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AI Agents · Rules & Validation

Put the Non-Negotiable Business Rules Outside the Prompt

Artificio agents can reason in natural language, but business controls should not depend on prompt wording alone. Rules & Validation combines deterministic data checks with runtime-enforced policies for autonomy, data freshness, step and retry limits, tolerances, thresholds and human escalation.

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  • Policy over instruction
  • Deterministic checks
  • Data-freshness controls
  • Human escalation
RULE EVALUATION · INVOICE CASEgoverned
Invoice Automation

rules + runtime policies before SAP action

policy v4
  1. Done: Mandatory fields presentpass
  2. Done: Vendor matched to SAP masterpass
  3. Done: Duplicate invoice checkpass
  4. Waiting: Price variance above tolerancepolicy stop
Policy requires a person

Variance is above the configured tolerance. The agent cannot continue autonomously.

Escalate for approvalStop case
  • Keep thresholds, tolerances and autonomy limits in governed policy instead of hardcoding numbers into instructions
  • Validate extracted or agent-generated data against documents, Data Views, master data, SAP or other approved sources
  • Use XREF, exact checks, thresholds, duplicates, date rules, calculations and transformations before an agent acts
  • Require live data freshness before external writes when cached data may be stale
  • Stop, retry, route, notify or ask a person when a rule or policy says the normal path is no longer safe
PolicyRuntime-enforced controls
ValidateExact business checks
FreshnessLive read before write
EscalateAsk when rules fail

Guardrail anatomy

Five control layers keep agent execution bounded.

Rules and policies work together: deterministic checks validate data, while runtime controls constrain autonomy and execution behavior.

  • 01 · BUSINESS VALIDATION

    Check the facts

    Verify required fields, permissible values, exact matches, dates, duplicates, line-item state and other deterministic conditions.Mandatory fieldsExact matchDate formatDuplicate
  • 02 · CROSS-REFERENCE

    Compare with trusted systems

    Use SAP, ERP, master data, local references or approved Data Views to confirm the process data before decisions or writes.SAP master dataXREFLocal referenceLive read
  • 03 · CALCULATIONS & FUNCTIONS

    Prepare valid downstream data

    Calculate derived values and transform content through operations such as defaults, replacement, concatenation, copy, split, slice or date conversion.CalculationDefault valueSplit / concatenateDate conversion
  • 04 · RUNTIME POLICY

    Constrain autonomy and resources

    Set approval requirements, data freshness, step caps, tool retries, response targets, tolerances and thresholds independently of prompt wording.AutonomyFreshnessRetry capsTolerances
  • 05 · EXCEPTION OUTCOME

    Decide what happens when control fails

    A failed rule can stop the case, retry a tool, route an exception, notify an owner or create a Waiting for you checkpoint.StopRetryRouteHuman review

What belongs in guardrails

Use deterministic controls for the decisions that must be repeatable.

The agent can interpret documents and context, while explicit rules and runtime policy control the business constraints that should be stable and auditable.

  • Policy

    Autonomy, thresholds & tolerances

    Control when an agent may act alone, when it must ask, and which numeric or business limits apply to the process.policy → evaluate → allow / ask / stop
  • Validation

    Required and permissible business data

    Check mandatory fields, exact values, date formats, duplicates, line-item status and other deterministic conditions.input → verify → pass / fail
  • XREF & ERP

    Trusted-data comparison

    Compare process data with SAP, master data, reference tables or other approved sources before the next action.extract → cross-reference → reconcile → decide
  • Transformation

    Prepare data for downstream action

    Apply calculations or functions to normalize values and create the exact format expected by SAP, APIs or other systems.calculate → transform → validate → hand off
Policy over instruction

Change a limit in one governed place, not by editing every prompt.

The Process Agent design explicitly keeps tolerances and thresholds in policy, referenced by the prompt. That lets an organization adjust a business limit without rewriting the process instructions or relying on the model to enforce a hardcoded value.

Prompt describes processPolicy defines limitsRule evaluates dataPass / ask / stopDecision logged

At runtime

Rules are evaluated as part of the case, not as a separate afterthought.

The agent can work from cached data for speed, but policy can require fresh reads before writes. Each rule or policy outcome becomes part of the case record and determines whether the agent continues, waits or stops.

RunningValidation passPolicy checkWaiting for youCompleted
  • Deterministic

    Exact checks where exactness matters

    Use explicit business logic for fields, values, tolerances, duplicates and reference-data matching.
  • Contextual

    AI rule where context matters

    Prompt-based rules can handle contextual checks while still participating in the governed rule sequence.
  • Auditable

    Outcome becomes case evidence

    The result of the rule or policy decision can be retained with the work item before the next action runs.

Runtime guardrails

Governance is enforced by the platform itself, not merely requested in the prompt.

The Process Agent page defines key runtime controls that remain effective regardless of instruction wording: approval on actions, credential isolation, freshness, limits, immutable versions and policy over instruction.

  • Policy

    Policy over instruction

    Thresholds and tolerances live in governed policy and are referenced by the prompt.
  • Freshness

    Data-freshness rules

    Use cached feeds for speed while requiring a live read of affected records immediately before a write.
  • Limits

    Limits & budgets

    Cap steps per case, tool retries and other runtime resources so an agent cannot run away or stall silently.
  • Human-in-the-loop

    Human approval on actions

    Rules or policy can move a case to Waiting for you before a sensitive action proceeds.
  • Versioning

    Immutable versions

    Prompt versions are fixed for cases already in flight, preserving the exact operating instructions used.
  • Credentials

    Credentials isolated

    Validation can use enterprise systems without exposing their credentials to the agent or case context.

Automating something that isn't on this page? The agents adapt to new document types, new systems and new rules. Tell us what you are automating and we will show you the nearest thing we run.

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Guardrails before SAP

Validate against current SAP data before the agent commits a change.

An agent can work from synced data, apply business rules and then perform a live SAP read immediately before a governed write. If master data, status, tolerances or policy do not permit the action, the case can stop or wait for a person.

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Why this matters

Why Rules & Validation matter for agentic automation.

  • Non-negotiables stay deterministicBusiness controls such as thresholds, required fields and duplicates do not need probabilistic interpretation.
  • Limits are centrally governedChange a threshold or tolerance in policy rather than hunting through prompt text.
  • Trusted systems stay authoritativeXREF and live reads let the agent verify against ERP or master data before acting.
  • AI is used where it helpsContextual rules can coexist with exact checks instead of replacing them.
  • Freshness is explicitPolicy can require current system data at the moment a write is about to happen.
  • Exceptions are routableFailed validation can stop, notify, retry or escalate rather than simply failing the whole process.
  • Human review is policy-drivenA rule outcome can create an explicit Waiting for you state.
  • Every decision is traceableRules and policy outcomes can be logged with the same case that later executes the action.

FAQ

Common questions.

Why should rules live outside the prompt?
The Process Agent model treats policies such as thresholds, tolerances and autonomy limits as runtime-enforced controls. That makes them governed business configuration rather than values that depend on prompt wording.
What can be validated?
Artificio's rule layer can validate structured process data with required-field checks, exact matches, permissible values, date rules, duplicates, thresholds, line-item status, XREF comparisons, calculations and transformations.
Can rules compare data with SAP?
Yes. Cross-reference logic can use approved SAP or other enterprise data, and runtime policy can require a live read before a write.
What happens when a rule fails?
The process can stop, retry, route an exception, notify someone or enter Waiting for you depending on the configured rule and policy.
What is a data-freshness policy?
It allows the agent to work from cached data for speed while requiring current data (such as a live read of affected records) immediately before an external write.
Can AI-based validation still be used?
Yes. Contextual AI rules can participate in the rule sequence for checks that need semantic understanding, while deterministic controls remain explicit for non-negotiable conditions.

Security & compliance

Enterprise governance by design

Rules & Validation combines deterministic business checks with runtime-enforced policies for action approval, credential isolation, data freshness, step and retry limits, immutable versions and centrally governed thresholds.

  • HUMAN-IN-THE-LOOP
  • CREDENTIALS ISOLATED
  • FULL AUDIT TRAIL

Bring your business rules

Show us the controls your team checks before a transaction can proceed.

We'll show how to turn mandatory fields, tolerances, XREF checks, duplicate rules, SAP validations, data-freshness requirements and approval thresholds into the agent's governed execution path.

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