The Internal State: A Framework for Evaluating and Governing Autonomous LLM-Powered Agents
This interactive report explores the shift towards agentic AI, systems that can autonomously reason, plan, and act. It deconstructs their architecture, examines how they are evaluated, outlines their inherent risks, and proposes frameworks for their governance. The goal is to move beyond seeing AI as a simple tool and instead understand it as a proactive, goal-driven collaborator that requires a new paradigm of oversight.
Generative AI (Reactive)
Processes a prompt and provides a corresponding output. Its agency is fundamentally reactive and transaction-centered, ending after the response is delivered. Think of it as a powerful but stateless content creator.
Agentic AI (Proactive)
Designed to achieve high-level objectives. It's a stateful, goal-oriented system that can decompose a broad goal into a sequence of actionable sub-tasks and execute them over time, interacting with tools and memory.
Core Architectural Components
An agent's intelligence comes not from a single model, but from a sophisticated architecture of interconnected components. The LLM acts as the "brain," but requires support systems to overcome its limitations. Click on a component below to learn more about its role in the agentic system.
Select a component to see its description.
Agentic Design Patterns
Agentic behavior is enabled by powerful design patterns that structure their operations. These are often combined to create robust and adaptable systems. Click on a pattern to explore its mechanism, use case, and trade-offs.
A Multi-Layered Evaluation Framework
Traditional LLM benchmarks are insufficient for agents. Evaluation must shift from assessing the final answer to assessing the entire process. This requires a new suite of metrics that can analyze an agent's internal states, such as its plans, reasoning chains, and tool use. Click on a category in the chart below to explore the relevant metrics.
Metric Details
Select a category from the chart to see detailed metrics and their descriptions.
A Taxonomy of Agentic Risks
The autonomy of agentic AI introduces a new spectrum of dynamic and emergent threats. Governance must be continuous and embedded within the system's architecture. Click on a risk category to understand the specific threats, their impact, and potential governance controls.
Select a risk category to see details.
Open Research Questions
While current systems are impressive, several fundamental questions remain unanswered, particularly concerning long-term stability and control.
- Managing Emergent Behaviors: How can we model, predict, and control system-level properties that arise from the collective dynamics of multi-agent systems?
- Long-Term Value Alignment: How do we ensure an agent's goals remain aligned with human values as it continuously learns and potentially modifies itself?
- Verifiable and Controllable AI: Key priorities include improving robustness, developing reliable self-improvement methods, extending formal verification techniques, and advancing internal state interpretability.
Recommendations for Responsible Deployment
Navigating the transition to an agentic future requires proactive and strategic leadership from all stakeholders.
For Technology Leaders (CAIOs, VPs of Engineering)
Adopt a systems-theoretic perspective, establish a centralized "agentic mesh" strategy to avoid fragmentation, and invest heavily in observability and real-time governance platforms.
For Risk and Compliance Officers
Adapt risk frameworks like NIST for autonomy, develop multi-stakeholder governance processes, and prioritize robust contractual frameworks to manage supply chain liability.
For Policymakers and Regulators
Develop concrete, technically-informed standards, foster international harmonization to prevent regulatory fragmentation, and support foundational research in AI safety and control.