Represent model APIs, workflow builders, business systems, collaboration channels, internal services, and custom agents in one operating model.
Administrators control installed versions, required configuration, credentials, agent assignments, runtime permissions, and publication state.
Choose administrator-managed service credentials or encrypted per-user connections when the connector supports user-owned authentication.
Pristan treats connectors as reviewed, versioned capabilities. What a user can see in chat is computed from the installed package, active version, agent assignment, user access, and credential model.
Install an exact reviewed connector version and keep publication, activation, upgrade, and rollback decisions explicit.
Assign approved connector operations to the agents that need them instead of exposing every integration everywhere.
When a connector declares per-user authentication, people connect their own account and Pristan keeps that credential private to them.
Connector operations run through platform policy, egress controls, validation, timeout and retry boundaries, and audit events.
Configure provider connections and invocation templates for the model services your teams use. Availability depends on the installed templates, credentials, network path, and provider configuration in your environment.
Represent commercial model APIs through supported provider connections and templates, including OpenAI-compatible endpoints where configured.
Configure enterprise-cloud model connections such as Azure OpenAI, AWS Bedrock, or Google Vertex AI when the corresponding template and credentials are available.
Register self-hosted or local runtimes through a compatible connection pattern and keep their hosting and ownership context with the agent.
Register OpenAI-compatible endpoints, internal inference services, and purpose-built model gateways without special casing each team.
Visual builders and orchestration frameworks create real agents, even when they never appear in SaaS discovery. Pristan registers them with the same owner, lifecycle, access, and evidence model as purchased AI tools.
Catalog n8n AI workflows and chat-triggered automations so owners, reviewers, and security teams can see what each workflow touches.
Register Dify chatflows, workflows, and RAG apps as governed agent records with owner, purpose, access, and environment metadata.
Bring visual AI flow builders like Langflow and Flowise into the same governed catalog as custom REST agents.
Retrieval systems are part of the agent risk surface. Pristan tracks which agents use which knowledge sources, where that data lives, and who is accountable for keeping it approved and current.
Govern embedding providers such as OpenAI, Cohere, Voyage, Vertex AI, Azure OpenAI, and similar services.
Connect vector databases, search indexes, and RAG pipelines back to the agent records that depend on them.
Track self-hosted embedding models and local retrieval services when teams keep sensitive knowledge inside private infrastructure.
Approved agents often meet employees inside chat tools. Pristan keeps those channel experiences tied to the catalog, so access, ownership, and audit evidence do not disappear inside a workspace.
Expose approved agents in Slack channels and DMs while keeping each bot tied to an accountable catalog record.
Register Teams-based agents, tenant workflows, and business chat experiences under the same governance controls.
Connect internal apps, approval flows, and notification channels without turning each one into a separate governance process.
Runtime signals are more useful when they connect back to the governed agent record. Pristan links traces, metrics, alerts, and audit exports to the owner, purpose, policy, and review history of each agent.
Use Pristan's OpenTelemetry, Prometheus, health, usage, and event surfaces to review supported runtime and platform signals.
Export ownership, access, lifecycle, usage, and review history for security, compliance, and management reporting.
Export or scrape supported telemetry through OpenTelemetry and Prometheus-compatible paths, then route it into the monitoring stack selected for the deployment.
Register them with the same owner, access policy, lifecycle, cost context, and audit evidence model as the rest of your AI stack.
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