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AI integration

Connect the capability. Preserve the source of truth.

Werkon integrates AI into existing systems by defining who may read what, which data remains authoritative, what the model may propose, how an action is approved, and how every result is reconciled or recovered.

Integration contract

Define meaning and authority at every boundary.

The integration contract covers more than endpoints and payloads. It records the source of truth, user and service identities, allowed fields, exact actions, timing, limits, side effects, failure behavior, and the team that owns each boundary. Use software integration for deterministic system handoffs and AI agent development when the capability also needs bounded tool use.

Inputs

Systems and records
Source and destination applications, environments, APIs, events, files, databases, record identifiers, ownership, update frequency, retention, residency, and existing integrations that must remain stable.
Identity and permission
User, service, tenant, role, scopes, audience, consent, approval, credential lifecycle, secret storage, revocation, separation of duties, and the server-side checks for each operation.
Data and model contract
Field definitions, sensitivity, required and optional values, validation, provenance, freshness, transformation, model input, model output, uncertainty, version, and prohibited disclosure.
Action and operation
Read, search, draft, create, update, notify, approve, retry, cancel, compensate, reconcile, rate limits, latency, volume, service windows, monitoring, incidents, and fallback responsibility.

Outputs

Boundary map
A system-by-system view of source authority, identities, data movement, allowed actions, model exposure, approvals, side effects, owners, and trust transitions.
Versioned interface contracts
Explicit request, response, event, error, authentication, validation, compatibility, retry, rate, timeout, and deprecation behavior with synthetic contract fixtures.
Controlled integration slice
One useful path from an authorized request through current context and bounded AI work to a validated proposal, approved action where required, authoritative update, and audit record.
Operations and recovery pack
Health signals, trace context, failure queues, reconciliation reports, alerts, runbooks, replay rules, rollback or compensation, manual fallback, change ownership, and handover evidence.

Integration path

Earn write access after the read path is understood.

The first production-shaped slice starts with narrow, observable access. Write authority is added only after data meaning, identity, model behavior, validation, approval, and recovery are proven together.

  1. 01

    Map authority and contracts

    Identify each authoritative record, user and service identity, permitted operation, data owner, interface contract, environment, limit, dependency, side effect, and accountable operator.

  2. 02

    Prove read-only context

    Retrieve the minimum current data under the requesting identity, preserve provenance, validate tenant and record scope, redact prohibited fields, and test denied and missing access.

  3. 03

    Add bounded AI work

    Pass only the approved context to the evaluated capability, validate its structured output, attach uncertainty and supporting records, and route rejected or incomplete results explicitly.

  4. 04

    Control action and recovery

    Recheck current state and permission before action, require approval where needed, protect against duplicate effects, record the change, and test timeout, partial failure, replay, compensation, and reconciliation.

  5. 05

    Release and own the boundary

    Stage volume, watch contract health and human burden, exercise provider and system outages, manage interface and model changes, keep a manual path ready, and hand operations to named owners.

Integration pattern

Choose the boundary that makes ownership clearest.

The simplest connector is not always the simplest operation. The pattern should fit source capability, coupling, timing, side effects, security, failure isolation, and the teams that will maintain it.

01One stable operation and clear owner

Direct API

Call a documented system interface directly when the contract is stable, the operation is narrow, latency matters, and the product team can own authentication, compatibility, retries, and support.

Evidence: Published contract, scope, audience, test environment, error model, limits, version policy, owner, dependency test, and outage path.

02Several systems need translation

Integration layer

Use a dedicated service when orchestration, normalization, policy, observability, vendor isolation, or reuse would otherwise be duplicated across product surfaces or model tools.

Evidence: Canonical model, transformation rules, ownership, access boundary, trace path, scaling assumptions, compatibility tests, and independent deployment plan.

03The work can complete later

Asynchronous event

Use durable events or queues when producers should not wait, work may take time, traffic is uneven, or downstream failure must be isolated and retried without repeating upstream activity.

Evidence: Event contract, identity, ordering need, deduplication key, retention, retry and dead-letter policy, consumer owner, replay test, and reconciliation view.

04The exception needs judgment

Human work queue

Create an owned review item when data conflicts, authority is unclear, the consequence is high, recovery is unsafe, or automation would hide more effort than it removes.

Evidence: Queue owner, context, priority, service expectation, decision options, audit, escalation, workload measure, and route back into the authoritative flow.

Boundary controls

Treat every connector as a trust transition.

Data crosses ownership, identity, and failure boundaries during integration. Each transition needs a smaller contract and less authority than the broad system account that may be easiest to obtain.

Identity survives the hop
Carry or derive the correct user, service, tenant, purpose, and approval context. Restrict tokens to the required audience and operation, store credentials outside model context, and test revocation and denial.
Contracts fail closed and visibly
Validate required fields, types, values, versions, freshness, record scope, and output before use. Reject incompatible or ambiguous state into an owned path rather than guessing or silently dropping it.
Side effects are replay-safe
Use stable identifiers, idempotency, current-state checks, transaction boundaries, ordering controls, and compensation where supported. Reconcile intended and recorded outcomes after uncertain completion.
Operation spans both systems
Trace requests across boundaries without logging sensitive payloads, monitor contract and queue health, alert accountable owners, test dependency outages, and keep recovery and manual fallback usable.

Engagement fit

Use AI integration when an isolated capability must become accountable product behavior.

Good reason to begin

  • A model, agent, or AI-assisted feature needs current data or controlled actions from existing systems to produce a useful result.
  • The source of truth, data owners, user or service identities, approval roles, interface owners, and operating team can participate.
  • A narrow read-first slice can prove access, meaning, model behavior, and failure handling before consequential writes are added.
  • The organization is prepared to own contract changes, credentials, monitoring, reconciliation, incidents, and provider or system outages after release.

Resolve before beginning

  • The requested work is primarily a manual file transfer or one-time migration rather than an ongoing product integration.
  • No system is accepted as authoritative, record identifiers conflict, or the meaning and sensitivity of required fields remain unresolved.
  • The proposed connector depends on shared broad credentials, cross-tenant access, hidden model exposure, silent consequential writes, or no recoverable failure path.
  • Target systems lack a supported interface and there is no approved, maintainable alternative for exchanging the required data or actions.

Source basis

Sources behind the control model.

  • 01

    RFC Editor

    RFC 9700: Best Current Practice for OAuth 2.0 Security

    The 2025 IETF best current practice covers current OAuth threats and recommends restricted token privileges, narrow resource audiences, stronger client authentication, replay protection, and secure transport.

  • 02

    NIST AI Resource Center

    AI RMF Core

    Current voluntary guidance includes post-deployment monitoring, user input, appeal and override, incident response, recovery, change management, and decommissioning.

[ WORKFLOW / SYSTEMS AUDIT ]
THE FIRST ENGAGEMENT

Start with one real workflow

A Systems Audit is the usual starting point. If the opportunity is already clear, we can move directly into a focused build.

Show Us the WorkflowStart with the free automation readiness checklist

OBSERVEQUANTIFYDECIDEBUILD

Continue with the work

Choose the next useful path.

Stay with the operating question. The technology can wait until the work is clear.

01

Explore AI services

Compare integration with consulting, custom AI, agents, workflow automation, models, language, vision, deployment, and support responsibilities.

Open path02

Build a custom AI system

Own the wider application path when integration is one responsibility inside a new AI-enabled product or internal tool.

Open path03

Automate a controlled workflow

Design the complete operational flow when system connections support a repeatable sequence of rules, model work, approval, and exceptions.

Open path04

Build a permission-aware AI agent

Use an agent when one bounded task needs variable planning and several permissioned tools behind the integration boundary.

Open path