Analysis 11 min read

# Runtime Harnesses for AI Agents: Why Better Models Are Not Enough

A growing body of research makes an uncomfortable point for anyone waiting on the next model: agent reliability is shaped not only by the underlying model but by the runtime harness around it — the interface that mediates tool use, action execution, environment constraints, feedback interpretation, and trajectory control. The thesis is explicit: adapt the interface, not the model. For software-engineering agents, that adapted interface cannot stop at retries and tool wiring. It has to include the architectural decisions the agent must not violate.

By Theo Valmis·June 2026

## What is a runtime harness for an AI agent?

Between a model and a useful agent sits a layer that does not get enough credit: the harness. It is the runtime that mediates everything the model does in the world — how tools are exposed and called, how actions are executed, how environment constraints are applied, how feedback is interpreted, and how the agent’s trajectory is steered when it goes wrong. The model proposes; the harness is what actually turns proposals into bounded, recoverable action.

Research into agent reliability increasingly locates the bottleneck here rather than in the model. A 2026 paper makes the point in its title — Adapting the Interface, Not the Model: Runtime Harness Adaptation for Deterministic LLM Agents — and shows a lifecycle-aware harness improving frozen models across 116 of 126 model-environment settings without changing a single weight. The blunt finding: when you want a more reliable agent, the highest-leverage move is often to adapt the interface around the model, not to wait for a better model.

Adapt the interface, not the model. Reliability is a property of the system around the model at least as much as the model itself.

## Why agent failures occur at the model-environment boundary

Most visible agent failures are not the model being “wrong” in the abstract. They happen at the boundary where the agent meets a real environment: a tool used incorrectly, an action taken with a side effect the agent did not anticipate, feedback misread, a trajectory that wandered because nothing constrained it. These are interface failures. A better model reduces some of them, but it does not change the fact that the boundary is where reliability is won or lost.

This maps cleanly onto a distinction we have drawn before. Harness engineering is the discipline of building this boundary well — the execution layer between a model and production. It is real and necessary work. But a reliable harness is not automatically a governed one.

## Environment contracts versus prompt instructions

The harness lesson generalizes to governance directly. A prompt instruction is a request the model may or may not honor. An environment contract is a property the harness enforces regardless of what the model decides. The whole reason “adapt the interface” works is that the interface can guarantee things the prompt can only ask for.

Architectural governance is exactly an environment contract. “Do not introduce a second HTTP client.” “All persistence goes through the repository layer.” “This module may not import that one.” These are not prompt suggestions to a coding agent; they are constraints the harness should enforce before the agent’s action takes effect.

Aspect | Prompt instruction | Environment contract (harness)
--- | --- | ---
Nature | A request | An enforced property
Honored when | The model chooses to | Always
Survives a model swap | Unpredictably | Yes
Architectural invariant | Hoped for | Checked
Failure is | Silent | Blocked, with a reason

## Why software-engineering agents need governance checkpoints

A coding agent operating without architectural checkpoints can produce changes that pass every test, run cleanly in the environment, and still erode the architecture. The harness handled execution perfectly; what it never had was a contract about which changes are allowed. The fix is to add governance checkpoints to the harness at the points where action becomes consequential: before tool execution, before commit, before pull request, and in CI.

Each checkpoint is the harness doing what harnesses do — mediating between the model’s proposal and the world — but with architectural decisions as part of the mediation. A change that violates an invariant does not get to be an action.

## Where architectural governance fits in the harness stack

Think of the agent stack as layers: the model proposes, the harness executes, and governance constrains. The research says the harness matters more than the model for reliability. The same logic says governance matters more than the model for architectural integrity — because integrity, like reliability, is a property of the interface, not a property you can prompt the model into. The missing layer in harness engineering is verification: a harness that can prove its actions respected the architecture, not just that they ran.

Better models will keep coming, and they will help. But reliability lives in the harness and integrity lives in governance — and neither arrives in the next checkpoint.

## Frequently asked questions

What is a runtime harness for an AI agent?+
It is the runtime layer between a model and useful action: how tools are exposed and called, how actions execute, how environment constraints apply, how feedback is interpreted, and how the agent's trajectory is steered. The model proposes; the harness turns proposals into bounded, recoverable action. Research increasingly locates agent reliability in this layer rather than in the model.
What does 'adapt the interface, not the model' mean?+
It means the highest-leverage way to make an agent more reliable is usually to improve the harness around the model — tools, environment contracts, trajectory control — rather than waiting for a better model. Reliability is a property of the system around the model at least as much as the model itself, because the interface can guarantee what a prompt can only request.
How does this relate to governance?+
Architectural governance is an environment contract, which is exactly the kind of thing a harness enforces. A prompt instruction like 'all persistence goes through the repository layer' is a request the model may ignore; encoded as a harness checkpoint, it becomes a constraint enforced before the agent's action takes effect. Governance is the harness applied to architectural decisions.
Where do governance checkpoints go in the harness?+
At the points where an agent's action becomes consequential: before tool execution, before commit, before pull request, and in CI. Each checkpoint mediates between the model's proposal and the world, with architectural decisions as part of the mediation, so a change that violates an invariant never becomes an action.
Related governance concepts

- Runtime Governance

  Enforcement across long-running, autonomous execution — the harness, governed.

- Verification Contracts

  Pre-registered checks the harness runs before an action takes effect.

- Executable Architectural Intent

  Architecture as an environment contract, not a prompt suggestion.

- Deterministic Enforcement

  The same change yields the same verdict, independent of the model.

Related operational essays

- What Is Harness Engineering?

  The execution layer between a model and production.

- Prompt Engineering vs Harness Engineering

  From optimizing inputs to designing the system around the model.

- The Missing Layer in Harness Engineering Is Verification

  Successful execution is not verified execution.

- Harness Engineering Still Needs Governance

  Execution is solved; architectural constraints are not.