.
How it works
Your rules are built into each operation.
There's no separate policy document to keep in sync. The rules are part of the steps the AI follows.
- 01
Set the rules
Your team decides what each step does, which apps it can use, and which decisions need a person.
- 02
The AI follows them
Each step can only use the apps it's given, and has to finish its job before the operation moves on.
- 03
Check any run
Every run records what steps the AI took, and which actions were taken.
- 04
Fix what went wrong
Malleable suggests fixes from real runs. Nothing changes until the owner approves.
In practice
See it in operations where compliance matters.
Operations that involve compliance and consequential decisions show why control belongs in the step where the work happens.
Audit evidence collection
An operation gathers the evidence for each sampled record, drafts explanations for any gaps, and asks the control owner to approve them before it builds the audit packet.
See the stepsPolicy change requests
Malleable confirms the requester's authority, checks the change against policy rules, and waits for licensed staff to approve it before it updates the policy.
See the stepsA buyer's checklist
Questions to ask before you roll out AI agents.
Governance for AI agents should cover what the agent may do, how its work gets reviewed, and what changes after something goes wrong.
- Can tool access and authority be limited separately for each step of an operation?
- Does the agent have to meet clear completion criteria before the work moves on?
- Does every run keep a record of what the AI did and what people decided?
- Do failures lead to specific fixes that the owner reviews before anything changes?
- Can IT choose which apps and models teams can use?
Let's pick an outcome to improve. See results in days, not quarters.
Book a demo