AI agents can deliver step-function gains in productivity and customer experience, but they also introduce new operational risks: non-deterministic behavior, opaque failure modes, tool-driven “real world” actions, and fast-changing dependencies (models, prompts, data, vendors). In enterprise environments, especially regulated ones, success depends less on how quickly you can build an agent and more on whether you can prove it is safe, reliable, and governed over time.

This handbook is a practical playbook for doing exactly that. It lays out the controls and operating model needed to deploy agents with confidence: defining scope and accountability, tracking dependencies, testing for robustness and safety, monitoring in production and more as systems evolve.

This is an evolving area of best practice and we hope provides a good grounding for the current state of play. 

Brought to you by the team at Safe Intelligence.

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