FLOWX.AI

Connectors do the integration work. A connector is an independently deployable microservice acting as an anti corruption layer over an external system, responsible for data transformation between formats and for enrichment such as flags and tracing identifiers.

Building one means configuring a Kafka consumer and producer, naming the in and out topics against the pattern the engine listens on, setting consumer threads against partition counts, defining the incoming and outgoing DTOs and implementing the business logic with the process instance UUID as the message key, with optional health checks, and FlowX publishes a quickstart repository and a currency exchange example connector. Connectors are invoked from the Process Designer through Send Message Task and Receive Message Task nodes, and the engine captures the reply into process variables.

The platform also connects to named systems, including the Jack Henry, FIS, Finastra and Temenos core banking platforms, COBOL mainframes, transportation management systems, databases and APIs, leaving the legacy estate in place rather than replacing it.

Sourcedocs.flowx.ai 5.9 building a connector and integrations pages, vendor submitted correction verifiedread 2026-09-09

FlowX processes are built from BPMN blocks, including exclusive (XOR) gateways that route each instance down one path on evaluated conditions and parallel gateways for concurrent branches. A Custom Agent node and BPMN integration place AI agents inside processes, and Agent Builder offers typed AI nodes such as intent classification and context retrieval.

Sourcedocs.flowx.ai 5.9 exclusive gateway, parallel gateway, Custom Agent node and Agent Builder pagesread 2026-09-30

The Knowledge Base integration ingests documents into Knowledge Bases that agents query during workflow execution, using vector embeddings with hybrid, semantic or keyword search, multiple stores, relevance scoring, append, replace and delete operations with an audit trail, and entries versioned with the app; companion pages cover managing stores, using Knowledge Bases in workflows and testing queries.

Sourcedocs.flowx.ai 5.9 Knowledge Base integration overviewread 2026-09-30

The hybrid AI and business rules pattern includes a runtime human gate as a named variation, with approval gates placed after AI steps so a reviewer sees the AI output and can correct it before it feeds the next step. The gates are built with User Task nodes in the parent BPMN process and are recommended for initial deployment and for high stakes decisions.

A confidence weighted variation feeds the AI confidence score into the business rules, so low confidence extractions tighten thresholds or flag the result for manual review. The alternating AI and script structure leaves a traceable record of which extraction produced which input value, which formula computed which result and which classification drove which filter.

Above the runtime, the FlowX.AI 5 architecture gates agent self modification of the platform, with agents proposing workflow changes and builder agents implementing them only after a designated operator approves. Observatory adds a policy engine evaluated against runs with Guards checking content before it reaches users, and tracks human in the loop and override mechanisms as EU AI Act controls with evidence attached.

Sourcedocs.flowx.ai 5.9 hybrid AI and business rules pattern and Observatory pages, vendor submitted correction verifiedread 2026-09-09

FlowX.AI operates an information security management system certified against ISO/IEC 27001, and an independent SOC 2 Type I examination confirmed its controls were suitably designed as of 17 June 2026 against the AICPA Trust Services Criteria for Security, Availability and Confidentiality. The SOC 2 Type II examination over an operating period is in progress, personal data is processed under GDPR, and the SOC 2 report and ISO 27001 certificate details are available under NDA through the contact form.

Platform security has five layers. Identity is delegated to an external provider, with step by step Keycloak and Microsoft Entra ID configuration. Role based access control works at organization, workspace and project level with a published roles and permissions matrix, and separate runtime authorization for each project governs who reaches a published solution through project roles, end user groups and solution sharing.

A dedicated Audit service centralizes platform events, and a Personal Information Guard detects and redacts personal data before it reaches a model and restores it afterwards. Swimlanes, business filters that restrict process instances by a business value, and permission based expressions that control UI visibility by role round out the controls, while Observatory's compliance mappings cover the customer's own AI apps.

Sourceflowx.ai trust page, docs.flowx.ai 5.9 Securityread 2026-09-11

Observatory is FlowX's layer for watching AI agents at work. An SDK decorates agents, chains and tools and streams events to the Observatory API, which stores telemetry in PostgreSQL and optionally routes content through Observatory Guards for safety checks; a run is one execution trace and an event is a log entry inside it. Four pillars read from that same store.

Observability gives real time tracing of every agent, chain and tool call with cost and latency analytics, drift detection and threshold alerts. Governance gives a policy engine evaluated against runs, a six dimensional risk score per app, evidence collection and assessments, with apps registered in an AI registry.

Compliance maps EU AI Act, NIST AI RMF and ISO 42001 controls to the runtime, with eighteen EU AI Act requirements scoped by risk tier, each resolving to met, partial, gap or out of scope, and a one click audit pack export returning per requirement evidence with timestamps, telemetry summaries and a gap analysis. ROI covers risk adjusted return, payback, NPV and Monte Carlo sensitivity per agent and project.

Retention is configurable, with a seven year setting named against the record keeping control, and Observatory can be self hosted as well as used as a managed service. These compliance mappings cover the customer's own AI estate.

Sourcedocs.flowx.ai 5.9 Observatory overview and EU AI Act compliance pages, vendor submitted correction verifiedread 2026-09-09

Outputs from subprocesses, workflows, AI agents and business rules land in process variables that downstream nodes read, so an agent's output stays available to later steps. Process variables persist for the life of an instance, variables on active instances can be updated without restarts, and running instances can be migrated between builds.

FlowX Database stores structured data across processes and apps, providing shared state beyond a single instance. Knowledge Bases hold static documents and dynamic data feeds with create, append, replace, delete and edit-metadata operations. No memory feature for individual agents, with its own retention settings or per user scope, is described.

Sourcedocs.flowx.ai v5.9 documentation indexread 2026-09-01

FlowX can run in the customer's own infrastructure as a containerized platform on Kubernetes, with setup guides for the FlowX Engine, Application Manager, Runtime Manager, Kafka authentication, Redis, ingress routing and authorization, and SaaS terms are published beside license terms. No managed cloud region selection is described.

Sourcedocs.flowx.ai 5.9 setup guides and flowx.ai legal footerread 2026-09-30

FlowX lists 246 pre designed agents by industry and business area, 80 for banking, 69 for insurance and 97 for logistics, each with a one line function and a deployment effort estimate of low, medium or high driven mainly by how many systems it integrates with.

FlowX describes catalog agents as blueprints rather than shipped components, each a pre designed combination of platform capabilities built with Agent Builder nodes and integration workflows, and every one is a variation of a small set of recurring agent typologies, each with its own page naming the nodes that implement it.

Agents are grouped by business area into multi agent stacks, nine covering retail mortgage underwriting and seven covering commercial onboarding, coordinated with the multi agent orchestration patterns, with a fraud investigation stack of seven (alert triage, false positive screening, evidence compilation, timeline generation, case narrative, SAR compilation, call transcription) and a garnishment processing stack of six among the named compositions. The same catalog can be browsed on the FlowX site with filters and detail views, where the count is rounded to 220 plus.

Sourcedocs.flowx.ai 5.9 agent catalog, vendor submitted correction verifiedread 2026-09-11

Scheduled processes automate instance creation on dates, times or intervals through Start Timer Event nodes, with timer expressions in cron syntax or ISO 8601 durations, and timer start, intermediate and boundary events are each covered separately. Incoming Webhooks start a process from an external HTTP POST or resume a running instance, secured with API keys and running on events with no polling.

Email Trigger connects to IMAP servers to monitor mailboxes, and Message Start Event starts an instance on a message, incoming email or incoming webhook. Four REST endpoints start processes, including by workspace and process name. Manage Triggers provides a central place to view, activate and deactivate event based triggers. A Chat UI component supports agent conversations with end users, with client SDKs for web, iOS and Android.

Sourcedocs.flowx.ai v5.9 documentation index reached via the published /llms.txtread 2026-09-01

Customers configure model providers themselves under Organization Settings, AI Settings, Model Providers. They add a provider from a catalog, supply their own API key or use the FlowX managed key, test the connection against the provider API, and then whitelist which discovered models are available downstream, grouped by capability with context window and indicative pricing shown for each model.

Defaults and Fallbacks assigns a default and an optional fallback model for each AI capability (text generation, image understanding, embeddings, document and OCR, developer agents, audio), set separately for Sandbox, Staging and Production workspaces. Workflow designers override the model on individual AI nodes using a portable provider and model identifier with an optional fallback provider and model, and the model is chosen from the node override first, then the workspace default, and otherwise the run errors.

Supported providers are OpenAI with FlowX managed or bring your own key and EU, US and Global base URL presets, Azure OpenAI as bring your own key against the customer's own deployment endpoint, Mistral as bring your own key across its chat, vision, embedding and OCR lines, and any OpenAI compatible endpoint through a custom base URL. Two named permissions gate reading and editing the configuration. New providers in the catalog arrive on SaaS first, at release 5.10, and reach self hosted deployments in the next LTS release family.

Sourcedocs.flowx.ai 5.9 AI providers and model configuration, vendor submitted correction verifiedread 2026-09-09

FlowX publishes SDKs for React, React Native, Angular, iOS and Android for embedding FlowX processes in client applications, API reference pages for platform resources such as storage buckets and objects, and an MCP API reference for managing MCP servers and tools, the last marked as internal paths under review.

Sourcedocs.flowx.ai 5.9 SDKs overview and API referenceread 2026-09-30

Evaluations lets customers test their own agents from end to end. A dataset attaches to a specific AI node in a specific workflow and holds test cases of input plus expected output plus optional retrieved context, added by hand or captured from a real node execution in a production run and marked with its source and a back reference.

A catalog of ten built in evaluators covers general quality (correctness, conciseness, hallucination, answer relevance), RAG quality (groundedness, helpfulness, retrieval relevance) and deterministic scorers (exact match, Levenshtein, JSON match against a declared output shape contract), with custom evaluators writable against a supplied scoring prompt and a numeric or binary pass threshold.

An experiment runs the workflow against every test case and scores each test case and evaluator pair through the async evals-judge service, producing aggregate scores per evaluator plus per test case rows showing input, actual output and expected output side by side, the judge's own reasoning, and pass or fail against each threshold.

Every experiment freezes the node configuration at launch, so a baseline run and a run after a change are compared per evaluator and a drop on any evaluator shows a regression between agent versions, and FlowX advises growing the regression set from flagged production executions.

Datasets, test cases and experiments are also exposed as REST endpoints for use from the customer's own tooling, and the judge model is resolved per organization through an EVAL capability an admin can rebind. Custom evaluators, multi dataset experiments and AI improvement suggestions carry later release badges.

Sourcedocs.flowx.ai 5.9 Evaluations, vendor submitted correction verifiedread 2026-09-09

The Browser Automation node is available from FlowX.AI 5.13.0 on SaaS deployments. It drives a real browser to complete tasks on web applications that have no API, including filling multi page forms, navigating portals and retrieving results, as the customer describes the task in natural language and an AI agent plans and executes the browser interactions inside the platform's execution, logging and document storage layers.

Outcomes are checked against the target site's recorded state changes rather than the agent's own success claim, browser sessions persist across workflow steps, and every run is logged through the platform's execution and observability layers. FlowX directs builders to an API integration where one exists and to the read only Web Page Extractor node where only page content is needed.

Underneath, a real Chromium session is driven through the open source browser-use library, with per step logging of action, reasoning and next goal and a screenshot per step, and FlowX has published a 405 run benchmark of nine models over nine browser tasks scored against the fixture site's recorded state. Self hosted deployments receive the node with the next LTS release family. No terminal, desktop or screen scraping automation is claimed.

Sourcedocs.flowx.ai Browser Automation, vendor submitted correction verifiedread 2026-09-23