Developers are increasingly using coding agents to write code, fix failures, and iterate on AI applications. These same tools can now help create evaluations, run them, inspect results, and guide the next improvement.
For developers and AI-native product managers, this changes how AI quality can be managed. Instead of treating evaluations as a separate exercise, teams can continuously improve applications within their existing development workflow.
But there's an important distinction: improving evaluation scores and establishing evaluation reliability are two different problems.
A higher score may indicate better application behavior. It could also reflect inconsistent grading, a judge that's misaligned with your team's standards, or application behavior that changes between runs.
1. Bringing Autoeval into the coding-agent workflow
Plumloom Autoeval is an open-source evaluation tool that integrates with coding agents through its CLI, MCP interface, and agent skills.
Developers can use tools such as Claude Code, Codex, Cursor, and Kiro to create evaluations, execute them, inspect failures, and refine their applications.
Consider an AI customer-support agent that provides incorrect refund instructions.
A developer or product manager identifies the expected behavior. Their coding agent helps create an evaluation, examines the failure, modifies the application, and runs the evaluation again.
The process repeats as the application improves. Developers often describe this iterative optimization as hill climbing: making incremental changes based on measured results.
Autoeval makes those results available within the agent's workflow, while providing separate mechanisms to assess their reliability.
2. Three distinct reliability checks
Judge calibration addresses whether the evaluation reflects your team's standards.
Teams define evaluation instructions and provide examples of acceptable and unacceptable behavior. This helps align AI judges with domain-specific expectations, rather than relying entirely on generic assessments.
Multiple judge assessments address grading consistency.
For recorded multi-turn conversations and agent traces, Autoeval uses multiple judge assessments to evaluate the same behavior. Agreement provides evidence of grading consistency, while disagreement can identify ambiguous criteria or difficult cases.
Multiple trials address application variability.
For single-intent evaluations, Autoeval supports repeated executions to measure how application behavior varies. Results include average scores, variation, and 95% confidence intervals.
These mechanisms measure different things. Rejudging a recorded agent trace provides evidence about grading consistency. Running an application multiple times provides evidence about its behavioral variation.
Both matter, but they answer different questions.
3. From evaluation results to release decisions
Autoeval uses this evidence to support configurable release gates.
For example, an evaluation may achieve an average score above the team's release threshold, while its confidence interval still crosses that threshold. In that situation, the evidence is insufficient to establish that the required performance level has been met.
Rather than relying solely on the average score, Autoeval's confidence-based gate holds the release.
For conversations and agent traces, teams can configure gates using individual quality metrics. Evaluation suites can combine multiple evaluations into a single release decision.
Only PASS clears the release gate.
The coding agent can investigate failures, suggest improvements, and rerun evaluations. Autoeval determines the release verdict using the configured criteria.
This separation keeps iterative development flexible while ensuring that release decisions are based on evaluation evidence.
4. Getting started
Autoeval brings evaluation execution, reliability measurement, and release gating into the tools developers already use.
Developers and AI-native PMs can improve applications through repeated evaluation and refinement, while Autoeval provides the evidence needed to assess those improvements before release.
Plumloom Autoeval is open source under Apache-2.0, with support for terminal workflows, coding agents, and CI integration.
Explore Autoeval on GitHub, try it with your coding agent, or contribute to the project.
GitHub: https://github.com/AlchemyInCode/plumloom-autoeval-oss