AI agents can now assess issues, gather data across systems, and decide the next best action. So how do you set AI agent decision limits without losing the flexibility that makes agents worth deploying?
Watch this on-demand AI agent decision limits webinar featuring a practitioner from Everest Group, a global research and advisory firm, and the Chief Product Officer at Decisions. The Everest Group analyst explains how agentic orchestration is shifting process design toward goal-oriented models, and walks through five scenarios where an agent shouldn’t proceed on its own. The Decisions team then shows how they used the Decisions platform to enforce runtime control, using redaction to keep PII from ever reaching a language model. They also show how pairing an agent step with a Decisions truth table allowed them to constrain agent outputs to an approved set of classifications and route anything unexpected to a human.
The core lesson: the question is shifting from how much you can automate to how much autonomy you can safely delegate, and the answer depends on the authority you give an agent and the impact of what it does.
What you’ll learn:
- Levels of agent autonomy: How governance needs change as agents move from assisting employees to recommending decisions, selecting outcomes, and taking action in enterprise systems.
- Five decisions that need human judgment: Real-world examples from insurance claims, supplier bank account changes, procurement approvals, payment holds, and KYC onboarding.
- Authority vs. access: Why technical access to a system shouldn’t translate into business authority, and why cumulative limits, like monthly supplier spend, need to be checked by a control layer rather than the agent alone.
