Plain-English direction
Both let people describe the work, context, and expectations without writing application code.
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Malleable compared with AI teammates
Put AI teammates inside an operation your team can own.
Malleable and AI teammate platforms both turn plain-English direction, company context, and connected tools into autonomous work. The difference is whether the reusable system is organized around an AI worker or around the operation the team is responsible for.
Shared foundation
What Malleable and AI teammate platforms have in common.
Background execution
Both can run recurring work autonomously in response to schedules, events, or direct requests.
Connected company context
Both can gather information and take action across the tools where the team already works.
Governed action
Both can constrain access, involve people before consequential actions, and preserve a record of execution.
The main difference
What is the team deploying and improving?
AI teammate platforms
A creator configures an AI worker around a responsibility: its instructions, context, tools, permissions, triggers, and delivery surfaces. The agent plans or follows the work within that setup.
Malleable
Domain experts define the operation: its outcome, stages, rationale, constraints, roles, and handoffs. Malleable assigns agent judgment and predictable execution within that shared operating model.
Side by side
What follows from that difference.
AI teammate platforms
Malleable
Process visibility
AI teammate platforms
The process is implied by a worker's instructions, tools, and triggersMalleable
The operation is visible as shared stages, decisions, and constraintsAuthorship
AI teammate platforms
An agent creator translates the work into instructions, access, and triggersMalleable
Domain experts define how the operation should run directlyRun consistency
AI teammate platforms
The worker re-plans open-ended parts of its responsibility each runMalleable
Stable stages keep background work consistent while judgment handles variationHuman coordination
AI teammate platforms
People invoke, supervise, approve, or receive work from the teammateMalleable
Handoffs define each participant's decision, timing, and contextChange scope
AI teammate platforms
A creator edits worker configuration and must anticipate downstream behaviorMalleable
The expert describes the change and reviews its operational effectAudit context
AI teammate platforms
Execution history shows what the worker did, not why the operation works that wayMalleable
Each run stays connected to rationale, structure, and human decisionsTool-by-tool
Compare Malleable with AI teammate platforms
Each page uses the vendor's current product materials and identifies where that tool or Malleable fits the work.