AI Agentic AI Risk Assessment Playbook

A logistics company is deploying an AI agent that autonomously books freight, renegotiates carrier rates, and executes contracts up to $250,000 without human approval. The agent has been in a pilot with 3 carriers for 6 weeks. Legal and risk want a formal risk assessment before full deployment.

When to use this playbook

• Use this playbook when the decision looks like the situation above: A logistics company is deploying an AI agent that autonomously books freight, renegotiates carrier rates, and executes contracts up to $250,000 without human approval.
• It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "Agentic AI Risk Assessment".
• Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.

What you'll need

• Agent capability description and decision authority matrix
• 6-week pilot log: all decisions made, contracts executed, carrier interactions
• Legal authority analysis: can an AI agent bind the company contractually?
• Risk appetite statement for autonomous decision-making
• Insurance policy language for errors and omissions

Attachments: Documents (Documents)

You are an AI governance specialist conducting a risk assessment for an autonomous AI agent at a logistics company. I am attaching:

Work only from the attached source files. If a conclusion is not supported, say so.

Produce:
1. Identify the scenarios from the pilot log where the agent's autonomous action created or could have created a problematic binding commitment—flag any near-misses.
2. Assess the legal validity of contracts executed by the AI agent under agency law: does the agent have actual authority, apparent authority, or neither?
3. Identify the failure modes: what happens if the agent negotiates a rate that creates a loss, executes a contract with an insolvent carrier, or makes an error at scale?
4. Recommend the human-in-the-loop thresholds: what decision types, dollar thresholds, and risk conditions should require human approval before execution?
5. Design the monitoring and override framework: what the agent must log, how humans can intervene, and what triggers automatic suspension.

Call out where independent models are likely to disagree, and list follow-up documents a reviewer should request.

What to expect

• Pilot log near-miss analysis
• Legal authority assessment for AI-executed contracts
• Failure mode and impact analysis
• Human-in-the-loop threshold recommendations
• Monitoring and override framework design

Review before you act

• Validate this output against source files before relying on it: Identify the scenarios from the pilot log where the agent's autonomous action created or could have created a problematic binding commitment—flag any near-misses.
• Validate this output against source files before relying on it: Assess the legal validity of contracts executed by the AI agent under agency law: does the agent have actual authority, apparent authority, or neither?.
• Validate this output against source files before relying on it: Identify the failure modes: what happens if the agent negotiates a rate that creates a loss, executes a contract with an insolvent carrier, or makes an error at scale?.
• Validate this output against source files before relying on it: Recommend the human-in-the-loop thresholds: what decision types, dollar thresholds, and risk conditions should require human approval before execution?.
• Confirm every cited figure, date, counterparty, or requirement against the attached originals — models compress and can drop a qualifier.
• Treat disagreement between models as a review item, especially on classification, materiality, and recommended next action.
• Do not authorize an operational, clinical, legal, credit, or enforcement action solely because the models agree.