Long-term memory: Evaluation, cost, and operations

Covers evaluation cases, quality metrics, latency and cost limits, monitoring, fallback, and incident handling for long-term memory. 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 Long-term memory, adopt a versioned orchestration policy and an explicit model-call boundary. Treat the block as a release and regression contract built from representative positive, boundary, denial, failure, and recovery cases. 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 Long-term memory actors, inputs, outputs, states, policy or schema versions, and externally visible outcomes described by this testing 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

• Persist the prompt, tool, retrieval, model, and policy versions needed to reproduce or explain each material outcome.
• Validate structured output before any side effect; malformed or incomplete output is an error, not partial authority.
• Separate untrusted user or retrieved content from system policy and label each context item with origin and trust class.
• Bound tokens, tool calls, latency, and spend per request; crossing a bound follows a named fallback instead of continuing unbounded.
• Record fixture version, environment, configuration, observed result, and evidence location for every decision-bearing run.
• Separate product failure, dependency failure, test-infrastructure failure, and inconclusive evidence in reports.
• Store only user-authorized durable facts with source, subject, purpose, confidence, visibility, creation and review times, expiry, and policy version.
• Let users inspect, correct, forget, and limit memory, re-authorize it for the current task, and prevent summaries or inferred traits from becoming silent permanent truth.

Implementation guidance

• Build the smallest deterministic fixture set that spans risk classes, then add production-derived cases only after privacy-safe review.
• Model Long-term memory inputs, outputs, actors, states, invariants, side effects, and evidence before selecting framework or vendor details.
• Store the applicable Long-term memory 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

• Provider failure, unsafe output, or exhausted budget returns a bounded fallback and retains correlation-safe diagnostic evidence.
• Invalid or contradictory Long-term memory 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

• Run the suite twice from a clean state and confirm that ordering, retries, and parallel execution do not change the verdict.
• Monitor Long-term memory 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.

References

• NIST AI 600-1, Generative AI Profile (applies as of 2026-09-08)