Why Traditional Evaluation Doesn't Work for Agentic AI

Why Legacy Oversight Models Break Under Agentic AI

Human-in-the-loop governance was built for a different generation of AI.

Traditional GenAI systems, and especially copilots, operate in a bounded loop. A human initiates an action, the model responds, and control returns back to the user. In those cases, review points are clear and manageable. Oversight is naturally embedded in the interaction.

Autonomous agents behave fundamentally differently.

Agents are designed to act versus simply assisting. They make decisions continuously, interact with multiple tools and data sources, and pursue objectives across time. Their behavior is a sequence of actions whose risk profile can change dynamically as context evolves.

This creates structural problems for legacy human-in-the-loop models.

Oversight Has No Natural Insertion Point

When an agent takes dozens, or even hundreds, of actions in pursuit of a goal, it’s no longer obvious where a human should intervene. Requiring approval at every step becomes paralysis.

Risk Materializes During Execution, Not Before It

Static approval processes assume risk can be assessed upfront. But with agents, meaningful risk often appears only once systems interact: Costs spike unexpectedly, outputs drift, or actions compound in unintended ways. Ultimately, one-time reviews miss what matters most.

Uniform Controls Create the Wrong Incentives

Applying the same oversight to low-risk and high-risk agent behaviors forces teams into a tradeoff between speed and compliance. In practice, this encourages bypasses, shadow agents, and undocumented workflows. Thereby, leadership has less visibility.

Accountability Becomes Blurred

When decisions are distributed across agents, tools, and models, static governance struggles to answer basic questions: Why did this happen? Who approved it? Which system was responsible? Without traceability, accountability erodes.

This is why human-in-the-loop governance, when applied indiscriminately to agents, fails in practice. It treats autonomy as something to restrain rather than something to design responsibly.

For CIOs and risk leaders, the challenge is recognizing that governance models built for reactive systems cannot govern systems that act autonomously. Governing agents requires an approach that accepts autonomy as a given and focuses human involvement where judgment, accountability, and context truly matter.

Proportional Intervention: Humans Where Judgment Actually Matters

If human-in-the-loop governance doesn’t scale for agents, what replaces it?

Humans must still be involved in oversight, but they should be tapped in far more intentionally. Proportional intervention is a governance design principle built for autonomous systems. Instead of inserting humans everywhere “just in case,” it places human judgment only where it meaningfully reduces risk, relying on automation everywhere else.

This model operates across three layers.

1. Automation by Default

Low-risk, repeatable agent actions should run autonomously. That means governance through automated controls that operate continuously.

These include:

• Real-time monitoring of performance, cost, and behavior
• Automated evaluations against defined quality and policy thresholds
• Alerts when conditions deviate from expectations

In this layer, humans define the operating boundaries. As long as agents remain within them, execution continues at speed.

2. Human Judgment at Decision Boundaries

Human involvement becomes critical at decision boundaries (i.e. moments where tradeoffs, uncertainty, or accountability cannot be resolved by rules alone).

Examples include:

• Approving exceptions to policy or risk thresholds
• Reviewing agent behavior changes that affect business outcomes
• Deciding whether to accept, mitigate, or halt emerging risk

Here, human judgement is contextual. It weighs intent, impact, and consequence in ways automation cannot.

3. Continuous Visibility for Accountability

Proportional intervention only works if humans have continuous visibility, even when they aren’t intervening. For agentic systems, governance is about reconstructability.

Leaders must be able to trace:

• What an agent did
• Why it made a decision
• Which inputs, tools, and models were involved
• How outcomes evolved over time

This level of observability enables faster audits, faster incident response, and stronger executive

What Safe Autonomy Looks Like in Practice

Safe autonomy is an operating model. One that embeds governance directly into how autonomous systems run, rather than layering it on after the fact.

In practice, this model has several defining traits.

Governance Is Embedded in Execution

Controls operate alongside agents as they act, monitoring behavior and enforcing boundaries in real time. Risk is managed continuously, not retroactively.

Oversight Is Unified Across the AI Portfolio

As agents proliferate, governance breaks when fragmented by tool, team, or environment. Leaders need a centralized view of agents, models, and workflows, regardless of where they’re built or deployed.

Controls Adapt to Risk

Low-risk actions move freely. Higher-risk behaviors trigger tighter scrutiny or human review. Governance flexes based on impact, not ideology.

Accountability Is Preserved Through Observability

When regulators, auditors, or executives ask hard questions, organizations can reconstruct decisions with confidence. This is what allows enterprises to deploy autonomous agents at scale without sacrificing safety or control.

Where Dataiku Fits In

Dataiku enables this operating model by embedding governance directly into how AI and agents are designed, deployed, and monitored. Rather than treating governance as a separate layer, Dataiku provides unified visibility across models, agents, and workflows.

This allows enterprises to scale agentic AI with confidence, maintaining accountability without re-introducing friction, fragmentation, or manual bottlenecks. Governance becomes an enabler of safe autonomy versus a constraint on execution.

The Takeaway: Governance for a World Where AI Acts

Autonomous agents are already reshaping how work gets done. The question for enterprise leaders is no longer whether to allow autonomy, but how to govern systems that act continuously and independently.

Human-in-the-loop governance, while well-intentioned, was never designed for this reality. Applied rigidly, it creates friction, blind spots, and false confidence. What works instead is a deliberate shift: human judgment where it matters most, automation everywhere else.

This evolution moves governance from static approvals to continuous oversight, from uniform controls to proportional intervention, and from fragmented visibility to unified accountability. As AI systems gain the ability to act, governance must evolve from control to intent — deciding not just what systems can do, but when humans must decide instead.