Constrain before execution
Per-stage tool access, explicit guidance, human authority boundaries, and completion gates limit what the agent can do and what counts as finished.
AI agent governance
Govern agentic work through the operation, not around it.
An agent is easier to govern when it works inside an explicit process. Malleable constrains what each stage can do, requires evidence that work is complete, produces structured records for review, and turns policy failures into visible opportunities to correct the operation.
Governance by design
Governance begins with more structure than a prompt.
A standalone agent may have broad instructions and broad tool access, then improvise an entire task inside one context window. That makes it difficult to predict where a policy applies, limit authority precisely, or reconstruct why an action occurred.
Malleable breaks an operation into explicit stages. Each stage has a goal, guidance, permitted tools, completion gates, and defined paths to people or other work. The agent still has room to exercise judgment, but it does so inside boundaries the organization can inspect and change.
Layered controls
Prevent, inspect, detect, and correct.
No single guardrail carries the whole burden. Malleable combines controls before and during execution with structured review and an improvement loop after the run.
Make behavior inspectable
Stages produce structured outputs and an attributable history of actions, decisions, and handoffs, making review more direct than reconstructing a free-form agent session.
Find policy failures after the fact
The self-improvement loop can identify behavior that violated policy, surface the mistaken example for correction, and propose a durable change to the operation.
Check with independent context
LLM-as-judge stages can review work in a separate context window, reducing the prompt-injection risk of asking the same compromised context to police itself.
The operating model
Make policy part of how the work runs.
Governance becomes an operating loop rather than a document that sits beside the agentic system.
- 01
Design
Domain owners and governance teams make policies, permissions, completion criteria, and human decisions explicit in the workflow.
- 02
Enforce
Each stage runs with only its permitted tools and must satisfy its gates before the operation can move forward.
- 03
Review
Structured outputs, run history, and independent judge steps make behavior easier to inspect against policy.
- 04
Correct
Detected failures and mistaken examples feed a reviewable improvement back into the workflow instead of remaining one-off incidents.
In practice
Governance is clearest when policy is part of the workflow.
Operations involving compliance, evidence, and consequential decisions show why control needs to live at the stage where work occurs.
Compliance evidence audit
A governed workflow can gather evidence, apply explicit requirements, record its findings, and route uncertain or consequential cases for human review.
Explore the workflowPolicy change verification
Malleable can check requested changes against policy, make its evidence visible, and keep approval authority with the appropriate person.
Explore the workflowA buyer's checklist
Inspect the controls at every stage of the lifecycle.
Agent governance should cover what the system may do, how behavior is reviewed, and how the operation changes after a failure is found.
- Can tool access and authority be limited separately for each stage of an operation?
- Must the agent satisfy explicit completion criteria before work advances?
- Does each run produce structured, attributable evidence for review?
- Can independent judge steps evaluate behavior outside the context that produced it?
- Do policy failures surface mistaken examples and lead to reviewable, durable corrections?