Galactos | AI Workflow Reliability
Audit Your Workflow

Your AI workflow can say ‘done’ before the real work is done.

Business outcomes can be wrong, incomplete, duplicated, or unresolved.

Galactos investigates consequential AI-enabled workflows from trigger to verified business completion.

Local success signals
Actual business outcome
Request understoodModel response

Cancel my Pro plan and refund the duplicate charge.

Intent recognized: cancellation + refund

Refund request acceptedAPI accepted

Refund: $49.00 Status: Accepted

200 OK
Workflow step completedAutomation complete

Refund reference written to support case.

Cancellation workflow marked complete.

Case closedTicket closed

Resolution: cancellation + refund completed.

Customer notification sent.

Actual business stateBusiness outcome
Subscription still active

Next renewal: scheduled Refund request: accepted Cancellation: not confirmed

WrongIncompleteDuplicatedUnresolved
✓Model response
✓API accepted
✓Automation complete
✓Ticket closed
×Business outcome
WrongIncompleteDuplicatedUnresolved

Case: customer requests cancellation + refund

The workflow reported success. The business process did not complete.

  1. Customer request

    “I’d like to cancel my subscription and get a refund.”

    Request understood

    Intent classified

    T+0s

  2. Policy retrieval

    Relevant policy found for plan, region, and date.

    Policy retrieved

    Policy match, v4.2

    T+1.3s

  3. Refund action

    Refund API call accepted by payment provider.

    Refund action accepted

    200 OK

    T+4.0s

  4. Case updated

    Support ticket marked as closed by the workflow.

    Ticket closed

    Case closed

    T+6.1s

  5. Authoritative state

    Subscription remains active in billing system. Next renewal still scheduled.

    Subscription still active

    Unresolved state

    T+8.4s

Reported vs. verified completion

Component success is not evidence of end-to-end completion.

Reported completion means the workflow, component, or interaction says the work finished. Verified completion means the intended business state has been independently checked in the authoritative system that can actually prove the outcome.

Reported completion

  1. User request

    Cancellation + refund request received.

    Request understood

  2. Model response

    Intent correctly understood.

    Intent classified

  3. Policy system

    Correct policy retrieved.

    Policy retrieved

  4. Payment API

    Refund call accepted.

    Refund action accepted

  5. Support system

    Ticket marked as closed.

    Ticket closed

Workflow reports success here.

Verified completion

Verify actual state

Check subscription status in billing system.

Subscription still active

Next renewal scheduled

Divergence

Verified completion

Would require confirmed cancellation and refund status in authoritative systems.

Authoritative state confirmed

Local success ≠ terminal success

Every component can report success while the workflow still fails.

Component success tells you that a local step behaved as expected. It does not necessarily tell you that the business state the workflow was supposed to create now exists.

The gap becomes harder to see when work crosses models, retrieval systems, APIs, queues, automation platforms, HITL approvals, and systems of record.

Reliability therefore requires a different standard for “done.”

Local evidenceTerminal evidence
ModelOK
context loss
RetrievalOK
APIOK
handoff delay
QueueOK
retry risk
AutomationOK
HITL approvalAccepted
approval ambiguity
System of recordOK
Business obligation?

Local signals do not prove the intended business outcome was achieved.

Observed success at each local component does not automatically compose into verified completion.

“Done” has two meanings.

Reported

The workflow, component, or interaction says the work finished.

Local evidence
Model response
API accepted
Automation complete
Case closed
Self-reported completion
No independent proof beyond this point.
Terminal evidence
Independent verification
Authoritative state confirmed

Verified

The intended business state has been independently checked in the authoritative state.

Verification begins where local success ends.

Local evidence can be necessary without being sufficient for completion.

Cross-domain

This problem appears anywhere AI crosses systems.

Work can look complete in one system, while the real business outcome tells a different story.

Support
Cancellation and refund
01
Reported

"Refund issued."

Actual

The refund request was accepted, but settlement failed or the subscription remained active.

Verified

The authoritative payment and subscription states confirm the requested remedy.

Revenue
Lead routing and follow-up
02
Reported

"Lead qualified and routed."

Actual

The lead was classified correctly, but ownership failed to propagate or no follow-up was created.

Verified

The correct owner receives the lead and the required downstream action exists in the CRM.

Knowledge
Answer generation and citation
03
Reported

"Answer generated."

Actual

The response relied on obsolete, conflicting, or unauthorized source material.

Verified

The answer uses the correct authoritative source set, version, and permissions.

Recognition

When this becomes a Galactos problem.

Galactos is useful when an AI-enabled workflow affects customers, revenue, or operations, but the team cannot confidently reconcile what the workflow reports with what actually happened from trigger through final state.

  1. Model response
  2. API accepted
  3. Automation complete
  4. Business outcome

37%

of AI-enabled customer service workflows had a material mismatch.

Consumer study · 2025

Example

A refund workflow succeeds for 99% of cases, but intermittently leaves the subscription active.

  • ReportedRefund issued
  • ReportedTicket closed
  • ActualSubscription still active

Example

An agent marks the case done, and a person still opens the ledger because the payout can be wrong.

  • ReportedReview complete
  • ActualA person is still checking

Example

The ticket is closed in the help desk. The billing record still shows the original charge.

  • ReportedTicket closed
  • ActualCharge still open

Example

A retry sends a second refund while the first refund is still settling.

  • ReportedRefund issued
  • ActualA second refund is in flight

Example

The permission changed last week. The workflow still applies the old rule and reports success.

  • ReportedRule applied
  • ActualThe current rule was not used

Example

The model trace, the ticket, and the ledger each hold a fragment. No one record shows the case.

  • ReportedCase logged
  • ActualThe full case cannot be rebuilt

The Diagnostic is designed for an existing consequential workflow. It is not an enterprise-wide AI transformation program.

The Intervention

The AI Workflow Reliability Diagnostic.

One consequential workflow. One defined business outcome. A bounded investigation of what actually happens from trigger to verified completion.

The Diagnostic identifies where reported execution diverges from the state the workflow was actually supposed to produce, why the divergence occurs, and what should be repaired first.

Depending on the workflow, the investigation may follow model behavior, context and retrieval, integrations, queues, permissions, retries, system-of-record updates, exception paths, and human handoffs. The unit of analysis remains the business outcome, not any single component.

Built from product systems thinking, workflow analysis, AI architecture, context systems, evaluation, and operational process design.

Scope: one workflow · Mode: diagnostic
SCOPE: ONE WORKFLOWMODE: DIAGNOSTICTRIGGERAUTHORITATIVECOMPLETION
Intent
Success
Evidence
Terminal
Outputs

What you leave with.

A compact evidentiary account of what the workflow is supposed to do, where it diverges, and what should happen next.

A.01For: Engineering

Workflow + Failure Map

Reconstructs the intended end-to-end path, systems, handoffs, state transitions, and meaningful failure points.

Illustrative structure
A.02For: Engineering · Risk

Evidence-Backed Defect Register

Documents each material divergence with the observable evidence behind it, consequence, likely cause where supportable, and priority.

Illustrative structure
IDDivergenceEvidenceSev
D-001Subscription state not updatedPayment ledger (T+2.1s)
HIGH
D-002Handoff not recordedAudit log: no entry found
MED
D-003Retry produced duplicate recordEvent stream: duplicate
LOW
A.03For: Product · Operations

Completion Contract

Defines the testable conditions required before the workflow may truthfully be considered "done," including the terminal state and evidence source.

Illustrative structure
Postconditions
—Subscription record reflects updated status.
—Status change event logged in audit trail.
Authoritative state
—Customer subscription table (primary record)
Evidence source
—Payment provider webhook response
—Internal event ledger sync
Scope
—Single path: trigger → state update → confirmation
A.04For: Owners of the fix

Prioritized Repair + Regression Plan

Translates findings into what should be corrected first and what tests should demonstrate that the corrected workflow continues to behave correctly.

Illustrative structure
P1Repair state propagation on confirmed success path
↳Test: Trigger confirmed cancellation; verify subscription status within defined window
P2Add logging at each external handoff boundary
↳Test: Confirm audit entry present at all handoff points after trigger
P3Establish regression baseline for affected paths
↳Test: Replay historical traces; compare output against expected terminal state
Method

Follow the business outcome, not the green checks.

01
Define Completion

Establish what must actually be true for the business obligation to count as complete.

02
Trace Execution

Reconstruct how representative cases moved across models, systems, handoffs, and human decisions.

→ webhook.received [T+0ms]
→ model.inference [T+142ms]
→ state.update [pending…]
03
Verify Actual State

Compare the workflow's reported state with authoritative evidence of what ultimately occurred.

system state
04
Reconstruct Divergence

Follow mismatched cases backward to locate where intended and actual execution separated.

05
Confirm Causes

Use available logs, evidence, replay, controlled tests, and system behavior to distinguish supported causes from speculation.

06
Prioritize Repairs

Rank fixes by consequence, recurrence, tractability, and the tests needed to prevent regression.

assert(subscription.status)
assert(auditLog.contains(id))
01
Define Completion

Establish what must actually be true for the business obligation to count as complete.

02
Trace Execution

Reconstruct how representative cases moved across models, systems, handoffs, and human decisions.

→ webhook.received [T+0ms]
→ model.inference [T+142ms]
→ state.update [pending…]
03
Verify Actual State

Compare the workflow's reported state with authoritative evidence of what ultimately occurred.

system state
04
Reconstruct Divergence

Follow mismatched cases backward to locate where intended and actual execution separated.

05
Confirm Causes

Use available logs, evidence, replay, controlled tests, and system behavior to distinguish supported causes from speculation.

06
Prioritize Repairs

Rank fixes by consequence, recurrence, tractability, and the tests needed to prevent regression.

assert(subscription.status)
assert(auditLog.contains(id))

Historical and read-only evidence comes first. Sandbox or controlled production testing is used only when appropriate to the engagement.

Approach

One business outcome,
followed across
every boundary
it crosses.

Galactos investigates one consequential workflow horizontally across models, integrations, systems, and human handoffs so the real business outcome can be verified.

  • Bounded scopeOne workflow. One defined business outcome.
  • Cross-boundary viewFollows the path across tools, systems, and approvals.
  • Useful outputClarifies where the workflow diverges and what to repair first.

Have a workflow you don't completely trust?

Walk me through what it is supposed to do, where uncertainty appears, and what you can currently observe. We can determine whether a bounded reliability diagnostic makes sense.

One workflow is enough.
Observe
Verify
Clarify
Repair