From working on a laptop to running in production
AI Agent Deployment
Scoped permissions, real secrets handling, sandboxing, eval gates, observability, and a rollback path a non engineer can trigger.
The sequence
Why this workflow exists
The gap between a working prototype and a production agent is mostly security and operations work, and it is the part that determines whether the project survives its first incident.
The steps in detail
Scope permissions down
Start from zero access and add only what the agent demonstrably needs. Broad tokens are the most common finding in every audit we run.
Broker secrets properly
Credentials injected at runtime from a vault, never embedded in prompts, configuration files, or logs.
Sandbox execution
Any code the agent runs executes in an isolated container with a network egress allowlist and a resource ceiling.
Gate releases on evals
The eval suite runs in CI. A prompt change is a code change and it does not ship on a failing gate.
Instrument everything
Traces per run, token cost, latency, tool call outcomes, and a taxonomy of failures that gets reviewed weekly.
Make rollback trivial
Versioned prompts and configuration with a one click revert an operations person can trigger without an engineer.
When this is the wrong choice
Every workflow has conditions where running it makes things worse. These are ours.
- The agent has not yet proven value in shadow mode, making this premature hardening
- There is no team to operate it after launch, so the observability goes unread
- A deterministic script would do the same job with none of the risk
Outcome. An agent running in production with bounded permissions, measured quality, visible cost, and a rollback path that does not require an engineer at 3am.
- CategoryOperate
- Scope6 steps, 2 to 6 weeks
- Human in the loopYes, at every gate that matters
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The quote tells you whether this workflow fits, what it costs, and where it would break in your specific environment.