1.14 – Agentic AI Operational Controls (Availability and Safety in Action) Lesson

Lesson Overview

Agentic AI can trigger workflows, make purchases, change records, and interact with customers. That shift changes the risk profile. Availability issues turn into safety issues when an error means a payment is sent, a policy is changed, or a public message goes out. This lesson shows how to plan and run controls that match the stakes.

You will learn a simple taxonomy that uses two dimensions. Impact means the consequence if the agent is wrong. Autonomy means how much the agent can act without human approval. Plotting use cases on this matrix helps you see where strict controls are required. High impact and high autonomy combinations call for several layers of safeguards.

The lesson fits into the course by connecting risk thinking to day to day operations. It is helpful for anyone preparing to deploy agents in production workloads. You will see practical examples like a procurement agent that drafts purchase orders but cannot authorize payments, and a site reliability agent that tests changes in non production while requiring a human to approve production updates. The goal is to speed up work without giving up human authority where it matters.

Who This Is For

If you are moving from prompts to agents that act inside your tools and processes, this lesson will help you set the right limits and approvals.

• Operations managers responsible for customer refunds, order management, or support workflows
• Product and engineering leads building agentic features into apps
• Procurement and finance teams exploring AI for vendor research and purchases
• Site reliability and platform engineers considering AI for monitoring and fixes
• Risk, compliance, and security professionals defining guardrails and audit needs
• AI program owners who must prove control, accountability, and safety

Where This Fits in a Workflow

Use this lesson when you design or upgrade an agent before production. Start by mapping each use case on the impact and autonomy matrix. That informs which approvals are required and which actions are off limits. Next, set technical guardrails such as scoped permissions, budget caps, allow listed operations, and rate limits. Build mandatory approval gates into the workflow so the agent cannot proceed without a human’s authorization on high stakes actions.

Run sandbox testing to simulate failures like API errors, malformed data, timeouts, or conflicts between agents. Include cases where a human declines an action so you can test the paths and train the approvers. Once in production, rely on detailed observability, a clear halt switch, and tested rollback plans to keep risk controlled.

Technical & Workflow Benefits

The old way is to let an agent try things with broad access and rely on spot checks when something looks off. That leads to silent failures, hard to trace actions, and unclear accountability. The approach in this lesson sets boundaries up front. Agents only get the tools and data they need. High impact steps pause for human approval. Actions are allow listed, budgets and transaction sizes are capped, and rate limits prevent rapid escalation.

This saves time without losing control. A procurement agent can research vendors and draft purchase orders quickly, while approvals keep spending safe. A site reliability agent can suggest and test changes in non production, while an engineer reviews anything that could affect customers. Detailed logs make audits and incident analysis faster. Staged rollouts with version controlled prompts and configurations reduce breakage when you update agents. Overall, you get speed where risk is low and safeguards where risk is high.

Practice Exercise

Try this with one real agent you are planning, such as a refund handler, purchase order drafter, or infrastructure helper.

• Step 1: Place the use case on the impact and autonomy matrix. List the actions the agent will take, the consequences if wrong, and which actions must require human approval.
• Step 2: Define guardrails. Scope the agent’s permissions, set budget and transaction limits, write an allow list of permitted operations, and apply rate limits. Add mandatory approval gates where needed.
• Step 3: Sandbox test. Simulate three failure scenarios such as an API error, malformed input, or a human declining an action. Verify logs capture prompts, tool calls, decision points, and outputs. Confirm the halt switch and rollback steps work.

Reflection: Which test exposed a missing control or unclear approval rule, and do your logs explain why the agent made each attempt?

Course Context Recap

This lesson moves from availability concerns to safety controls for agents that can act inside your business. Earlier lessons introduced human in the loop and why people need both training and authority to stop actions. Here you extended that into concrete operational controls, guardrails, testing, observability, and incident response. Next, continue through the course to deepen your deployment practices, apply the go or no go checklist, and strengthen approvals and change management for new agent capabilities. Watch the video and keep building your control set as you progress.