Most teams don't fail at AI because the model is weak. They fail at identity, audit, integration, and the jump from demo to production. TurfAI is the orchestration layer built for that jump — agents that act under governance, on your stack, with a trail you can defend.
PII is tokenised before it ever reaches a model, and the reversal key lives in your KMS — not ours, not the LLM provider’s. Every run leaves an immutable, Article-28-ready audit trail.
TurfAI is a Model Context Protocol server today — 12 tools and 3 resource types your existing AI clients can call — not just a consumer of someone else’s tools.
Teams of specialised agents that collaborate over a shared blackboard, not one prompt trying to do everything.
A reason–act–observe loop with goal decomposition and dynamic tool selection — with human-in-the-loop and per-agent guardrails where you set them.
Claude, GPT, Gemini, Llama, Mistral, or Ollama — swap models without rebuilding the workflow.
Agents, workflows, and squads ship as portable JSON — on-prem-ready, exportable, and yours to extend.
Workflow tools move data between apps. They were not built for non-deterministic agents that need bounded autonomy and human-in-the-loop control. MsOpsAi’s founder called TurfAI “10× better than Workato.”
Open builders are powerful for developers. TurfAI adds what an enterprise actually needs on top — Data Shield, full audit, governed prompts, and managed deployment — instead of leaving you to assemble it.
Building your own orchestration layer — identity, audit, observability, deployment — takes 12–18 months and a dedicated platform team. TurfAI gives you production-grade rails from day one.
No — it is the rails around the model: orchestration, identity, Data Shield, memory, execution, messaging, storage, and observability built for agents, not humans.