AI-Assisted Development Workflow Review

AI-assisted development that doesn't ship hallucinations

Coding agents make a team faster until the first confident, wrong pull request reaches production. The fix is a workflow, not a ban: tests that state intent before the code exists, review that knows where agents go wrong, and specifications the agent can follow. Experts here set that up and review the output.

03 · experts matched 41 cases across their profiles Contracted through Invental Request a review ↗

Lead match

Code review · Arch. review · Arch. design · Due diligence · Vibe-code rescue · Mentoring

AI-assisted development and JavaScript architecture lead

Best for: teams adopting AI coding agents who want quality to hold · JS/TS scale-ups with growing architecture debt · founders with an AI-built app that needs a senior review · corporate teams training engineers in TDD.

engineering leader and principal-level JavaScript / TypeScript architect · North America View profile →

Lead match · 01 engineering leader and principal-level JavaScript / TypeScript architect

From the network

Selected cases from these experts’ profiles.

Test-first workflow for a team adopting AI coding agents

A team started using AI coding agents, shipped faster for a month, and now regressions are rising. The expert sets up a test-first loop (spec, failing unit tests, agent implementation, review gate), shows the team how to write tests that catch hallucinated behaviour, and reviews the first PRs under the new process.

You get

A written workflow, test conventions and coached PRs.

Bookable · Team mentoring · Architecture designTypeScript / JavaScript, unit-testing tooling, LLM coding agents

AI-assisted development and JavaScript architecture lead →

Review of AI-generated JavaScript / TypeScript pull requests

A team's PRs are now mostly agent-written and nobody has time to read them closely. The expert reviews for the four usual agent failures (missing tests, coupled modules, duplicated logic, missed security issues) and leaves fixes and rules the team can feed back to the agent.

You get

Reviewed PRs and a short rules file for agents and people.

Bookable · PR/MR code reviewJavaScript, TypeScript, Node.js, React

AI-assisted development and JavaScript architecture lead →

Spec-driven AI coding workflow

AI coding assistants produce plausible code that drifts from intent. One of our experts built a spec-driven workflow around an AI coding agent: specs first, small scoped changes, tests and review as the gate. Outcome: they ship and run a live product largely through this workflow.

Track record · SaaS / own product · solo to small teamAI coding agent, TypeScript, CI/CD, test suites

AI-native staff engineer →

What engineering leaders do about AI-written code

Everyone is shipping AI-written code; few know how others keep it safe. One of our experts polled dozens of engineering leaders across North America and Europe. Every respondent had AI-written code in production. Security, code quality and losing understanding of the codebase were the top concerns, and roughly four in ten had already had a security incident. Nearly all reviewed AI code at least as strictly as human code, with human review and security scanners as the main safeguards. The expert applies the same rule in their own teams: engineers use AI but stay accountable for what ships.

Track record · cross-industry (many handling sensitive, compliance-bound data) · all sizescode review process, security scanners, CI pipelines

Continuous-delivery fractional CTO →

Questions buyers ask

How do you stop AI coding agents from producing broken code?+

Write the tests first so the agent works against a stated intent, keep changes small, require a human review of every AI-generated pull request, and give the agent written architecture rules. Tests catch wrong behavior; review catches wrong design.

Is code review still needed if the team uses AI agents?+

More than before. Agents produce more code per day, and plausible-looking mistakes are harder to spot. Review is also where developers keep learning instead of only accepting suggestions.

How do you stop AI coding agents from producing hallucinated or broken code?+

Write the tests first and make the agent satisfy them. Unit tests turn "looks right" into a pass or fail check, so invented functions, wrong edge cases and silent regressions show up right away. Add small, well-specified tasks and human review of the architecture, and agent output stays maintainable.

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