For AI that changes the real world
An approval can be correct—and already out of date. unFragged builds controls that check whether an AI action still belongs in the world it’s about to change.
Keep the reason connected to the action.
Keep the action connected to its effect.
01 / Put continuity to the test
The agent proposed a $180 refund.
Eight minutes passed.
You decide what changed.
10:01 / What the AI saw
Customer 18291 · Support case 194
This is the situation the proposal belongs to.
10:09 / What is true now
A credit was issued while the agent was reasoning.
1 condition changed. The old approval has not changed with it.
One action. Current conditions.
Does it still belong?The account changed. Check this exact refund against the current situation.
Runs the illustrative checks below.
After execution / Independent effect check
Tool response: success. Effect: not yet verified.
A declared illustration of state, authority, delegation, evidence, trajectory, repeat-use, and effect checks. It runs locally in this page; it does not issue a refund or run the private DVS or Orphard systems.
DVS governs release authority. Orphard governs trajectory and recovery. Reliability cannot create permission, and permission cannot guarantee a reliable trajectory.
Competent existing controls may already catch this case. The receiving system must enforce relevant conditions when it commits the action, including changes after the release check. The combined enterprise contract remains an integration direction to test.
The illustration establishes neither production effectiveness nor an advantage over your existing stack. See how a joint comparison works.
The question between reasoning and consequence
Identity knows who. Policy knows what’s allowed. Workflows know the next step. The missing question can be whether those answers still agree for this action, now.
unFragged’s integration direction connects the state, authority, and evidence your systems already produce—then checks the effect in the system that acts.
Consequential Action Continuity
Before AI changes something real, make sure the conditions that justified the action are still true. After it acts, verify what actually happened.
Keep a current, traceable picture of the work and what changed.
Check that the proposed action remains supported by current state, scope, evidence, and authority.
Compare the observed result with the intended effect, separately from a tool’s success message.
YOUR IDENTITY · YOUR POLICIES · YOUR WORKFLOWS · YOUR AGENTS · YOUR BUSINESS SYSTEMS
Explore how they connect ↗02 / Move from explanation to evidence
Follow a recorded request to the receiving system. Read what was tested, what happened, and exactly how far that result reaches.
6 June 2026 · Both computers controlled by the founder.
Open the result and its scope ↗DVS / Focused local rerun30 May 2026 · Selected local test conditions.
Open the result and its scope ↗Orphard explores trajectory and recovery. Its public evidence currently defines the research question.
Explore a failure
Change the situation. See where release authority or the direction of work needs to be checked.
ILLUSTRATIVE SCENARIOS · NOT CUSTOMER RECORDS
Approval expires while an update waits in a queue.
Without control
If the relevant condition goes unchecked
With control
Check current authority before release. The queued update stays held until its exact transition is supported.
A record changes after the agent prepares an update.
Without control
If the relevant condition goes unchecked
With control
Hold the old proposal, reread the current record, and review the changed action.
An agent retries a request after an uncertain response.
Without control
If the relevant condition goes unchecked
With control
Check the first outcome and whether the retry has current authority before releasing another request.
An approved task moves to another agent with a different action path.
Without control
If the relevant condition goes unchecked
With control
Hold release and rebuild the plan around the changed actor, route, and available evidence.
A long-running task keeps making progress after the objective changes.
Without control
If the relevant condition goes unchecked
With control
Pause continuation, reconcile the objective and evidence, then resume only within a supported operating envelope.
A premise supporting an action and its plan is withdrawn.
Without control
If the relevant condition goes unchecked
With control
Reassess the action and trajectory against the remaining evidence before resuming.
Then the baseline may be enough. Reproduce that result with the strongest competent existing controls. If no residual gap remains, stop here—no additional unFragged control is justified by this example.
03 / Build where the work happens
Bring one workflow where AI changes something real. Establish what your existing controls already solve. Test the remaining gap together.
Your team keeps its operational improvement and native repairs. Licensing starts only when you want the unFragged capability to persist, repeat, or scale.
Start a technical partnership Or describe your workflow →