Execution Integrity | Engineering AI for Partial Execution, Recovery and Control | R.A.H.S.I. Framework™
An agent says “done.”
The ticketing API timed out.
Did the ticket fail to open, or did it commit while the acknowledgement was lost?
That distinction determines whether retrying is safe.
Microsoft’s retry guidance warns that repeating a non-idempotent operation can cause duplicate effects. A timeout does not establish the commit state.
Execution Integrity
Execution Integrity is the R.A.H.S.I. architecture lens for establishing what an AI-driven workflow did and controlling what happens next.
A produced response, a completed workflow, committed transactions and the intended business outcome require different evidence.
For workflows that change records, move data or trigger payments, design for uncertain states:
Reconcile Before Retrying
Check the external system when commit status is unknown.
Bound retries and define when they are valid.
Record What Committed
Partial success can leave earlier changes in force.
Saga compensation may address those changes, but it cannot always restore the original state.
Mark irreversible steps before execution.
Persist Recovery State
Durable checkpoints let another process resume a long-running workflow.
First establish which steps committed.
Recovery is more than replay.
Contain and Stop
Circuit breakers and bulkheads limit repeated calls and blast radius.
Safe termination or human adjudication may be the right path.
Correlation IDs trace requests across components; they do not prove transaction truth.
Reconstructable AI™ requires the agent decision, tool calls, retries, human interventions and final external-system state to be examined together.
Test:
- lost acknowledgements
- rejected downstream transactions
- exhausted retries
- failed compensation
Verify that:
- duplicate effects are prevented
- uncertain commits are resolved
- the final state is established
Failure should be designed before success is celebrated.
| R.A.H.S.I. Framework™
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Execution Integrity | Engineering AI for Partial Execution, Recovery and Control | R.A.H.S.I. Framework™
AI workflows can partially succeed, fail mid-execution, or recover inconsistently. R.A.H.S.I. explores execution integrity through retries, checkpoints, compensation, state, observability and controlled recovery.
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