Draw
Author the agent as typed nodes and edges on a visual canvas, with typed Start and End contracts that define its API.
Platform · Agent Builder
Compose intelligence, brick by brick.
Drag agents, tools and memory into a living graph — the flow keeps running.
drag a node · reliable by construction
A workflow can be deterministic even when its nodes are not.
You don't get reliable agents by making the model predictable. You get them by building a harness whose control flow, contracts and side effects are predictable — and placing the model inside as one bounded, observed node. Reliability is an architectural property, engineered into the graph, not a model property to hope for.
RAILS · Reliable Agent Execution via Layered State-machinesPipeline
From a visual workflow to a running state machine.
The author draws nodes and edges; the platform compiles them ahead of time; the compiled graph executes phase by phase with checkpointed state.
Compile
A topological sort orders nodes into parallel phases and builds a LangGraph state machine — control flow fixed at compile time, not by the model at runtime.
Run
Execution streams every phase transition over SSE while persisting the full run to a checkpointer.
Resume & audit
Checkpointed state makes any run replayable and resumable — reconstruct what ran, in what order, on what input, at what cost.
Node catalog
Place the stochastic core on a gradient.
Node types span a gradient from fully deterministic to fully generative. Good design keeps control flow and contracts on the deterministic end and confines generation to clearly marked nodes.
Deterministic
Same output every run.
- Condition
- Custom Python
- Data Transformation
- Convert to PDF
- Integration Workflow
Bounded
The model picks within a fixed set.
- Orchestrator
- Document / Text Extraction
- ML Prediction
- Guardrails / Data Privacy
- Aggregator (AND / OR)
Generative
Free-form text & reasoning.
- Text Generation
- Conversation / Complex Agent
- Deep Agent / Deep Research
- Aggregator (SYNTHESIZE)
- Image Description
Confine generation, then convert it back to deterministic form — a schema, a branch, a validated field — as early as possible.
Capabilities
Everything an enterprise agent needs, built in.
Visual workflow engine
A drag-and-drop canvas that compiles directly to an executable state machine. No ML expertise, no glue code.
30+ node types
Reasoning agents, orchestrators, document extraction, RAG, A2A, MCP and more — covering most enterprise automation patterns out of the box.
Any LLM, cloud or on-premise
Anthropic, OpenAI, Google, Azure, Mistral and xAI in the cloud; Ollama for self-hosted models that never leave your network. Switch per node — optimise cost and quality, with no vendor lock-in.
Open protocols
A2A, MCP and AP2 for agent-to-agent collaboration, tool discovery and mandate-based, agent-led payments.
Agentic RAG
Query decomposition, relevance grading and adaptive retry keep agents grounded in your proprietary data.
Production-ready
PostgreSQL state, execution resume, SSE streaming, per-node timeouts and retry policies, full execution traces.
Agent types
Not every agent is the same shape.
Deep agents
Break a task into steps, keep a working file system for context, and delegate. Built for multi-step research, code generation and legacy migration — where one prompt and a loop are not enough.
Browser-use agents
Drive a real browser: navigate, click, fill and verify on systems that never exposed an API. Where there is no integration to build against, the screen is the integration.
Multi-agent
Specialist agents that discover one another and collaborate across teams and organisations over A2A, each owning its own task, tools and permissions.
Subagents
A deep agent spawns focused workers with their own clean context, so a long investigation never crowds out the main thread — and each one reports back a result, not a transcript.
Next
Design your first agent.
Bring a regulated process. We'll draw it as an agent, compile it, and show you the audit trail — on your stack.