Why agent reliability doesn't scale with model capability

Background

AI agents now operate inside real workflows. They call tools, modify data, and interact with users and mission-critical production systems.

Model capability is no longer the primary bottleneck for increased AI adoption, even in enterprise settings. Modern LLMs can reason over multi-step tasks, plan actions, and interface with external systems well enough to support significant automation. As a result, teams are deploying agents into increasingly critical paths.

What hasn't improved at the same pace is reliability. Agents still fail in subtle, unpredictable ways, usually not because the model isn't capable enough, but because small misalignments in prompts, context, or tool interactions compound over time. These failures are often silent and only surface when a user notices something went wrong.

This creates a mismatch: trust in agents is rising faster than our ability to verify that they behave correctly in production. Traditional assumptions about testing and QA start to break down once agents operate continuously, adapt to changing context, and take actions with real consequences.

Why QA Breaks Down for Agents

Teams building AI agents quickly run into the limits of traditional testing. Pre-deploy evaluations (e.g., prompt tests or scripted workflows) tend to validate isolated behaviors rather than the end-to-end experience of an agent operating in production.

These tests are reassuring but brittle. Agents that consistently pass local evals still fail in real environments: tool calls break under edge cases, context drifts over long sessions, handoffs between steps degrade, or the agent completes the right steps while producing the wrong outcome. Most of these failures aren't explicit errors, they're silent deviations that only become visible to end users.

When something goes wrong, reproducing the failure is difficult. The exact combination of context and state that caused the issue often no longer exists. Fixes become reactive and narrow, and teams are left guessing whether the underlying problem is resolved or just temporarily fixed.

The result is a fragile QA process that doesn't scale with agent complexity. Each update introduces risk and reliability remains something teams hope for rather than something they can verify. Testing, monitoring, and debugging exist as separate steps rather than as a continuous loop.

Without a closed reliability loop (where production failures directly inform evaluation, and evaluation meaningfully predicts production behavior) — reliability remains reactive. Agents may appear stable, but confidence is shallow, and each iteration risks introducing new failure modes.

What Reliable Agents Actually Require

Improving agent reliability requires treating reliability as a continuous, end-to-end process.

Reliable agents need systems that can surface failures as they occur in production, not days later through user reports. These failures need to be captured in a way that preserves the relevant context so they can be meaningfully reproduced and understood.

Fixes only matter if they hold under real conditions: production failures must feed directly back into evaluation, allowing teams to verify that changes prevent the same class of issues from recurring rather than simply masking symptoms.

Most importantly, reliability needs to be measured for entire workflows, not isolated steps. An agent that follows the right sequence of actions but produces the wrong result is unreliable but the right result also doesn't necessarily mean the agent followed the right steps. Until evaluation reflects how agents are actually used, confidence in their behavior will remain fragile.

Closing this loop is what turns agent reliability from a hope into something teams can systematically enforce.