Buyer guide

By Specship · Last updated May 26, 2026

AI coding agent evaluation checklist.

The useful question is not whether an agent can write code. It is whether the code can be reviewed, tested, controlled, and trusted inside your team’s real workflow.

Evaluate Specship Review security model
Fast screen
  • Does it start from a clear ticket?
  • Does it write acceptance criteria?
  • Does it create tests before code?
  • Does it open reviewable PRs?
  • Can you cap budget and permissions?

1. Workflow fit

Direct answer: teams should evaluate AI coding agents by workflow fit, test discipline, PR reviewability, security boundaries, budget controls, and failure handling rather than judging only demo speed.

Start with the surface area. Some tools are excellent editor copilots. Others are autonomous agents. Others are CI bots. For team work, look at where the job starts, where the output lands, and whether the workflow matches how your team already ships.

Ticket intake

Can the agent read work from GitHub Issues, Linear, Jira, ClickUp, or your queue?

PR output

Does it create a branch and pull request instead of only returning a chat answer?

Review loop

Can it respond to human comments with code changes on the same branch?

2. Code quality and test discipline

Reliable agent output needs an inspectable definition of done. Prefer systems that require acceptance criteria, show test intent, and make quality evidence easy to review.

  • Does the agent write or update tests before implementation?
  • Does it explain which existing patterns it followed?
  • Does the PR include risk notes, coverage, and commands run?
  • Can your CI gate block merge if checks fail?

3. Security and permissions

An AI coding agent touches repositories, tickets, credentials, and sometimes production workflows. Treat it like a software engineer with scoped access, not like a harmless chatbot.

  • Are OAuth scopes explicit and reviewable?
  • Can you restrict repos, paths, commands, or environments?
  • Are credentials kept out of model prompts and logs?
  • Is there an audit log for ticket pickup, commits, comments, and merges?

4. Operational reliability

Teams need to know what the agent is doing, how much it costs, and where it failed. A useful agent system should make queue state, budget, logs, and recovery visible.

Budget controls

Daily caps, per-ticket spend, and pause controls reduce surprise bills.

Failure handling

Ambiguous tickets should ask questions; failing runs should leave useful logs.

Human override

A team member should be able to pause, reroute, or take over a branch.

Questions to ask vendors

  1. What exactly happens between ticket assignment and pull request creation?
  2. Can we review acceptance criteria before code is written?
  3. Which tests does the agent write, and how does it decide?
  4. How are secrets, OAuth tokens, and repository permissions handled?
  5. What happens when the agent is wrong, blocked, or over budget?
  6. Which claims are available today, and which are still beta or roadmap?
Specship’s answer: our product is intentionally shaped around ticket-to-PR work: acceptance criteria first, tests before implementation, PR review as the durable output, and controls for budget, policy, and human override.