AI assistant: Reference architecture

Combines the required frontend, backend, data, security, integrations, and operational patterns for the AI assistant solution. It fixes adoption boundaries and observable failure evidence while keeping environment-specific limits configurable.

Package status: reference context ready for human review. The contract and test scenarios are complete, but no claim is made that an adopting implementation has passed them.

Decision

For AI assistant, adopt a bounded product scope and an explicit composition of independently adoptable blocks. Use one owned boundary, explicit state model, and versioned interfaces; keep policy decisions separate from adapters and side effects. This architecture block focuses specifically on reference architecture. This package is a reference contract: the adopting team must choose numeric limits, providers, jurisdictions, and operational owners from evidence in its own environment.

Scope

• The AI assistant actors, inputs, outputs, states, policy or schema versions, and externally visible outcomes required for the reference architecture focus of this architecture block.
• Primary and alternate interfaces, background work, caches, integrations, support paths, and evidence that can exercise the same boundary.
• Adoption-specific configuration, rollout, recovery, and verification responsibilities needed to apply the reference safely.

Outside this block

• Choosing a universal vendor, framework, jurisdiction, numeric threshold, retention period, or service-level objective for every adopter.
• Claiming that packaged scenarios have run against a downstream implementation or that this reference grants legal, security, or accessibility certification.

Contract

• Identify target actors and core journeys before selecting infrastructure; every included block traces to a journey or cross-cutting risk.
• Separate MVP commitments, later capabilities, and non-goals so the starter does not imply an unlimited product scope.
• Define service, data, permission, integration, and operational boundaries without forcing one vendor or deployment topology.
• List the verification and launch evidence required from each composed block and keep its review status visible.
• Document component ownership and allowed dependency direction, including which data may cross each boundary.
• Make synchronous, asynchronous, and batch paths implement the same authority and state invariants.
• Scope the starter to named user journeys, conversation state, grounded knowledge, bounded tools, structured outputs, approval points, privacy controls, and evaluation evidence.
• Keep autonomous high-impact actions, unrestricted memory, unsupported claims, universal model fallback, and domain certification outside the pack unless separately governed.
• For the reference architecture variant, enforce this declared boundary: combines the required frontend, backend, data, security, integrations, and operational patterns for the ai assistant solution.

Implementation guidance

• Start with the domain state and interface contract, then attach adapters, storage, queues, and provider-specific code.
• Model AI assistant inputs, outputs, actors, states, invariants, side effects, and evidence before selecting framework or vendor details.
• Store the applicable AI assistant contract or policy version with material state so migrations, replay, and support decisions remain attributable.
• Introduce the path behind controlled rollout, compare expected and observed outcomes, and keep a tested recovery route until adoption evidence is complete.

Failure handling and safeguards

• If a required block or launch control is unresolved, the pack reports the gap and does not label the solution production-ready.
• Invalid or contradictory AI assistant state is rejected or quarantined; the implementation never guesses a value that broadens authority or duplicates an effect.
• Retries are bounded and use durable identity; after exhaustion, work reaches an inspectable terminal state with an accountable owner.

Verification and operations

• Exercise the contract through each adapter and prove that a failed dependency cannot bypass the authoritative boundary.
• Monitor AI assistant success, denial, validation failure, dependency failure, retry exhaustion, and recovery by contract version without sensitive payload dimensions.
• Re-run paired positive and negative fixtures after policy, schema, dependency, migration, or boundary changes and preserve the resulting evidence.

Adoption assumptions

• Names and values in the example are a concrete fixture, not universal defaults; adopters replace them through documented evidence and ownership.
• The adopting system can provide authenticated identity, durable operation or record versions, bounded telemetry, and a controlled path for change.

The executable-looking examples in this package are fixtures and acceptance contracts. Run the collection validator to check structure and metadata, then translate and execute the scenarios in the target repository before recording implementation evidence.