Nexthink vs Serval (2026)

Remediations run on the employee's own device, and Spark makes an authenticated round trip into the customer's ITSM. Its actions, remote actions, agent actions, workflows and others, run during a conversation through the Nexthink Collector, with their results fed into the next turn, so the changes land in the customer's estate, not in Nexthink's own records. ServiceNow is named for knowledge-base and ticket indexing and for the service request catalog.

Spark retrieves past incidents from a third-party ITSM system and raises escalated tickets back into it for the employee, with a short description, the actions taken and the conversation transcript, and it can retrieve and link to third-party data when the customer asks. Underneath sit a native connector system, published APIs and the Nexthink Query Language, and Amplify brings DEX context and remediations straight into the ITSM console. Spark is also embedded in tools such as MS Teams and ServiceNow Virtual Agent.

Sourcedocs.nexthink.com/legal/global-ai-hub/spark-ai-model-card and nexthink.com/platform/integrations-infinityread 2026-09-12

The integration catalog runs to 164 documentation pages, indexed at docs.serval.com/llms.txt, with a page per connector. Ticketing covers ServiceNow, Jira, Zendesk and Freshservice, bidirectionally, so a customer can keep existing tooling during migration. Identity covers Okta, Microsoft Entra, LDAP and Active Directory, plus SailPoint for governance and RSA SecurID for MFA. Endpoints run through Jamf and Tanium and HR through Workday.

Knowledge comes from Notion, Confluence, Google Drive and Slab, collaboration from Slack, Microsoft Teams and Google Workspace, and finance and operations connectors include Coupa, Salesforce, LaunchDarkly, Vanta and Presidio. Connectors take authenticated action rather than mirroring data.

For RSA SecurID, Serval stores the client key encrypted and attaches it automatically to every call it makes on the customer's behalf, and workflows drive a full MFA flow, starting, verifying, checking and canceling attempts on demand. For Presidio, Serval exchanges credentials for a short lived JWT and reads asset records on the customer's behalf whenever a Serval workflow runs, with the regional endpoint chosen by the workflow.

Both happen at execution rather than by background sync. Provisioning is the heaviest write path, with automated provisioning and deprovisioning via SCIM, direct API or custom workflows, and just in time access grants revoked automatically when the window ends. Access can also be revoked via API. Serval's AI knows the endpoint catalog and can help build workflows against it. Connector maturity is labeled, with RSA marked Beta in the integrations UI.

Sourcedocs.serval.com RSA SecurID and Presidio integration pages, with the integrations indexread 2026-09-12

Spark picks its own tools in a loop until it needs the employee. Its LLM can select one or more tools to gather information, querying device and user data, connecting to local data sources through the Collector, searching knowledge articles, the service request catalog and the available remote actions, agent actions and workflows, and retrieving past incidents from ITSM.

It then offers or executes actions, escalates or ends the conversation, repeating the cycle until it needs input from the employee, so tools are chosen at run time, not along a coded branch. Nexthink calls Spark an agentic AI resolution engine that understands employee issues in natural language, evaluates DEX data and system context, and executes IT-approved actions to resolve issues on its own.

Flow adds low-code, event-driven workflows that customers write to detect, communicate, integrate and act, and remote actions, agent actions and workflows are configuration objects the customer creates and enables. Workspace serves as a central agentic cockpit for IT.

Each participant has a distinct role, from the employee who asks and the administrator who sets the approved actions to the service desk agent who receives an escalated ticket with the transcript and actions taken, as in an example that runs from a slow-laptop complaint through diagnosis to remediation or escalation.

Sourcedocs.nexthink.com/legal/global-ai-hub/spark-ai-model-card, /platform/user-guide/spark and nexthink.com/platform/flowread 2026-09-12

A workflow can call another as a step through child workflow composition, workflow journeys can be viewed as dependency graphs, and workflows can be organized into folders, shared across teams, tagged, searched semantically and versioned with diffs. Temporal is listed among the required datastores as the workflow orchestration component. It is a durable execution engine, so a run survives process restarts and resumes, which holds long running, multi step work together when people interrupt it.

Workflows can pause for approval, and Serval also has conditional forms in Slack, criteria based assignment rules, a default triage team for ambiguous requests and ITIL aligned change management. The Automation Agent turns natural language descriptions into TypeScript, so automations build themselves from a description. Serval markets deterministic workflows rather than execution the model improvises, since the model writes the workflow and the workflow then runs the same way every time. Workflows and skills are managed as code in Git with version diffs.

The participants include the employee raising a request, three named agents with different jobs, approvers who can be delegated to or overridden by a team manager, assignment groups and external ticket subscribers. Work spans ticketing, just in time access, asset management, onboarding and offboarding, compliance workflows and campaigns, with Security, IT, HR, Finance and Operations each able to run isolated environments on the same platform.

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Employees reach Spark in Teams and other enterprise chats, including MS Teams, ServiceNow Virtual Agent, Moveworks and Amazon Connect, with further reach through APIs and the A2A protocol into whatever chat front end the customer already runs. Nexthink ships its own Microsoft Teams application and works in LLM-supported languages, with English or Japanese as the tenant fallback.

Separately, Flow runs event-driven workflows fired by real-time alerts from endpoint telemetry, not by an employee starting a conversation, and alerting supports proactive IT management. Work therefore arrives two ways, from an employee asking in a channel and from a condition detected on a device. Outbound messaging and surveys reach employees in-app, and Spark sends progress updates and questions during a resolution, not only a final answer.

Sourcedocs.nexthink.com/legal/global-ai-hub/spark-ai-model-card and nexthink.com/platform/flowread 2026-09-12

Workflows can be triggered by events and by webhooks, which let any external system act as an event source, as well as by Slack reactions, SLAs and ticket assignment, and campaigns can be triggered directly from a workflow. Child workflow composition lets one flow start another, and tickets, access requests and onboarding events all start work. Schedules are a separate mechanism, covering recurring campaigns, assignment schedules, SLAs with business hours and pause conditions, and automatic ticket bumps for unresponsive requesters, a time based trigger acting without a person.

The Help Desk Agent covers intake and resolution across Slack, Teams, email, phone and portal. Further channels include an embedded widget with a live agent and multiple email intake addresses per team, along with Microsoft Teams notifications and reply routing improvements, conditional forms in Slack, dynamically rendered forms, a Serval Inbox consolidating notifications, and white label Slack bot customization. The UI ships in nine languages with on demand ticket translation.

Sourcedocs.serval.com release notes index and serval.com/insights buyer's guideread 2026-09-12

Spark retrieves from an index of the customer's own knowledge base, past tickets and resolution notes, alongside live device telemetry. Its sources include knowledge base articles the customer imports into Nexthink Infinity, past ticket insights, and human-authored resolution notes from third-party systems that help Spark reduce escalations. ServiceNow knowledge base articles and tickets are indexed for retrieval, and manually uploaded files are indexed too, so the index stands between runs.

On each run, Spark's LLM picks the tools it needs to search knowledge articles and the service request catalog and to pull past incidents from a third-party ITSM system, suggesting remediations from notes written by human agents. As an optional fallback that an administrator must enable, Spark can search a curated list of trusted websites chosen by Nexthink when customer-specific sources fall short. Users can see the sources behind a response to check its accuracy.

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A native knowledge base, in beta, sits alongside the synced stores and is maintained in Serval, with knowledge base management, knowledge tagging within guidance, lexical and keyword search alongside semantic retrieval, cross team KB sharing, and a tag catalog with owners set for teams, resources and roles. Answers ground in help docs built in Serval or synced from Notion, Google Drive, Confluence and Slab, and reviewers can publish approved knowledge updates into Notion, Slab, Confluence, Freshservice and ServiceNow, so corrections flow back out to the systems of record.

Retrieval respects permissions at read time through knowledge source permission enforcement, so an employee cannot be answered from a document they are not entitled to see. The Help Desk Agent can also search the web, tickets, assets and access history are queryable alongside, and the Insights Agent analyzes ticket patterns. Serval's asset estate tracks hardware, software licenses, SaaS subscriptions and cloud costs in one schema the agent reasons over.

Sourcedocs.serval.com release notes indexread 2026-09-12

Working notes last only for the conversation, and no memory carries over between conversations. Spark writes internal notes, from the user, to keep context through multi-step interactions, and in A2A integrations context is carried by reusing contextId and taskId values within a conversation thread. Device timelines and endpoint state are the customer's operational data, held because monitoring devices is the product.

Spark's improvement over time comes from supervisor feedback used in reinforcement learning, which is learning folded into the model. The 30 day conversation previews and 13 month summaries are audit records kept for supervisors. Nexthink's AI model card lists every input, output, step and storage period Spark uses and names no persistent agent memory, preference store or decision trace across conversations.

Sourcedocs.nexthink.com/legal/global-ai-hub/spark-ai-model-card and /platform/configuring_nexthink/.../spark-agent2agent-integrationread 2026-09-12

Per team guidance, knowledge sources and configuration persist in Serval, but none of these is a memory that learns per user over time, and no agent memory is described. Guidance, now called Skills, is a set of written procedures and runbooks the customer authors so agents follow internal practice, with a diff view for suggested changes and version history. It persists because a person wrote it down, not because the agent retained anything.

The native knowledge base and the corpora synced from Notion, Confluence, Google Drive and Slab are the knowledge layer, and workflows, skills and settings managed as code in Git are authored artifacts. The runtime stack uses PostgreSQL as the primary application database, Redis for caching and ephemeral state, and Temporal for workflow orchestration.

Temporal keeps a workflow run alive through restarts, but that is state within one execution, and no component in the stack holds what an agent learned. There is no named memory layer, no retained preference the agents reapply for a user or requester, no decision trace carried between tickets, no stated scope or lifetime for anything held, and no customer surface to inspect, edit, export or purge agent state apart from tickets, skills and knowledge. Ticket history is a record of work.

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Before an impactful fix runs, the employee sees its impact and has to answer yes, and this consent step is on by default. Spark runs only within IT-defined guardrails using approved actions, and for actions an administrator flags as needing employee consent, such as remediations with an impact, Spark shows the impact from the action configuration and runs the action only after an explicit yes. Spark always asks for user approval before taking issue-resolution actions unless an admin has explicitly allowed actions to run without consent.

Customers vet and select which remote or agent actions Spark can use, and an action is available only if it was enabled during Spark configuration. Spark escalates a ticket to the support team only after the employee confirms, and supervisors review interactions for quality and give feedback. Inference passes through AWS Bedrock Guardrails, which filter six harm categories and block prompt attacks, and Spark cannot answer about other users or their devices.

Sourcedocs.nexthink.com/legal/global-ai-hub/spark-ai-model-card and /platform/user-guide/sparkread 2026-09-12

Workflows can pause for approval, so execution halts mid flow and waits for a person, and customers author these gates in a redesigned approval flow builder. Around them sit custom approval workflows, natural language workflow approvals, approval delegation, team managers able to override approvals from a ticket, and an approvals API for custom follow up logic. Multi step approvals apply to workflows and access requests alike.

Access requests route through approval before a grant is made, just in time access is time bound and revoked automatically when the window ends, and the access history export records the approver and the justification against every grant. Where approval is not requested, guardrails apply. Serval markets deterministic workflows with security layers, namely RBAC, approval gates, API scope limits and an air gap between help desk and builder.

That air gap means the agent that talks to employees cannot edit workflows or integration scopes, so a compromised conversation cannot widen the agent's own authority. Skills and runbooks hold agent behavior to the team's written procedures, with a diff view for reviewing changes the AI suggests before they take effect.

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Admins use role-based access control to give specific users or groups access to Spark's AI features while restricting others, and employee access is controlled through the admin console of the channel Spark is embedded in. Secure access tokens are used, and an employee can reach only their own devices and data. Data moves over HTTPS and is encrypted with AES-256 at rest, personal data fields are removed automatically when NQL queries are generated, retention is configurable by the customer, and a DPA is available.

The trust center shows a certification strip with the AICPA mark and four more certification images labeled only Certification 1 through 4, so those standards cannot be named. The CEO's signed statement names compliance with GDPR, CCPA and the EU AI Act, and a dedicated AI Compliance Team of legal, privacy and security experts reviews each AI component. The Security Hub at security-hub.nexthink.com is where the four unlabeled certifications would be named.

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Serval holds SOC 2 Type II, states adherence to HIPAA and GDPR, and documents HIPAA-oriented deployment and data controls. It points customers to its Trust Center for the current Type II attestation report, and a written BAA covers HIPAA workloads and a DPA covers GDPR. Access management is centralized, with SAML and SCIM for SSO and user provisioning, role based access control for least privilege permissions, customizable API scopes for app integrations, and multi step approvals for workflows and access requests.

Further controls include custom roles, scoped API keys, knowledge source permission enforcement, ticket visibility groups for access controlled routing, a read only agent role, default access profiles and app owners, skills gated by access profile, and sharable access reviews. Separation of duties takes the form of an air gap between help desk and builder, so the help desk agent that talks to employees cannot edit workflows or integration scopes or change what agents are allowed to do.

Sourceserval.com/updates/soc2, /insights/hipaa-compliant-itsm and the serval.com platform pageread 2026-09-12

Audit logs cover Spark's conversations, decisions and remediations, and admins define approved actions, workflows and governance policies and review those logs. Spark can log its internal reasoning and processes for supervisor review without sharing them with employees, and reasoning details for supervisors are a standard system output. Escalated tickets carry the actions taken and the conversation transcript to the service desk.

Full conversation previews, including employee PII, are kept for 30 days and conversation summaries with a user reference for 13 months, under retention settings the customer configures. Service desk supervisors see Spark-employee conversations and resolution-performance metrics, and employees can check the sources used to build a response. Nexthink's real time observability across devices, networks and applications covers the customer's estate and is a separate product from Spark's own logs.

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Every workflow run is logged step by step with inputs, outputs, status and duration. Serval offers a sample workflow run export with per-step detail and timestamps and an access history export with approver, justification, start/end and revocation reason, and its governance guidance tells customers to confirm that logs include per-step inputs and outputs, precise timestamps and CSV/JSON export for GRC integration.

An access record carrying the approver and the justification is an audit trail of a consequential agent action with the human decision attached. SIEM integration carries it into the customer's security estate, so evidence is generated when work happens rather than reconstructed before an audit.

Alongside sit agent powered run debugging, version diffs for workflows and skills, a guidance diff view for reviewing AI suggestions, automation suggestion history and workflow journeys viewable as dependency graphs, so a reader can see what ran, what changed and what the agent proposed.

Dashboards on request volume, time to resolution and SLA compliance measure the help desk operation, AI resolution rates show what the agent itself did, and the Insights Agent reports on ticket patterns and automation opportunities. Analytics dashboards are in public beta, with a refreshed home.

Sourceserval.com/insights SOC 2 governance page, with docs.serval.com release notesread 2026-09-12

Customer data, including AI processing, stays in the customer's region. All data is stored in the customer's region, Spark uses GenAI models that Nexthink operates in its own AWS environment in that region, and AI processing on AWS takes place in the AWS region aligned with the customer's Nexthink deployment. No region list, named customer environment or way to choose a region is published. Deployment is cloud only on AWS, the Collector is an endpoint agent that feeds that cloud, not a deployment option, and there is no self-hosted or on-premise option.

Sourcedocs.nexthink.com/legal/global-ai-hub/spark-ai-model-card and nexthink.com/trust-centerread 2026-09-12

Serval comes in four delivery models. Fully managed Serval Cloud has no infrastructure to manage, automatic updates and a 99.9% uptime SLA. In Hybrid Self-Hosted, the customer runs only the Serval worker inside its own infrastructure while Serval operates the backend, control plane, web app and database, so the worker is the only Serval component that ever touches the internal network or handles integration credentials.

Fully self hosted comes in two operating modes, Managed in Your AWS and Self-Managed on Your Kubernetes. It is single tenant in an AWS account the customer owns, the application runtime and all customer data remain in that account, and the customer's AWS credentials never leave its environment. Either the customer's team or Serval operates it, and the deployment is identical either way.

The deployment bundle holds Terraform, Kubernetes and Helm configuration and setup scripts, and the AWS stack is EKS, Aurora PostgreSQL, ElastiCache, OpenSearch, S3 and Secrets Manager. Customers can point at their own PostgreSQL, Redis, Temporal, OpenSearch or S3 compatible storage, with the upgrade cost of doing so spelled out, and DNS requirements go down to the app and API hostnames.

Private network patterns include Site-to-Site VPN, Direct Connect, Transit Gateway and private DNS resolution. The cloud product also offers a region choice, with EU data residency available to organizations, and Microsoft GCC High is supported for US government workloads. Installation and day two operations sit in a password protected area shared with customers and evaluators, while everything needed to evaluate the models is public.

Sourcedocs.serval.com self-hosting section and release notesread 2026-09-12

Nexthink's automation catalog of remote actions, agent actions, workflows and others is one of Spark's knowledge sources, and customers vet and select which of them Spark can use, since actions are available only if enabled during Spark configuration. Each remediation is a unit a customer switches on and Spark uses when it fits, and removing one leaves the rest working.

A customer-facing Library is published at nexthink.com/library and promoted in the site navigation, though its size and whether entries come from Nexthink or the community are not described. Flow is a low-code engine for building further workflows, and the platform is sold as Infinity plus named expansion modules and named AI agents.

Sourcedocs.nexthink.com/legal/global-ai-hub/spark-ai-model-card and nexthink.com/libraryread 2026-09-12

Three named agents ship, each doing a different job. The Help Desk Agent handles intake and resolution across Slack, Teams, email, phone and portal, the Automation Agent turns natural language descriptions into TypeScript, and the Insights Agent detects patterns and suggests automations. Without the Insights Agent the help desk still resolves tickets, and without the Automation Agent the other two still work.

A browsable catalog of installable workflow bundles sits alongside them, with packs for specific estates such as LDAP/AD, Okta and Google Workspace, plus workflow backed catalog items and service catalog ingestion. An installable Okta workflow does work when selected, and removing it leaves the others whole. Workflows can be organized into folders, shared across teams, searched semantically and tagged. A native knowledge base and Skills add reusable content, though the customer writes those. The catalog comes from Serval, with no third party or community contribution path and no marketplace of externally built agents.

Sourceserval.com/insights buyer's guide, first party about Serval, with docs.serval.com release notesread 2026-09-12

Spark uses vetted, fine-tuned off-the-shelf LLMs, including Meta Code Llama and Claude by Anthropic, and runs agentic reasoning through AWS Bedrock LLM APIs, with Bedrock as the hosting and inference layer, not a separate model. Nexthink picks the model for each component, and there is no model picker, admin setting or bring your own key option. Nexthink does not train models across customers, builds no global training set from customer interactions, and shares no customer or personal data with the AI providers themselves, such as Anthropic or Meta.

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Serval uses two model providers and splits the work between them, and the customer does not choose. In self hosted deployments, the customer provides its own keys for the providers Serval uses at runtime, an Anthropic API key as the primary provider for AI agents and help desk functionality and an OpenAI API key for embeddings, image description and specific model tasks. In Managed in Your AWS, the customer owns the AI provider keys and any customer-owned credentials supplied during onboarding, and an in product surface manages Anthropic usage.

The customer therefore owns the provider relationship, sees the spend and controls the data path to the model, but that is control over credentials, cost and data path, not model selection. Serval decides which provider handles which task, and there is no model picker, per agent model assignment, version pin or way to substitute another provider for either role. The key arrangement applies to the self hosted and hybrid models, and nothing says a Serval Cloud customer supplies keys.

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Through the Spark Agent2Agent integration, outside agents can discover and invoke Spark over A2A, an open standard, to query device and user insights, run safe guardrailed actions, receive asynchronous callbacks and present clear outcomes. Spark exposes its agent card to a primary AI agent, which routes the employee request to Spark as a subagent, with contextId and taskId carried across turns and responses returned to a configured webhook over authenticated service-to-service communication.

A step-by-step setup guide covers this public vendor-neutral API. The platform documentation at docs.nexthink.com includes a legal and AI hub and an llms.txt index at docs.nexthink.com/legal/llms.txt with markdown versions of each page, alongside the Nexthink Query Language, published APIs and Amplify, which brings Nexthink context into the customer's ITSM. Spark also runs inside outside systems such as MS Teams and Moveworks, which call Nexthink.

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A large API lets outside callers reach Serval. The documentation index at docs.serval.com/llms.txt lists three sections, Documentation at 142 pages, Integrations at 164 pages and an API reference at 252 pages, and two OpenAPI documents, auth-openapi-spec.yaml and openapi-spec-v2.yaml, are served from the API reference section. Scoped API keys and customizable API scopes for app integrations grant access per integration rather than all or nothing.

An approvals API handles custom follow up logic, and access can be revoked via API and workflows, so an outside caller can drive a consequential action inside Serval. Beyond the REST API there is a CLI, which can raise access requests, and user MCP, which puts Serval inside the customer's own assistant. Webhook triggered workflows cover the event side, and workflows, guidance and configuration are managed as code in Git repositories.

Sourcedocs.serval.com llms.txt index, OpenAPI spec listings and release notesread 2026-09-12

Nexthink validates Spark model updates against test datasets, using precision, recall and proprietary in-house metrics suited to each part of the system, so a change is tested and the results can be compared. A post-deployment loop runs on top for each customer. Supervisors assess Spark conversation logs and give feedback that feeds reinforcement learning cycles, and that learning is used only for the customer's own employees, not pooled.

The system tracks resolution success rate and escalation frequency, and supervisors get resolution-performance metrics. DEX scoring, benchmarking against fifteen million endpoints and AI adoption measurement look at the customer's estate, not the agent. The precision-and-recall harness is Nexthink's own and gates its model updates, and no sandbox where a customer can test a changed action or workflow before it reaches employees is described.

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Help Desk Agent evaluations are in beta, a harness aimed at the agent's own behavior. What an evaluation produces is not stated, with no score, pass rate, judge verdict or comparison format. Workflows and skills are managed as code in Git with version diffs, so a change can be reviewed before it lands.

Agent powered run debugging lets a failed run be inspected, and a guidance diff view for reviewing AI suggestions gates the agent's own proposed changes to its instructions. Serval markets deterministic workflows, so behavior repeats between runs.

Its commercial claim is a guaranteed automation rate, citing customer automation percentages, which measures how much of the customer's ticket volume the agent cleared.

Sourcedocs.serval.com release notes indexread 2026-09-12

Devices are fixed through scripted actions run by an endpoint agent, not by operating a screen. Spark runs remote actions, agent actions, workflows and others through the Nexthink Collector on the device and connects to local data sources through the Collector for troubleshooting.

This is machine-to-machine execution against processes, services, configuration and files, not windows and buttons, so a changed screen layout does not affect it. No browser session, desktop control or RPA fallback appears among Spark's inputs, outputs and data flow.

Spark can search a curated set of external web resources when customer knowledge falls short, which is retrieval from a list Nexthink curates.

Sourcedocs.nexthink.com/legal/global-ai-hub/spark-ai-model-card and /platform/user-guide/sparkread 2026-09-12

Integrations and APIs are how Serval acts, not driving a browser or a computer interface, and every connector works through an API. For RSA SecurID, Serval stores a client key and attaches it automatically to every call it makes on the customer's behalf, and for Presidio it exchanges credentials for a short lived JWT and sends it as a bearer token on every request. Provisioning runs via SCIM, direct API or custom workflows. Across 164 connectors the pattern is authenticated service to service calls, and the self hosted worker exists to reach internal APIs, LDAP and Active Directory over the network, not to operate a desktop.

Serval markets deterministic workflows compiled to TypeScript. It integrates Jamf and Tanium and runs device actions across managed endpoints, with every grant, device action and workflow run exportable, and those actions go through the MDM and endpoint platforms' own APIs. The CLI, which can raise access requests, is a way for people to reach Serval, not Serval operating anything. There is no hosted or local browser, desktop session, remote control, RPA or recorder.

Sourcedocs.serval.com RSA SecurID and Presidio integration pages, with the release notes indexread 2026-09-12