AI workflow automation

Build AI workflows that run like software.

Design visual agent pipelines, connect your own providers and data, then run, debug, schedule, and trigger them from one focused workspace.

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Input Customer request

Agent Analyze and plan

Tool Search files

Output Ready response

Build layer

Compose agents, tools, files, and logic on a visual canvas.

Create reusable building blocks first, then wire them into workflows that are easier to inspect than scripts and more durable than one-off prompts.

Provider-backed agents Use Anthropic, OpenAI, Google, or OpenAI-compatible credentials with per-agent prompts, models, tools, and structured outputs.

Tools and functions Attach file search, JSON schemas, provider-native tools, built-ins, or sandboxed Python functions the agent can call.

Control-flow nodes Branch, loop, delay, catch failures, call HTTP, send messages, query SQL, and reuse configured nodes as templates.

Run layer

Operate every run with traceability, cost visibility, and recovery tools.

Once a pipeline works, run it from chat, schedule it, trigger it from webhooks, or chain it from another pipeline without losing observability.

Triggers that fit real work Start pipelines manually, from schedules, from signed webhooks, or when another pipeline completes.

Debug from the trace Inspect step inputs and outputs, retry attempts, rendered prompts, model choices, and replay a run from a specific node.

Costs and notifications Track token usage and estimated spend per run, compare outputs, and get notified when scheduled jobs fail.