Deploy an AI agent by packaging a tested version, configuring its runtime and permissions, and introducing real work through a controlled rollout. Deployment includes the tools, data connections, and acceptance checks around the model. Copying a successful prompt into a production application is only one small part of that process.
First, record the version of the application, its instructions, and its tool configuration. Supply credentials through an appropriate protected mechanism and restrict each connection to the actions the task needs. Keep test and production data separate. Verify that a missing credential, unavailable tool, or malformed input produces a visible failure rather than a misleading success message.
Next, run a limited pilot with an accountable reviewer. For a document-review agent, this could mean processing a small set of real documents and comparing its findings with a human review before allowing broader use. Observe completed results, failed runs, unnecessary retries, and the effort required to correct output. Decide in advance which failures should pause the rollout.
Prepare a rollback path before increasing access. Retain the previous application configuration, know how to stop new tasks, and test how credentials can be revoked. Reverting software will not necessarily undo external changes already made by a tool, so write-capable workflows need their own recovery procedure. Expand the deployment when the pilot meets the task's acceptance criteria, not merely because the agent ran without crashing.
Source: Anthropic, Building effective agents. Rollout steps are implementation guidance, not a product deployment guarantee.
