Evaluate risks associated with autonomous AI agents including autonomy level, decision scope, human oversight, action reversibility, environmental impact, and safeguard strength. Get risk scores, deployment recommendations, and mitigation strategies for agentic AI systems.
Rate each dimension from 0-100. For risk factors (autonomy, scope, impact), higher values indicate more risk. For mitigation factors (oversight, reversibility, safeguards), higher values indicate better protection.
Risk Factors (higher = more risk)
Mitigation Factors (higher = better protection)
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The Agentic AI Risk Calculator helps organizations evaluate the safety and risk profile of autonomous AI systems. As AI agents gain capabilities to take independent actions—from coding and research to financial trading and business automation—understanding and managing their risks becomes critical. Get comprehensive risk assessments, deployment recommendations, and mitigation strategies tailored to your agentic AI implementation.
Agentic AI refers to AI systems that can autonomously perceive their environment, make decisions, and take actions to achieve goals with minimal human intervention. Unlike traditional AI that responds to specific queries, agentic systems can plan multi-step tasks, use tools, interact with external systems, and adapt their behavior. This autonomy creates unique risks including unpredictable behavior, unintended consequences, and difficulty maintaining human control. Risk assessment helps organizations deploy agentic AI safely while capturing its transformative benefits.
Risk Calculation Formula
Risk Score = Σ(wᵢ × Rᵢ × (1 - Mᵢ))Autonomous agents can take many actions quickly without human approval. A misconfigured trading bot can execute thousands of trades in seconds. A coding agent can modify critical systems. The speed and scale of autonomous action means errors compound rapidly, making proactive risk assessment essential.
Complex agentic systems can exhibit unexpected behaviors that weren't anticipated during design. Multi-agent systems may develop emergent coordination or conflict patterns. Risk assessment helps identify scenarios where agent behavior may diverge from intended objectives.
Regulators worldwide are scrutinizing autonomous AI systems. The EU AI Act classifies many agentic applications as high-risk. NIST guidelines emphasize human oversight for autonomous systems. Proactive risk assessment demonstrates due diligence and supports regulatory compliance.
Customers, partners, and employees need confidence that AI agents acting on their behalf are safe and controllable. Documented risk assessments with clear mitigation strategies build trust and enable broader adoption of beneficial agentic AI applications.
Autonomous coding agents like Devin or Claude Code can write, test, and deploy code with varying levels of independence. Assess risks of agents that can execute code, modify files, and interact with production systems. Ensure appropriate sandboxing and approval workflows.
AI agents that execute trades, manage portfolios, or make financial decisions autonomously carry significant risk. Evaluate position limits, loss thresholds, market condition safeguards, and human override capabilities. Critical for regulatory compliance and risk management.
Agents that automate multi-step business workflows—from customer service to procurement to HR—need careful risk assessment. Evaluate impact on stakeholders, data access requirements, and exception handling for edge cases.
AI agents that browse the web, access APIs, and synthesize information autonomously. Assess data privacy risks, source validation, and potential for misinformation propagation. Important for agents that inform business decisions.
Systems where multiple AI agents collaborate or compete introduce emergent complexity. Assess coordination risks, resource conflicts, and cascading failure scenarios. Multi-agent architectures require particularly careful risk evaluation.
Agents that interact directly with customers—from support bots to sales assistants—carry reputational and compliance risks. Evaluate escalation paths, response boundaries, and monitoring for inappropriate outputs.
Inherent risk is the risk level before applying any controls or safeguards—based purely on the agent's autonomy, scope, and potential impact. Residual risk is what remains after accounting for human oversight, reversibility mechanisms, and safety guardrails. Effective controls should significantly reduce inherent risk to acceptable residual levels.