Reliable without being rigid
An explicit workflow gives every run a dependable structure. Agents can still interpret incomplete inputs, handle exceptions, and find a safe path to the outcome.
AI-native automation
Automation, rebuilt for the agentic age.
You wanted recurring work to happen without you. Instead, automation came to mean rigid flows, fragile connectors, and calling a technical friend when something broke. Malleable delivers the original promise: work that reliably gets done, now with the flexibility and speed of AI.
Built from first principles
AI-native is a standard, not a feature.
AI-native simply means designing something today the way you would if you knew from the beginning that AI works. It is not an AI step inserted into an old automation builder. It is the automation itself, reconsidered around what agents make possible.
That changes the starting point. Instead of wiring together a perfect sequence of triggers and actions, your team describes the outcome, context, constraints, and judgment behind the work. Malleable turns that understanding into an operation that can run reliably in an imperfect world.
The combination
The strengths of automation and AI, without the usual tradeoff.
Automation made work repeatable and inspectable, but struggled with change. AI made software flexible and fast to shape, but left teams wondering what would happen next. Bringing them together solves the primary problems on both sides.
Flexible without becoming opaque
AI applies judgment inside a process your team can see. It does not disappear into a prompt or improvise the entire operation from scratch each time.
Auditable without connector archaeology
Every action and decision remains attributable. When a tool or situation changes, the workflow has enough context to recover or explain what needs attention.
Fast to change, safe to run
The people who know the work can refine it in plain English. The operation changes quickly while its goals, constraints, and history remain intact.
The operating model
Describe the work. Let it run. Change it as fast as the work changes.
Malleable removes the technical translation layer that made traditional automation slow to build and painful to maintain.
- 01
Explain
Your experts describe the outcome, recurring work, exceptions, and reasons behind their decisions in the language they already use.
- 02
Shape
Malleable turns that knowledge into a visible workflow with the right structure, judgment, software, and human handoffs.
- 03
Run
The operation carries the work across tools and people, adapting when needed while recording what happened at every step.
- 04
Refine
Your team gives feedback in plain English, and real runs reveal where the workflow should become smarter, faster, or more predictable.
In practice
Built for operations, not isolated AI tasks.
AI-native automation becomes valuable when it carries recurring work across systems, decisions, and handoffs—not merely when it produces a good answer once.
Meter configuration at Guidewheel
Guidewheel turned its installation SOP into a runnable workflow. Field teams submit a few inputs, then Malleable carries out the repetitive configuration work in the background.
Read the Guidewheel storyInsurance form verification
An agentic workflow reads submitted forms, applies state-specific requirements, explains what is missing, and records the approved result.
Explore the workflowA buyer's checklist
Look for more than AI inside a flow builder.
The test is whether the product was reconsidered around AI, not whether an existing automation canvas gained an agent step.
- Can the people who own the work define and change the automation themselves?
- Can it handle imperfect inputs and new exceptions without another branch for every possibility?
- Can it use deterministic execution where reliability matters and agent judgment where flexibility matters?
- Can your team inspect any run and understand every action, decision, and handoff?
- Can the automation improve through use without creating a new technical maintenance burden?