KLA vs LangSmith
LangSmith is excellent for tracing, evals, and annotation workflows. KLA is built for regulated workflows: decision-time policy gates, approval queues, and auditor-ready evidence exports.
Tracing is necessary. Regulated audits usually ask for decision governance + proof: enforceable policy gates and approvals, packaged as a verifiable evidence bundle (not just raw logs).
For ML platform, compliance, risk, and product teams shipping agentic workflows into regulated environments.
Ultimo aggiornamento: 17 dic 2025 · Versione v1.0 · Non costituisce consulenza legale.
A chi è rivolta questa pagina
Un inquadramento dal punto di vista dell'acquirente (non una denigrazione).
For ML platform, compliance, risk, and product teams shipping agentic workflows into regulated environments.
A cosa serve realmente LangSmith
Basato sulla sua funzione principale (e dove si sovrappone).
LangSmith is built for observing and improving LLM/agent runs: tracing, evaluation tooling, and human annotation workflows, especially when you build on LangChain/LangGraph.
Sovrapposizione
- Both help teams understand what happened in a run (inputs, outputs, metadata) and debug failures.
- Both can support sampling and evaluation loops, with different end goals (iteration vs audit deliverables).
- Both can export run data; the difference is whether it’s raw logs/traces or a deliverable-shaped evidence bundle.
In cosa eccelle LangSmith
Riconosciamo i punti di forza dello strumento, distinguendoli dai deliverable di audit.
- Developer-first tracing and debugging for agentic apps.
- Evaluation workflows, including online evaluators with filters and sampling rates.
- Annotation queues for structured human feedback on runs.
- Bulk export of trace data for pipelines and retention workflows.
- Strong fit if you are already deep in LangChain/LangGraph.
Dove i team regolamentati hanno ancora bisogno di un livello aggiuntivo
- Decision-time approval gates for business actions (block until approved), with captured reviewer context as a workflow decision record.
- A clear separation between "human annotation" (after-the-fact review) and "human approval" (enforceable gate) for high-risk actions.
- Deliverable-shaped evidence exports mapped to Annex IV (oversight records, monitoring outcomes, manifest + checksums), not just raw traces.
- Proof layer for long retention: append-only, hash-chained integrity with verification mechanics auditors can validate.
Pronto all'uso vs da costruire
Una suddivisione equa tra ciò che è disponibile come workflow principale e ciò che va assemblato tra più sistemi.
Pronto all'uso
- Run tracing and debugging for LLM/agent workflows.
- Evaluation tooling (including online evaluators and configurable sampling).
- Human annotation queues for labeling and review.
- Bulk data export of run/trace data.
- Team access controls (plan-dependent).
Possibile, ma lo costruite voi
- An enforceable approval gate that blocks high-risk actions in production until a reviewer approves (with escalation and overrides).
- Workflow decision records (who approved/overrode what, what they saw, and why) tied to the business action, not only to the run.
- A mapped evidence pack export (Annex IV sections to evidence), with a manifest + checksums suitable for third-party verification.
- Retention, redaction, and integrity posture (e.g., 7+ years, WORM storage, verification drills).
Esempio concreto di workflow regolamentato
Uno scenario che mostra dove si colloca ciascun livello.
KYC/AML adverse media escalation
An agent screens a customer, retrieves adverse media, and proposes an escalation/SAR recommendation. The high-risk action (escalation or filing) must be blocked until a designated reviewer approves.
Dove LangSmith è utile
- Debug which sources were used and why the model made a recommendation.
- Run evals to reduce false positives/false negatives and improve reviewer consistency.
- Export traces for downstream analytics and retention systems.
Dove KLA è utile
- Enforce a checkpoint that blocks escalation until the right role approves (with escalation rules).
- Capture approval/override decisions as first-class workflow records with context and rationale.
- Export a verifiable evidence bundle mapped to Annex IV and oversight requirements.
Decisione rapida
Quando scegliere l'uno o l'altro (e quando acquistare entrambi).
Scegliete LangSmith quando
- You primarily need dev tracing/evals and are not being audited on workflow decisions.
- You want a tight loop inside the LangChain ecosystem.
- Your “buyer” is an engineering team optimizing prompts and reliability.
Scegliete KLA quando
- Your buyer must produce auditor-ready artifacts (Annex IV, oversight records, monitoring plans).
- You need approvals/overrides to be first-class workflow controls, not notes in a trace.
- You need one-click evidence exports with integrity verification mechanics.
Quando non acquistare KLA
- You only need observability and experimentation tooling for non-regulated apps.
- You already have a workflow engine + ticketing + retention/signing and you’re comfortable assembling evidence bundles yourself.
Se acquistate entrambi
- Use LangSmith for dev iteration and evaluation loops.
- Use KLA to enforce runtime governance (checkpoints + queues) and export evidence packs for audits.
Cosa KLA non fa
- KLA is not a replacement for developer-first tracing/eval tooling used to iterate on prompts.
- KLA is not a prompt playground or prompt-versioning system.
- KLA is not a request gateway/proxy for model calls.
Il ciclo di controllo di KLA (Governare / Misurare / Dimostrare)
Cosa significa "evidenze di livello audit" in termini di funzionalità di prodotto.
Governare
- Checkpoint policy-as-code che bloccano o richiedono revisione per le azioni ad alto rischio.
- Code di approvazione basate sui ruoli, escalation e override registrati come record decisionali.
Misurare
- Revisioni a campione basate sul rischio (baseline + intensificate durante incidenti o dopo modifiche).
- Tracciamento dei near-miss (passaggi bloccati o quasi bloccati) come segnale di controllo misurabile.
Dimostrare
- Traccia di audit tamper-proof, append-only, con timestamping esterno e verifica di integrità.
- Bundle di esportazione dall'Evidence Room (manifesto + checksum) verificabili in modo indipendente dagli auditor.
Nota: alcuni controlli (SSO, workflow di revisione, finestre di conservazione) dipendono dal piano. Consultate /pricing?ref=confronto.
Checklist RFP (scaricabile)
Un artefatto di procurement condivisibile.
# Checklist RFP: KLA vs LangSmith Utilizzate questa checklist per valutare se gli strumenti di "osservabilità / gateway / governance" coprono effettivamente i deliverable di audit per workflow regolamentati basati su agenti. ## Requisiti essenziali (deliverable di audit) - Mappatura delle esportazioni in stile Annex IV (campi della documentazione tecnica -> evidenze) - Registri di supervisione umana (code di approvazione, escalation, override) - Piano di monitoraggio post-market + sampling policy basata sul rischio - Traccia di audit tamper-evident (verifiche di integrità + conservazione a lungo termine) ## Chiedete a LangSmith (e al vostro team) - Can you enforce decision-time controls (block/review/allow) for high-risk actions in production? - How do you distinguish “human annotation” from “human approval” for business actions? - Can you export a self-contained evidence bundle (manifest + checksums), not just raw logs/traces? - What is the retention posture (e.g., 7+ years) and how can an auditor verify integrity independently? - How do you prove that an approve/stop gate was enforced in production (not just annotated after the fact)?
Fonti
Riferimenti pubblici utilizzati per mantenere questa pagina accurata e imparziale.
Nota: le funzionalità dei prodotti cambiano. Se notate informazioni obsolete, segnalatelo tramite /contact?ref=confronto.