Design an agent's behavior, connect it to real systems, verify who it's talking to, keep a person in the loop when it matters, and prove afterward exactly what it did - one platform, not a pile of glue code.
Most teams get a proof-of-concept working in a week, then spend months finding out it forgets everything, can't be reliably connected to anything real, and has no answer for "what happens if it's wrong." That gap - between an impressive demo and something you'd actually hand real responsibility to - is the whole reason Inteliflow exists.
No memory, no session state - every message starts from zero, so it can't hold a real conversation or hand off cleanly into automation.
Without real retrieval grounding, a confident-sounding answer and a correct one are indistinguishable until it's too late.
Give an LLM function-calling access to real systems and one bad call is one incident away - most frameworks have no approval gate at all.
A chat window is not an integration. Connecting it to your actual APIs, databases, and directories is usually where the "quick prototype" stalls for months.
Every capability below ships in Inteliflow today - not on a roadmap.
A real execution engine behind the canvas - Start/End, Agent, Decision, Tool, Human Approval, Prompt, Loop, Email, Teams, and Identity nodes all actually run, with full execution history per step.
Configure a persona, system prompt, and model per agent - OpenAI, Azure OpenAI, Ollama, or any OpenAI-compatible endpoint - with full control over temperature, max tokens, top-p, and frequency penalty.
REST APIs, webhooks, HTTP functions, SQL databases, and MCP servers - connected via real OpenAI/Azure/Ollama function-calling, with one-click cURL import to stand up a tool in seconds.
Ground replies in your own documents - configurable chunking, OpenAI/Azure/Ollama embeddings, and Qdrant or Pinecone as the vector store, with a strict "answer only from documents" mode.
LDAP, local accounts, and full OAuth2/OIDC - including real "Sign in with Google/Microsoft/GitHub" via Authorization Code + PKCE. Identity checks pause a workflow and verify the end user directly, never through the model.
Vector-based episodic memory per agent, a configurable conversation context window, and shared session variables that flow between chat and any workflow the agent triggers.
PII masking, toxicity filtering, and prompt-injection defense on every reply - plus configurable execution limits so a runaway tool-calling loop can't run forever.
Mark any tool or workflow step as requiring sign-off - the run pauses and a real person approves or rejects it from a single unified Approvals inbox before anything sensitive executes.
Organizations, workspaces, and granular roles - Platform Admin, Organization Admin, Workspace Admin/Editor/Viewer, and fully custom roles - with a complete audit log of who did what.
Execution history, token usage, and a full activity log across agents, workflows, and conversations - filterable and exportable for cost tracking and review.
Every agent is reachable over a real REST API, scoped per API key - build Inteliflow into your own product instead of only using it as a standalone app.
A guided setup wizard, encrypted local secret storage, and a SQL Server backing store - deploy inside your own infrastructure with nothing phoning home.
Four stages, in order - each one is a real gate the run passes through, not a marketing phase.
Build an agent's persona and behavior, or lay out a full multi-step workflow on the visual canvas.
Wire in real tools, knowledge bases, and identity providers - nothing here is a mock or a stub.
Add approval gates, guardrails, and execution limits exactly where the workflow actually needs them.
Talk to it directly or trigger it via API - then audit exactly what happened, step by step.
Try the hosted Inteliflow demo - build an agent, connect a tool, and watch a real workflow execute.
