TurfAI · Orchestration
Agents that survive contact with production.
Most agent demos work once, on a clean input, with a human watching. TurfAI runs multi-step agents the way your business actually runs them — with identity, memory, approval gates, and a full execution trail. Not a chat window. The rails around the model.
Trusted in production by enterprise teams
What we run
Agent patterns we run in production.
Three patterns, not a feature catalogue — the ones we actually put in front of regulated enterprises.
Multi-agent pipelines
Specialised agents hand work to each other — extract, decide, act, verify — instead of one prompt trying to do everything.
Human-in-the-loop agents
Approval gates on the steps that carry risk. The agent proposes; a named human signs off; the decision is logged.
Document & decision agents
Read messy enterprise documents, extract what matters, and route the decision — the work behind Fexo's 24× and Aukera's chart audit.
How it works
How a TurfAI agent is built.
01
Connect
Wire it into your stack.
REST API nodes, MCP servers, and enterprise connectors give the agent the systems it needs — your data, your tools, your stack.
02
Orchestrate
Run it under control.
Multi-step pipelines with memory, retries, approval gates, and human-in-the-loop control. Built for agents, not for a human clicking through a UI.
03
Observe
See everything it did.
Every run is traceable: what the agent saw, what it decided, who approved, what it changed. If you can't measure it, it's still an experiment.
Why agents reach production here
Built for the part after the demo.
Bounded autonomy
You decide where the agent acts on its own and where it must ask. Autonomy is a setting, not a leap of faith.
Cost and token control
Per-step model routing and budget ceilings, so a runaway loop is a config limit, not a surprise invoice.
Memory that persists
State that survives across steps and sessions — agents that remember the case, not just the last message.
Observability built in
Execution traces, audit logs, and metrics from day one — the same rails Intelligent Operations runs on.
In production. Not in a pilot.
Production is the only proof.
FAQ
Questions teams ask about agents.
No — it is the rails around the model: orchestration, identity, memory, execution, messaging, storage, and observability built for agents, not humans.