How to deploy AI agents.
Six steps from "we should automate this" to a production agent your team relies on. This guide is the same playbook AstraGenie ships with every customer deployment.
From scope to running in production.
1. Define the scope
Pick one outcome. "Reply to inbound support tickets in under 15 minutes." Specific, measurable, owned. Vague scope is the most common reason deployments stall.
2. Choose the team
Match the outcome to one of the six AstraGenie teams or compose a custom one. The team's specialists already cover the recurring shape of the work.
3. Configure
Brand voice, restricted topics, escalation thresholds, success criteria. Set once; the team applies them everywhere.
4. Connect integrations
OAuth or service accounts to your CRM, helpdesk, repos, content tools. The agent only sees what you give it access to.
5. Set triggers
Continuous, scheduled, or webhook-driven. The trigger decides when the agent runs and what kicks it off.
6. Monitor and iterate
Per-step traces, output samples, failure summaries. You read the first week's runs together with us and tune from there.
Production-grade from day one.
Hosted infrastructure
No model serving for you to operate. Inference, retries, rate limits, and failover are handled.
Audit logs
Every agent action logged with input, output, tool calls, and timing. Exportable.
Human checkpoints
Configurable approvals for high-impact actions. Adjust as trust grows.
What slows deployments down.
Vague goals, too many integrations on day one, no agreed success metric, and skipping the first-week review. We avoid all four by default. Read about the orchestration layer → or browse use cases →.
Get your first agent live this week.
Book a 30-minute call. We'll walk through the deployment plan for your specific use case.