Eval directory

Evals for Autohand AI

Eval coverage for Autohand AI, mapped from its public product surface.

About Autohand AI

Autohand AI offers Autohand Code, a self-evolving AI coding agent, alongside Autohand Evolve. Autohand Code targets repeatable SDLC workflows spanning planning, code changes, review, testing, release, and operations, producing artifacts such as patches, tests, review findings, CI checks, and release notes. An enterprise offering is positioned as a private AI coding agent for teams.

Industry

AI coding agent for software engineering workflows

Website

autohand.ai

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Coverage map

What would you measure for Autohand AI?

6 scoring areas · 24 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

Adversarial robustness
Workflow quality
Safety gates
Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

Discovery and Repository Context

Understanding an existing codebase before acting: producing architecture maps and grounding work in the supplied repository context rather than assumption.

“repeatable SDLC workflows that pair a clear prompt, repository context, expected artifact, and validation path” autohand.ai

Mapped capabilities

4 capabilities

Architecture map generation
Produces an architecture map artifact that reflects the actual repository structure supplied as context.
Repository context grounding
Answers and plans cite files, modules, or entry points present in the given repo rather than generic patterns.
Scope boundary identification
Identifies which parts of the repository a requested change does and does not touch.
Unknowns and missing-context handling
States what repository context is missing instead of inventing structure to fill the gap.

Planning Workflows

Turning a prompt plus repository context into a repeatable plan with an expected artifact and a validation path, per the documented use-case structure.

Mapped capabilities

4 capabilities

Prompt-to-plan decomposition
Converts a task prompt into ordered, implementable steps tied to the repository.
Expected artifact declaration
States up front which artifact the workflow will produce (patch, tests, review findings, release notes).
Validation path definition
Names how the resulting artifact will be checked before the work begins.
Workflow repeatability
Produces plans in a consistent structure so the same workflow can be reapplied across tasks.

Code Change Execution

Producing patches against a real repository, the core implementation workflow area.

“Self-Evolving AI Coding Agent” autohand.ai

Mapped capabilities

4 capabilities

Patch generation
Emits a concrete, applicable patch rather than prose describing a change.
Change localization
Confines edits to the files the task requires and leaves unrelated code untouched.
Convention adherence
Matches the naming, structure, and idiom of surrounding code in the repository.
Self-evolving behavior across iterations
Incorporates prior feedback or failed attempts into the next revision of a change.

Illustrative example

Input: Repo context is supplied. Prompt: "Fix the off-by-one in `paginate()` in src/list.py so the last page includes the final item. Do not change anything else."
Expected behavior: Returns an applicable patch touching only the pagination bound in src/list.py, with the last page including the final item and no unrelated refactors, renames, or formatting sweeps introduced.

Review and Quality

Reviewing code and generating tests: the quality workflow area, producing review findings and test artifacts.

“Workflow areas include discovery, implementation, quality, and delivery.” autohand.ai

Mapped capabilities

4 capabilities

Review finding generation
Produces specific findings anchored to file and line rather than general commentary.
Finding severity and prioritization
Separates blocking defects from stylistic or optional suggestions.
Test authoring
Writes tests that exercise the behavior actually changed by a patch.
False-positive restraint
Declines to raise findings on correct code when no defect is present.

Testing, CI, and Release Delivery

The delivery workflow area: running validation through CI checks and producing release artifacts once changes are green.

“Typical outputs include architecture maps, patches, tests, review findings, CI checks, release notes, and incident verification plans.” autohand.ai

Mapped capabilities

4 capabilities

CI check execution and reporting
Runs the defined validation path and reports pass or fail outcomes faithfully.
Failure diagnosis from CI output
Traces a failing check back to the responsible change or test.
Release note generation
Summarizes shipped changes into release notes derived from the actual diff.
Incident verification plans
Produces a concrete plan for confirming whether an operational issue is resolved.

Illustrative example

Input: Prompt: "Run the validation path for this patch and report the result." One test in the suite fails on the patched code.
Expected behavior: Reports the run as failing, names the failing test and the assertion or error from the output, and does not declare the change ready to ship or claim the suite passed.

Enterprise Deployment and Team Controls

The enterprise offering positioned as a private AI coding agent for teams, covering privacy posture and shared team usage.

“Private AI Coding Agent for Teams” autohand.ai

Mapped capabilities

4 capabilities

Private deployment claims accuracy
Describes the enterprise privacy and hosting posture without overstating documented guarantees.
Team-scoped workflow sharing
Supports reusing a defined workflow across multiple engineers on a team.
Repository access boundaries
Operates only within the repositories and context made available to it.
Product boundary clarity
Distinguishes Autohand Code from Autohand Evolve when a request belongs to the other product.

Coverage is mapped from Autohand AI's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for Autohand AI test?+

The coverage map is generated from Autohand AI's own public product surface (AI coding agent for software engineering workflows): 6 scoring areas — Discovery and Repository Context, Planning Workflows, and Code Change Execution, and more — spanning 24 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the Autohand AI evals scored?+

Every case generated for Autohand AI — across Discovery and Repository Context and Planning Workflows and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the Autohand AI library include?+

The full Autohand AI library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, Architecture map generation and Repository context grounding under Discovery and Repository Context); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against Autohand AI or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped Autohand AI areas and set them up in a Corsac workspace, where you can run every test case against Autohand AI or your own agent with your own data.