A Visual Guide to Evaluating & Governing Autonomous AI Agents
Processes a prompt and provides a corresponding output. Its agency is fundamentally reactive and transaction-centered.
Designed to achieve high-level objectives. It's a stateful, goal-oriented system that can plan and execute tasks over time.
An agent's intelligence comes from a system of interconnected components. The LLM "brain" is supported by memory, planning, and tools to overcome its limitations and interact with the world.
Agentic behaviors are enabled by powerful design patterns. While highly effective, their implementation complexity varies significantly, impacting development and maintenance.
Traditional AI evaluation focuses only on the final output. For agents, this is insufficient. We must evaluate the entire process—the internal reasoning, planning, and tool use—to ensure safety and reliability.
of Agentic AI projects are predicted to be scrapped by 2027 due to operational failures and lack of ROI (Gartner).
The autonomy of agents introduces a new class of stateful and dynamic threats. Governance must be continuous and embedded within the system's architecture to manage these risks effectively.
An attacker subtly injects false data into the agent's memory, causing long-term behavioral drift and flawed decision-making.
An adversary alters the agent's perceived goals or planning logic, hijacking its intent to perform unauthorized actions.
The agent is tricked into using its integrated tools for malicious purposes, such as data exfiltration or unauthorized transactions.
The agent is retrained on its own flawed internal data, leading to a degenerative feedback loop that erodes accuracy and diversity.