Analysis / Development and agents
What is an agent harness, and why does it affect results?
Two applications using the same model can perform very differently. An agent harness organises the work around it: context, tools, memory and controls.
What an AI agent harness does
A harness is the environment that lets a model work: how it receives information, calls tools, retains state and continues after each result.
Consider an agent reviewing a catalogue. The model interprets listings; the application decides which products it can access, validates changes and determines when approval is needed. That organisation affects the outcome.
An example from ARC-AGI-3
ARC Prize reported 62.7% with its Standard integration and 98.6% with Provider Adapter at the same reasoning level, max. The adapter preserves reasoning state between requests and uses compaction.
The highlighted 99.9% result used high reasoning instead. Comparing it with 62.7% and claiming that only the harness changed overlooks that configuration difference.
This illustrates why conditions matter. It does not predict an equivalent improvement in business tasks.
Review the components of your integration
| Component | Example decision |
|---|---|
| Context | Which documents are included or excluded |
| Tools | Whether it can read, prepare changes or execute them |
| State | How completed tasks are tracked |
| Validation | Which conditions code checks before accepting output |
| Recovery | What happens after an error, interruption or incomplete response |
Set attempt limits and a clear exit when the system cannot continue. An apparently correct result can conceal repeated work, lost progress or duplicate actions.
Compare configurations fairly
Keep tasks, data and acceptance criteria constant. When evaluating the harness, retain the same model and settings too. Change one component at a time when you need to understand its effect.
Record success, errors, total time and human interventions. Include cases where tools return conflicting information or documents contain instructions unrelated to the assignment.
Useful records should explain why an output was accepted, rather than merely showing that the agent finished.
What to ask for when commissioning an agent
Request a description of the workflow, permissions, review points and recovery procedure. Model selection is part of that design; switching models may require tool changes and renewed testing.
Start by defining how to assess agent reliability. For connections to business applications, also read our introduction to MCP.
To write that brief with its test conditions, tell us what the agent decides and where it stops.
Sources
Checked on September 20, 2026